Is it me or my smartphone? Investigating predictors of nomophobia and smartphone dependency
Ana C. Ruiz Pardo and J.P. Minda
October 29, 2021
Introduction
This document outlines the analyses from Study 1 and 2 in Is it me or my smartphone? Investigating predictors of nomophobia and smartphone dependency (Ruiz Pardo & Minda, October 2021 preprint, <>). The rising prevalence of smartphones has prompted research about how they can impact cognitive abilities. Therefore, the purpose of this study was to investigate what individual differences can predict people’s smartphone depenency and nomophobia. To do do, we examined a variety of individual difference measures: impulsiveness, depression, stress, anxiety, personality (big five), self-esteem, emotional intelligence, and self regulation. Additionally, participant’s gender, age, and age of first smartphone were recorded.
Two studies were used to compare university students (study 1) to the general population (study 2). Each study asked three key questions… all related to how age may play a role in people’s typical smartphone use and whether or not individual differences were significant predictors of smartphone dependency and nomophobia (i.e., cohort differences).
Criterion measures (2):
Nomophobia (NMPQ; Yildirim and Correia 2015)
Smartphone Attachment and Dependency Inventory (SAD; Ward et al.,
Predictor measures (6):
Barratt Impulsiveness Scale-Brief (BIS-brief; Steinberg et al. 2013)
Depression, Anxiety, and Stress Scale (DASS-21; Lovibond and Lovibond 1995)
International Personality Item Pool NEO (IPIP-NEO-60; Maples-Keller et al. 2019)
Rosenberg Self-Esteem Scale (RSES). Cite: (Rosenberg 1965)
Schutte Self-Report Emotional Intelligence Test (SSEIT; Schutte et al. 1998)
Self-Regulation Scale (SRS; Schwarzer et al. 1999)
Demographic & Smartphone Use measure (1):
- Smartphone use questionnaire (designed for the present study, revised from Ruiz Pardo & Minda, 2021)
Procedure
LOI/consent > Questionnaires in the same random assignment: Demographics, SUQ, NMP-Q, SRS, SAD, DASS-21, BIS-Brief, SSEIT, RSES, IPIP-NEO-60 > debriefing > UWO ethics > approx 40 min in total.
Hypotheses
All sig predictors of nomophobia
All sig predictors of smartphone dependency
Analysis Prep
Load Libraries
Before importing the raw data, the required libraries were loaded.
Additional Functions
Rounding p-values
This rounding function was adapted from Dr. Emily Nielsen’s Rpubs. The function (“p_round(x)”) was created to assess and print p-values. If \(p \ge .005\), the function will display “$p = $” and the value rounded to two decimal places. If $ .0005 p <.005\(, the function will display “\)p = $" and the value rounded to three decimal places. If \(p < .0005\), the function will display “\(p < .001\)”. (click to expand)
p_round <- function(x){
if(x > .005)
{x1 = (paste("= ", gsub("0\\.","\\.", round(x, digits = 2)), sep = ''))
}
else if(x == .005){x1 = (paste("=", gsub("0\\.","\\.", 0.01)))
}
else if(x > .0005 & x < .005)
{x1 = (paste("= ", gsub("0\\.","\\.", round(x, digits = 3)), sep = ''))
}
else if(x == .0005){x1 = (paste("=", gsub("0\\.","\\.", 0.001)))
}
else{x1 = (paste("<", gsub("0\\.","\\.", 0.001)))
}
(x1)
}
Flatten Correlation Output
The following takes a correlation output and flattens it to a long format. Originally from STHDA’s “Correlation matrix : Formatting and visualization” (http://www.sthda.com/english/wiki/correlation-matrix-formatting-and-visualization#.YW4IOfbtntM.link).
# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}Import Raw Data Files
There are two raw data files for the analyses.
Study 1
Study 1 - Survey Data
- csv file exported from Qualtrics
- data collected online
Study 2
Study 2 - Survey Data
- csv file exported from Qualtrics
- data collected in-person
NOTE: To avoid confusion, the naming convention for the two studies was as follows for all data, analyses, visualizations, etc.: Study 1 = “SONA” and Study 2 = “Mturk”
1. Study 1 Data: SONA
First, the raw data was imported.
# this will import the raw excel data file for study 1
# this file has been anonymized, so any identifiable information has been removed
SONA_raw <- read.csv("SONA_survey_data(oct19)-final.csv", header = TRUE)After importing the raw data, the file is cleaned by removing participants based on several criteria:
Testing Data: experimenter data (i.e., testing prior to data collection), any irrelevant rows
Incomplete Data: participants who did not complete the study
- 4 did not finish
No consent: participants who did not consent to the study
- 7 did not consent
Unnecessary Variables: columns which are not relevant to the analyses (e.g., distribution type, distribution language)
# clean the data
SONA_raw_temp <-
# start with removing experimenter and irrelevant rows from the data
# there is no need to count the ps removed at this stage
SONA_raw %>%
# remove row 1 & 2 -- not data
slice(3:nrow(SONA_raw)) %>%
# remove testing data
filter(DistributionChannel != "test") %>%
# next, remove ps w/ incomplete data -- include only thos who have finished (i.e., "1")
filter(Finished == 1) %>%
# remove any ps who did not consent to the study
filter(Q1.2 == 1) %>%
# remove any ps who did not report being at least 18 or older
filter(!Q2.1 <17) %>%
# remove p w/ age "2"
filter(!Q2.1 == 2) %>%
# remove unnecessary columns
# should be done after others since columns used to filter
select(-c(Status:IPAddress, ResponseId:ExternalReference, DistributionChannel:Q1.2, SC0))Additionally, a “participant” column was added to denote the participant ID, the columns were renamed for easier reference, any unclear or inappropriate responses (e.g., non-numeric response for items requiring a numeric response) were removed, and all variables were formatted as numeric or factor as appropriate. Lastly, any questionnaire scores were calculated.
# add a participant column to data
SONA_data_temp <-
cbind(data.frame("participant" = c(1:nrow(SONA_raw_temp))), SONA_raw_temp)
SONA_data <-
# change data file type to tibble
as.tibble(SONA_data_temp) %>%
# rename columns...
dplyr::rename(duration_sec = Duration..in.seconds., age = Q2.1, gender = Q2.2, genderO = Q2.3, lang = Q2.4, langO = Q2.5, prof = Q2.6, live = Q2.7, liveO = Q2.8, edu = Q2.9, employ = Q2.10, employ_FT = Q2.10_1, employ_PT = Q2.10_2, employ_SE = Q2.10_3, employ_UE = Q2.10_4, employ_student = Q2.10_5, employ_retired = Q2.10_6, employ_unable = Q2.10_7, employ_PNTS = Q2.10_8, industry = Q2.11, industryO = Q2.12, industry_student = Q2.11_1, industry_goods = Q2.11_2, industry_utilities = Q2.11_3, industry_const = Q2.11_4, industry_manuf = Q2.11_5, industry_service = Q2.11_6, industry_finance_realtor = Q2.11_7, industry_tech = Q2.11_8, industry_business = Q2.11_9, industry_educ = Q2.11_10, industry_health = Q2.11_11, industry_info_culture = Q2.11_12, industry_accomod = Q2.11_13, industry_public_admin = Q2.11_14, industry_other = Q2.11_15, industry_DNA = Q2.11_16,
# smartphone questions
# frequency of use
phone_for_work = Q2.13, specific_app = Q2.14, age_first_phone = Q3.1, app_most_used = Q3.2, app_most_usedO = Q3.3, app_most_used_overall = Q3.4,
# iphone questions -- ST
iphone = Q4.1, ST = Q4.2, ST_app_most_used = Q4.4, ST_app_most_usedO = Q4.5, ST_app_text_mess = Q4.6, ST_app_most_used_overall = Q4.7, ST_weekly_tot_hours = Q4.8, ST_daily_pickups = Q4.9, ST_daily_not = Q4.10,
# comfort level qs
com_gen = Q5.1_1, com_unattended = Q5.1_2, com_leave_with_others = Q5.1_3, com_locked = Q5.1_4, com_room_task = Q5.1_5,
#privacy qs
passcode = Q6.1, passcode_others = Q6.2, passcode_relation = Q6.3, passcode_relation_parents = Q6.3_1, passcode_relation_other_fam = Q6.3_2, passcode_relation_sig_other = Q6.3_3, passcode_relation_friend = Q6.3_4, passcode_relation_work = Q6.3_5, passcode_relation_other = Q6.3_6, passcode_relation_DNA = Q6.3_7, passcode_relation_overall = Q6.4, other_use = Q6.5, other_room = Q6.6,
# distraction qs
dist_daily = Q7.1_1, dist_study = Q7.1_2, dist_device = Q7.2, dist_deviceO = Q7.3, dist_device_studywork = Q7.4, dist_device_studyworkO = Q7.5,
dist_device_social = Q7.6, dist_device_socialO = Q7.7,
# location & power qs
pow_not_using = Q8.1_1, pow_notifications_on = Q8.1_2, pow_vibrate = Q8.1_3, pow_study = Q8.1_4, pow_exam = Q8.1_5, pow_lec = Q8.1_6, pow_work = Q8.1_7, pow_meeting = Q8.1_8, pow_sleep = Q8.1_9,
loc_typical = Q8.2, loc_study = Q8.3, loc_exam = Q8.4, loc_lec = Q8.5, loc_work = Q8.6, loc_meeting = Q8.7, loc_social = Q8.8,
# exploratory qs
communicate = Q9.1, communicateO = Q9.2, phone_use = Q9.3, phone_useO = Q9.4, phone_value = Q9.5, phantom = Q9.6,
# Questionnaires
# Nomophobia -- criterion - no subscales
NMPQ_1 = Q10.1_1, NMPQ_2 = Q10.1_2, NMPQ_3 = Q10.1_3, NMPQ_4 = Q10.1_4, NMPQ_5 = Q10.1_5, NMPQ_6 = Q10.1_6, NMPQ_7 = Q10.1_7, NMPQ_8 = Q10.1_8, NMPQ_9 = Q10.1_9, NMPQ_10 = Q10.2_1, NMPQ_11 = Q10.2_2, NMPQ_12 = Q10.2_3, NMPQ_13 = Q10.2_4, NMPQ_14 = Q10.2_5, NMPQ_15 = Q10.2_6, NMPQ_16 = Q10.2_7, NMPQ_17 = Q10.2_8, NMPQ_18 = Q10.2_9, NMPQ_19 = Q10.2_10, NMPQ_20 = Q10.2_11,
# SRS -- predictor - no subscales
# rev coded = 5, 7, 9
SRS_1 = Q11.1_1, SRS_2 = Q11.1_2, SRS_3 = Q11.1_3, SRS_4 = Q11.1_4, SRS_5 = Q11.1_5, SRS_6 = Q11.1_6, SRS_7 = Q11.1_7, SRS_8 = Q11.1_8, SRS_9 = Q11.1_9, SRS_10 = Q11.1_10,
# SAD -- predictor - 4 subscales based on Ruiz Pardo & Minda, 2021
# dependence (1-3), accessibility (5-7), emotional attachment (8-11), distractibility (12-13)
SAD_dep_1 = Q12.1_1, SAD_dep_2 = Q12.1_2, SAD_dep_3 = Q12.1_3, SAD_4 = Q12.1_4, SAD_access_5 = Q12.1_5, SAD_access_6 = Q12.1_6, SAD_access_7 = Q12.1_7, SAD_ea_8 = Q12.1_8, SAD_ea_9 = Q12.1_9, SAD_ea_10 = Q12.1_10, SAD_ea_11 = Q12.1_11, SAD_dist_12 = Q12.1_12, SAD_dist_13 = Q12.1_13,
# DASS-21 -- predictor - 3 subscales
# depression (3, 5, 10, 13, 16, 17, 21), anxiety (2, 4, 7, 9, 15, 19, 20), stress (1, 6, 8, 11, 12, 14, 18)
DASS_D_3 = Q13.1_3, DASS_D_5 = Q13.1_5, DASS_D_10 = Q13.1_10, DASS_D_13 = Q13.1_13, DASS_D_16 = Q13.1_16, DASS_D_17 = Q13.1_17, DASS_D_21 = Q13.1_21, DASS_A_2 = Q13.1_2, DASS_A_4 = Q13.1_4, DASS_A_7 = Q13.1_7, DASS_A_9 = Q13.1_9, DASS_A_15 = Q13.1_15, DASS_A_19 = Q13.1_19, DASS_A_20 = Q13.1_20, DASS_S_1 = Q13.1_1, DASS_S_6 = Q13.1_6, DASS_S_8 = Q13.1_8, DASS_S_11 = Q13.1_11, DASS_S_12 = Q13.1_12, DASS_S_14 = Q13.1_14, DASS_S_18 = Q13.1_18,
# BIS-brief -- predictor - no subscales
# rev coded = 1, 4, 5, 6
BIS_1 = Q14.1_1, BIS_2 = Q14.1_2, BIS_3 = Q14.1_3, BIS_4 = Q14.1_4, BIS_5 = Q14.1_5, BIS_6 = Q14.1_6, BIS_7 = Q14.1_7, BIS_8 = Q14.1_8,
# SSEIT - predictor - no subscales
# rev coded = 5, 28, 33
SSEIT_1 = Q15.1_1, SSEIT_2 = Q15.1_2, SSEIT_3 = Q15.1_3, SSEIT_4 = Q15.1_4, SSEIT_5 = Q15.1_5, SSEIT_6 = Q15.1_6, SSEIT_7 = Q15.1_7, SSEIT_8 = Q15.1_8, SSEIT_9 = Q15.1_9, SSEIT_10 = Q15.1_10, SSEIT_11 = Q15.1_11, SSEIT_12 = Q15.1_12, SSEIT_13 = Q15.1_13, SSEIT_14 = Q15.1_14, SSEIT_15 = Q15.1_15, SSEIT_16 = Q15.1_16, SSEIT_17 = Q15.1_17, SSEIT_18 = Q15.1_18, SSEIT_19 = Q15.1_19, SSEIT_20 = Q15.1_20, SSEIT_21 = Q15.1_21, SSEIT_22 = Q15.1_22, SSEIT_23 = Q15.1_23, SSEIT_24 = Q15.1_24, SSEIT_25 = Q15.1_25, SSEIT_26 = Q15.1_26, SSEIT_27 = Q15.1_27, SSEIT_28 = Q15.1_28, SSEIT_29 = Q15.1_29, SSEIT_30 = Q15.1_30, SSEIT_31 = Q15.1_31, SSEIT_32 = Q15.1_32, SSEIT_33 = Q15.1_33,
# RSES - predictor - no subscales
# rev coded = 3, 5, 8, 9, 10
RSES_1 = Q16.1_1, RSES_2 = Q16.1_2, RSES_3 = Q16.1_3, RSES_4 = Q16.1_4, RSES_5 = Q16.1_5, RSES_6 = Q16.1_6, RSES_7 = Q16.1_7, RSES_8 = Q16.1_8, RSES_9 = Q16.1_9, RSES_10 = Q16.1_10,
# IPIP-NEO-60 - predictor - 5 subscales
# Neuroticism (1-12), Extraversion (13-24), Openness (25-36), Agreeableness (37-48), Conscientiousness (49-60)
# rev coded = 9-12; 16; 28, 30-34m 36; 39, 40, 43-46; 52, 58-60
NEO_N_1 = Q17.1_1, NEO_N_2 = Q17.1_2, NEO_N_3 = Q17.1_3, NEO_N_4 = Q17.1_4, NEO_N_5 = Q17.1_5, NEO_N_6 = Q17.1_6, NEO_N_7 = Q17.1_7, NEO_N_8 = Q17.1_8, NEO_N_9 = Q17.1_9, NEO_N_10 = Q17.1_10, NEO_N_11 = Q17.1_11, NEO_N_12 = Q17.1_12, NEO_E_13 = Q17.1_13, NEO_E_14 = Q17.1_14, NEO_E_15 = Q17.1_15, NEO_E_16 = Q17.1_16, NEO_E_17 = Q17.1_17, NEO_E_18 = Q17.1_18, NEO_E_19 = Q17.1_19, NEO_E_20 = Q17.1_20, NEO_E_21 = Q17.1_21, NEO_E_22 = Q17.1_22, NEO_E_23 = Q17.1_23, NEO_E_24 = Q17.1_24, NEO_O_25 = Q17.1_25, NEO_O_26 = Q17.1_26, NEO_O_27 = Q17.1_27, NEO_O_28 = Q17.1_28, NEO_O_29 = Q17.1_29, NEO_O_30 = Q17.1_30, NEO_O_31 = Q17.1_31, NEO_O_32 = Q17.1_32, NEO_O_33 = Q17.1_33, NEO_O_34 = Q17.1_34, NEO_O_35 = Q17.1_35, NEO_O_36 = Q17.1_36, NEO_A_37 = Q17.1_37, NEO_A_38 = Q17.1_38, NEO_A_39 = Q17.1_39, NEO_A_40 = Q17.1_40, NEO_A_41 = Q17.1_41, NEO_A_42 = Q17.1_42, NEO_A_43 = Q17.1_43, NEO_A_44 = Q17.1_44, NEO_A_45 = Q17.1_45, NEO_A_46 = Q17.1_46, NEO_A_47 = Q17.1_47, NEO_A_48 = Q17.1_48, NEO_C_49 = Q17.1_49, NEO_C_50 = Q17.1_50, NEO_C_51 = Q17.1_51, NEO_C_52 = Q17.1_52, NEO_C_53 = Q17.1_53, NEO_C_54 = Q17.1_54, NEO_C_55 = Q17.1_55, NEO_C_56 = Q17.1_56, NEO_C_57 = Q17.1_57, NEO_C_58 = Q17.1_58, NEO_C_59 = Q17.1_59, NEO_C_60 = Q17.1_60
) %>%
# replace unclear/inappropriate responses
# remove non-numeric responses to "age_first_phone"
# this will force all non-numeric value to "NA"
mutate(age_first_phone = as.numeric(age_first_phone)) %>%
# change variables to numeric or factor as needed
# demographic qs
mutate(age = as.numeric(age), age = as.numeric(age), gender = factor(gender, levels = c(1:4), labels = c("Male", "Female", "Other", "Prefer not to say")), lang = factor(lang, levels = c(1:2), labels = c("English", "Other")), prof = factor(prof, levels = c(1:3), labels = c("Low", "Moderate", "High")), live = factor(live, levels = c(1:11), labels = c("North America", "Central America", "South America", "Europe", "Africa", "Asia", "Australia", "Pacific Islander", "Caribbean Islands", "Other", "Prefer not to say")), edu = factor(edu, levels = c(1:9), labels = c("some high school", "high school or equivalent", "some college, no degree", "associate degree", "bachelor’s degree", "master’s degree", "professional degree", "doctorate or higher", "Prefer not to say")),
# employ = factor(employ, levels = c(1:8), labels = c("full-time", "patt-time", "self-employed", "unemployed", "student", "retired", "unable to work", "Prefer not to say")), industry = factor(industry, levels = c(1:16), labels = c("student", "goods-producing sector", "utilities", "construction", "manufacturing", "services-producing sector", "finance, insurance, real estate, rental and leasing", "professional, scientific and technical services", "business, building and other support services", "educational services", "health care and social assistance", "information, culture and recreation", "accommodation and food services", "public administration", "other", "does not apply to me")),
# phone use -- general
phone_for_work = factor(phone_for_work, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), specific_app = factor(specific_app, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), app_most_used = factor(app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")),
# phone use -- iphone / ST
iphone = factor(iphone, levels = c(1:2), labels = c("yes", "no")), ST = factor(ST, levels = c(1:3), labels = c("had ST", "ST new", "no ST")), ST_app_most_used = factor(ST_app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), ST_app_text_mess = as.numeric(ST_app_text_mess), ST_weekly_tot_hours = as.numeric(ST_weekly_tot_hours), ST_daily_pickups = as.numeric(ST_daily_pickups), ST_daily_not = as.numeric(ST_daily_not),
# comfort level qs
com_gen = as.numeric(com_gen), com_unattended = as.numeric(com_unattended), com_leave_with_others = as.numeric(com_leave_with_others), com_locked = as.numeric(com_locked), com_room_task = as.numeric(com_room_task),
# privacy qs
passcode = factor(passcode, levels = c(1:2), labels = c("yes", "no")), passcode_others = factor(passcode_others, levels = c(1:2), labels = c("yes", "no")), passcode_relation = factor(passcode_relation, levels = c(1:7), labels = c("parents", "other family", "significant other", "friends", "co-worker / supervisor", "other", "DNA")), other_use = factor(other_use, levels = c(1:4), labels = c("Yes, only in my presence.", "Yes, depending on the individual and only in my presence.", "Yes, depending on the individual and does not need to be in my presence.", "No")), other_room = factor(other_room, levels = c(1:2), labels = c("yes", "no")),
# dustraction qs
dist_daily = as.numeric(dist_daily), dist_study = as.numeric(dist_study), dist_device = factor(dist_device, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_studywork = factor(dist_device_studywork, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_social = factor(dist_device_social, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")),
# location & power questions
pow_not_using = as.numeric(pow_not_using), pow_notifications_on = as.numeric(pow_notifications_on), pow_vibrate = as.numeric(pow_vibrate), pow_study = as.numeric(pow_study), pow_exam = as.numeric(pow_exam), pow_lec = as.numeric(pow_lec), pow_work = as.numeric(pow_work), pow_meeting = as.numeric(pow_meeting), pow_sleep = as.numeric(pow_sleep), loc_typical = factor(loc_typical, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_study = factor(loc_study, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_exam = factor(loc_exam, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_lec = factor(loc_lec, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_work = factor(loc_work, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_meeting = factor(loc_meeting, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_social = factor(loc_social, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")),
# exploratory qs
communicate = factor(communicate, levels = c(1:4), labels = c("Family", "Friends", "Work", "Other")), phone_use = factor(phone_use, levels = c(1:5), labels = c("Calling/Texting", "Social Media", "Games", "Email", "Other")), phone_value = factor(phone_value, levels = c(1:4), labels = c("$0-$20", "$21-$40", "$41-$60", ">$60")), phantom = factor(phantom, levels = c(1:2), labels = c("yes", "no")),
) %>%
mutate_at(vars(starts_with("employ_")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("industry_")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("NMPQ")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SRS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SAD")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("DASS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("BIS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SSEIT")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("RSES")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("NEO")),funs(as.numeric)) %>%
# reverse code items...
# this REPLACES original items with new reverse-coded values
# the subtraction is made based on the likert scale
# so if the scale is 1-4, then it should be 5-score
# for SRS: 5, 7, 9
mutate(SRS_5 = 5-SRS_5) %>%
mutate(SRS_7 = 5-SRS_7) %>%
mutate(SRS_9 = 5-SRS_9) %>%
# for BIS: 1, 4, 5, 6
mutate(BIS_1 = 5-BIS_1) %>%
mutate(BIS_4 = 5-BIS_4) %>%
mutate(BIS_5 = 5-BIS_5) %>%
mutate(BIS_6 = 5-BIS_6) %>%
# for SSEIT: 5, 28, 33
mutate(SSEIT_5 = 6-SSEIT_5) %>%
mutate(SSEIT_28 = 6-SSEIT_28) %>%
mutate(SSEIT_33 = 6-SSEIT_33) %>%
# for RSES: 3, 5, 8, 9, 10
mutate(RSES_3 = 5-RSES_3) %>%
mutate(RSES_5 = 5-RSES_5) %>%
mutate(RSES_8 = 5-RSES_8) %>%
mutate(RSES_9 = 5-RSES_9) %>%
mutate(RSES_10 = 5-RSES_10) %>%
# for NEO: 9-12; 16; 28, 30-34, 36; 39, 40, 43-46; 52, 58-60
mutate(NEO_N_9 = 6-NEO_N_9) %>%
mutate(NEO_N_10 = 6-NEO_N_10) %>%
mutate(NEO_N_11 = 6-NEO_N_11) %>%
mutate(NEO_N_12 = 6-NEO_N_12) %>%
mutate(NEO_E_16 = 6-NEO_E_16) %>%
mutate(NEO_O_28 = 6-NEO_O_28) %>%
mutate(NEO_O_30 = 6-NEO_O_30) %>%
mutate(NEO_O_31 = 6-NEO_O_31) %>%
mutate(NEO_O_32 = 6-NEO_O_32) %>%
mutate(NEO_O_33 = 6-NEO_O_33) %>%
mutate(NEO_O_34 = 6-NEO_O_34) %>%
mutate(NEO_O_36 = 6-NEO_O_36) %>%
mutate(NEO_A_39 = 6-NEO_A_39) %>%
mutate(NEO_A_40 = 6-NEO_A_40) %>%
mutate(NEO_A_43 = 6-NEO_A_43) %>%
mutate(NEO_A_44 = 6-NEO_A_44) %>%
mutate(NEO_A_45 = 6-NEO_A_45) %>%
mutate(NEO_A_46 = 6-NEO_A_46) %>%
mutate(NEO_C_52 = 6-NEO_C_52) %>%
mutate(NEO_C_58 = 6-NEO_C_58) %>%
mutate(NEO_C_59 = 6-NEO_C_59) %>%
mutate(NEO_C_60 = 6-NEO_C_60) %>%
# add scores for each questionnaire... rowwise() %>% dplyr::mutate(SAD_score = sum(c_across(SAD_1:SAD_13)))
# for each p....
# get NMPQ score -- SUM
rowwise() %>% dplyr::mutate(NMPQ_sum = sum(c_across(NMPQ_1:NMPQ_20))) %>%
# get SRS score -- SUM
rowwise() %>% dplyr::mutate(SRS_sum = sum(c_across(SRS_1:SRS_10))) %>%
# get SAD score -- SUM
# overall (1-13)
rowwise() %>% dplyr::mutate(SAD_sum = sum(c_across(starts_with("SAD")))) %>%
# dependency (1-3)
rowwise() %>% dplyr::mutate(SAD_dep_sum = sum(c_across(SAD_dep_1:SAD_dep_3))) %>%
# accessibility (5-7)
rowwise() %>% dplyr::mutate(SAD_access_sum = sum(c_across(SAD_access_5:SAD_access_7))) %>%
# emotional attachment (8-11)
rowwise() %>% dplyr::mutate(SAD_ea_sum = sum(c_across(SAD_ea_8:SAD_ea_11))) %>%
# distractibility (12-13)
rowwise() %>% dplyr::mutate(SAD_dist_sum = sum(c_across(SAD_dist_12:SAD_dist_13))) %>%
# get DASS score -- SUM
# depression (3, 5, 10, 13, 16, 17, 21)
rowwise() %>% dplyr::mutate(DASS_D_sum = sum(c_across(starts_with("DASS_D")))) %>%
# anxiety (2, 4, 7, 9, 15, 19, 20)
rowwise() %>% dplyr::mutate(DASS_A_sum = sum(c_across(starts_with("DASS_A")))) %>%
# stress (1, 6, 8, 11, 12, 14, 18)
rowwise() %>% dplyr::mutate(DASS_S_sum = sum(c_across(starts_with("DASS_S")))) %>%
# get BIS score -- SUM
rowwise() %>% dplyr::mutate(BIS_sum = sum(c_across(starts_with("BIS")))) %>%
# get SSEIT score -- SUM
rowwise() %>% dplyr::mutate(SSEIT_sum = sum(c_across(starts_with("SSEIT")))) %>%
# get RSES score -- SUM
rowwise() %>% dplyr::mutate(RSES_sum = sum(c_across(starts_with("RSES")))) %>%
# get NEO score -- SUM
# Neuroticism (1-12)
rowwise() %>% dplyr::mutate(NEO_N_sum = sum(c_across(starts_with("NEO_N")))) %>%
# Extraversion (13-24)
rowwise() %>% dplyr::mutate(NEO_E_sum = sum(c_across(starts_with("NEO_E")))) %>%
# Openness (25-36)
rowwise() %>% dplyr::mutate(NEO_O_sum = sum(c_across(starts_with("NEO_O")))) %>%
# Agreeableness (37-48)
rowwise() %>% dplyr::mutate(NEO_A_sum = sum(c_across(starts_with("NEO_A")))) %>%
# Conscientiousness (49-60)
rowwise() %>% dplyr::mutate(NEO_C_sum = sum(c_across(starts_with("NEO_C"))))
# example to get measn: get NMPQ score -- MEAN
# rowwise() %>% dplyr::mutate(mean(c_across(NMPQ_1:NMPQ_20))) %>%
# export final data file
write.csv(SONA_data,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/SONA_data(oct28).csv", row.names = FALSE)2. Study 2 Data: Mturk
First, the raw data was imported.
# this will import the raw excel data file for study 1
# this file has been anonymized, so any identifiable information has been removed
Mturk_raw <- read.csv("Mturk_survey_data(oct19)-final.csv", header = TRUE)After importing the raw data, the file is cleaned by removing participants based on several criteria:
Testing Data: experimenter data (i.e., testing prior to data collection), any irrelevant rows
Incomplete Data: participants who did not complete the study
- 75 did not finish
No consent: participants who did not consent to the study
- 7 did not consent
Unnecessary Variables: columns which are not relevant to the analyses (e.g., distribution type, distribution language)
# clean the data
Mturk_raw_temp <-
# start with removing experimenter and irrelevant rows from the data
# there is no need to count the ps removed at this stage
Mturk_raw %>%
# remove row 1 & 2 -- not data
slice(3:nrow(Mturk_raw)) %>%
# remove testing data
filter(DistributionChannel != "test") %>%
# next, remove ps w/ incomplete data -- include only thos who have finished (i.e., "1")
filter(Finished == 1) %>%
# remove any ps who did not consent to the study
filter(Q1.2 == 1) %>%
# remove any ps who did not report being at least 18 or older
filter(Q2.1 >17) %>%
# remove unnecessary columns
# should be done after others since columns used to filter
select(-c(Status:IPAddress, ResponseId:ExternalReference, DistributionChannel:Q1.2, SC0))Additionally, a “participant” column was added to denote the participant ID, the columns were renamed for easier reference, any unclear or inappropriate responses (e.g., non-numeric response for items requiring a numeric response) were removed, and all variables were formatted as numeric or factor as appropriate. Lastly, any questionnaire scores were calculated.
# add a participant column to data
Mturk_data_temp <-
cbind(data.frame("participant" = c(1:nrow(Mturk_raw_temp))), Mturk_raw_temp)
Mturk_data <-
# change data file type to tibble
as.tibble(Mturk_data_temp) %>%
# rename columns...
dplyr::rename(duration_sec = Duration..in.seconds., age = Q2.1, gender = Q2.2, genderO = Q2.3, lang = Q2.4, langO = Q2.5, prof = Q2.6, live = Q2.7, liveO = Q2.8, edu = Q2.9, employ = Q2.10, employ_FT = Q2.10_1, employ_PT = Q2.10_2, employ_SE = Q2.10_3, employ_UE = Q2.10_4, employ_student = Q2.10_5, employ_retired = Q2.10_6, employ_unable = Q2.10_7, employ_PNTS = Q2.10_8, industry = Q2.11, industryO = Q2.12, industry_student = Q2.11_1, industry_goods = Q2.11_2, industry_utilities = Q2.11_3, industry_const = Q2.11_4, industry_manuf = Q2.11_5, industry_service = Q2.11_6, industry_finance_realtor = Q2.11_7, industry_tech = Q2.11_8, industry_business = Q2.11_9, industry_educ = Q2.11_10, industry_health = Q2.11_11, industry_info_culture = Q2.11_12, industry_accomod = Q2.11_13, industry_public_admin = Q2.11_14, industry_other = Q2.11_15, industry_DNA = Q2.11_16,
# smartphone questions
# frequency of use
phone_for_work = Q2.13, specific_app = Q2.14, age_first_phone = Q3.1, app_most_used = Q3.2, app_most_usedO = Q3.3, app_most_used_overall = Q3.4,
# iphone questions -- ST
iphone = Q4.1, ST = Q4.2, ST_app_most_used = Q4.4, ST_app_most_usedO = Q4.5, ST_app_text_mess = Q4.6, ST_app_most_used_overall = Q4.7, ST_weekly_tot_hours = Q4.8, ST_daily_pickups = Q4.9, ST_daily_not = Q4.10,
# comfort level qs
com_gen = Q5.1_1, com_unattended = Q5.1_2, com_leave_with_others = Q5.1_3, com_locked = Q5.1_4, com_room_task = Q5.1_5,
#privacy qs
passcode = Q6.1, passcode_others = Q6.2, passcode_relation = Q6.3, passcode_relation_parents = Q6.3_1, passcode_relation_other_fam = Q6.3_2, passcode_relation_sig_other = Q6.3_3, passcode_relation_friend = Q6.3_4, passcode_relation_work = Q6.3_5, passcode_relation_other = Q6.3_6, passcode_relation_DNA = Q6.3_7, passcode_relation_overall = Q6.4, other_use = Q6.5, other_room = Q6.6,
# distraction qs
dist_daily = Q7.1_1, dist_study = Q7.1_2, dist_device = Q7.2, dist_deviceO = Q7.3, dist_device_studywork = Q7.4, dist_device_studyworkO = Q7.5,
dist_device_social = Q7.6, dist_device_socialO = Q7.7,
# location & power qs
pow_not_using = Q8.1_1, pow_notifications_on = Q8.1_2, pow_vibrate = Q8.1_3, pow_study = Q8.1_4, pow_exam = Q8.1_5, pow_lec = Q8.1_6, pow_work = Q8.1_7, pow_meeting = Q8.1_8, pow_sleep = Q8.1_9,
loc_typical = Q8.2, loc_study = Q8.3, loc_exam = Q8.4, loc_lec = Q8.5, loc_work = Q8.6, loc_meeting = Q8.7, loc_social = Q8.8,
# exploratory qs
communicate = Q9.1, communicateO = Q9.2, phone_use = Q9.3, phone_useO = Q9.4, phone_value = Q9.5, phantom = Q9.6,
# Questionnaires
# Nomophobia -- criterion - no subscales
NMPQ_1 = Q10.1_1, NMPQ_2 = Q10.1_2, NMPQ_3 = Q10.1_3, NMPQ_4 = Q10.1_4, NMPQ_5 = Q10.1_5, NMPQ_6 = Q10.1_6, NMPQ_7 = Q10.1_7, NMPQ_8 = Q10.1_8, NMPQ_9 = Q10.1_9, NMPQ_10 = Q10.2_1, NMPQ_11 = Q10.2_2, NMPQ_12 = Q10.2_3, NMPQ_13 = Q10.2_4, NMPQ_14 = Q10.2_5, NMPQ_15 = Q10.2_6, NMPQ_16 = Q10.2_7, NMPQ_17 = Q10.2_8, NMPQ_18 = Q10.2_9, NMPQ_19 = Q10.2_10, NMPQ_20 = Q10.2_11,
# SRS -- predictor - no subscales
# rev coded = 5, 7, 9
SRS_1 = Q11.1_1, SRS_2 = Q11.1_2, SRS_3 = Q11.1_3, SRS_4 = Q11.1_4, SRS_5 = Q11.1_5, SRS_6 = Q11.1_6, SRS_7 = Q11.1_7, SRS_8 = Q11.1_8, SRS_9 = Q11.1_9, SRS_10 = Q11.1_10,
# SAD -- predictor - 4 subscales based on Ruiz Pardo & Minda, 2021
# dependence (1-3), accessibility (5-7), emotional attachment (8-11), distractibility (12-13)
SAD_dep_1 = Q12.1_1, SAD_dep_2 = Q12.1_2, SAD_dep_3 = Q12.1_3, SAD_4 = Q12.1_4, SAD_access_5 = Q12.1_5, SAD_access_6 = Q12.1_6, SAD_access_7 = Q12.1_7, SAD_ea_8 = Q12.1_8, SAD_ea_9 = Q12.1_9, SAD_ea_10 = Q12.1_10, SAD_ea_11 = Q12.1_11, SAD_dist_12 = Q12.1_12, SAD_dist_13 = Q12.1_13,
# DASS-21 -- predictor - 3 subscales
# depression (3, 5, 10, 13, 16, 17, 21), anxiety (2, 4, 7, 9, 15, 19, 20), stress (1, 6, 8, 11, 12, 14, 18)
DASS_D_3 = Q13.1_3, DASS_D_5 = Q13.1_5, DASS_D_10 = Q13.1_10, DASS_D_13 = Q13.1_13, DASS_D_16 = Q13.1_16, DASS_D_17 = Q13.1_17, DASS_D_21 = Q13.1_21, DASS_A_2 = Q13.1_2, DASS_A_4 = Q13.1_4, DASS_A_7 = Q13.1_7, DASS_A_9 = Q13.1_9, DASS_A_15 = Q13.1_15, DASS_A_19 = Q13.1_19, DASS_A_20 = Q13.1_20, DASS_S_1 = Q13.1_1, DASS_S_6 = Q13.1_6, DASS_S_8 = Q13.1_8, DASS_S_11 = Q13.1_11, DASS_S_12 = Q13.1_12, DASS_S_14 = Q13.1_14, DASS_S_18 = Q13.1_18,
# BIS-brief -- predictor - no subscales
# rev coded = 1, 4, 5, 6
BIS_1 = Q14.1_1, BIS_2 = Q14.1_2, BIS_3 = Q14.1_3, BIS_4 = Q14.1_4, BIS_5 = Q14.1_5, BIS_6 = Q14.1_6, BIS_7 = Q14.1_7, BIS_8 = Q14.1_8,
# SSEIT - predictor - no subscales
# rev coded = 5, 28, 33
SSEIT_1 = Q15.1_1, SSEIT_2 = Q15.1_2, SSEIT_3 = Q15.1_3, SSEIT_4 = Q15.1_4, SSEIT_5 = Q15.1_5, SSEIT_6 = Q15.1_6, SSEIT_7 = Q15.1_7, SSEIT_8 = Q15.1_8, SSEIT_9 = Q15.1_9, SSEIT_10 = Q15.1_10, SSEIT_11 = Q15.1_11, SSEIT_12 = Q15.1_12, SSEIT_13 = Q15.1_13, SSEIT_14 = Q15.1_14, SSEIT_15 = Q15.1_15, SSEIT_16 = Q15.1_16, SSEIT_17 = Q15.1_17, SSEIT_18 = Q15.1_18, SSEIT_19 = Q15.1_19, SSEIT_20 = Q15.1_20, SSEIT_21 = Q15.1_21, SSEIT_22 = Q15.1_22, SSEIT_23 = Q15.1_23, SSEIT_24 = Q15.1_24, SSEIT_25 = Q15.1_25, SSEIT_26 = Q15.1_26, SSEIT_27 = Q15.1_27, SSEIT_28 = Q15.1_28, SSEIT_29 = Q15.1_29, SSEIT_30 = Q15.1_30, SSEIT_31 = Q15.1_31, SSEIT_32 = Q15.1_32, SSEIT_33 = Q15.1_33,
# RSES - predictor - no subscales
# rev coded = 3, 5, 8, 9, 10
RSES_1 = Q16.1_1, RSES_2 = Q16.1_2, RSES_3 = Q16.1_3, RSES_4 = Q16.1_4, RSES_5 = Q16.1_5, RSES_6 = Q16.1_6, RSES_7 = Q16.1_7, RSES_8 = Q16.1_8, RSES_9 = Q16.1_9, RSES_10 = Q16.1_10,
# IPIP-NEO-60 - predictor - 5 subscales
# Neuroticism (1-12), Extraversion (13-24), Openness (25-36), Agreeableness (37-48), Conscientiousness (49-60)
# rev coded = 9-12; 16; 28, 30-34m 36; 39, 40, 43-46; 52, 58-60
NEO_N_1 = Q17.1_1, NEO_N_2 = Q17.1_2, NEO_N_3 = Q17.1_3, NEO_N_4 = Q17.1_4, NEO_N_5 = Q17.1_5, NEO_N_6 = Q17.1_6, NEO_N_7 = Q17.1_7, NEO_N_8 = Q17.1_8, NEO_N_9 = Q17.1_9, NEO_N_10 = Q17.1_10, NEO_N_11 = Q17.1_11, NEO_N_12 = Q17.1_12, NEO_E_13 = Q17.1_13, NEO_E_14 = Q17.1_14, NEO_E_15 = Q17.1_15, NEO_E_16 = Q17.1_16, NEO_E_17 = Q17.1_17, NEO_E_18 = Q17.1_18, NEO_E_19 = Q17.1_19, NEO_E_20 = Q17.1_20, NEO_E_21 = Q17.1_21, NEO_E_22 = Q17.1_22, NEO_E_23 = Q17.1_23, NEO_E_24 = Q17.1_24, NEO_O_25 = Q17.1_25, NEO_O_26 = Q17.1_26, NEO_O_27 = Q17.1_27, NEO_O_28 = Q17.1_28, NEO_O_29 = Q17.1_29, NEO_O_30 = Q17.1_30, NEO_O_31 = Q17.1_31, NEO_O_32 = Q17.1_32, NEO_O_33 = Q17.1_33, NEO_O_34 = Q17.1_34, NEO_O_35 = Q17.1_35, NEO_O_36 = Q17.1_36, NEO_A_37 = Q17.1_37, NEO_A_38 = Q17.1_38, NEO_A_39 = Q17.1_39, NEO_A_40 = Q17.1_40, NEO_A_41 = Q17.1_41, NEO_A_42 = Q17.1_42, NEO_A_43 = Q17.1_43, NEO_A_44 = Q17.1_44, NEO_A_45 = Q17.1_45, NEO_A_46 = Q17.1_46, NEO_A_47 = Q17.1_47, NEO_A_48 = Q17.1_48, NEO_C_49 = Q17.1_49, NEO_C_50 = Q17.1_50, NEO_C_51 = Q17.1_51, NEO_C_52 = Q17.1_52, NEO_C_53 = Q17.1_53, NEO_C_54 = Q17.1_54, NEO_C_55 = Q17.1_55, NEO_C_56 = Q17.1_56, NEO_C_57 = Q17.1_57, NEO_C_58 = Q17.1_58, NEO_C_59 = Q17.1_59, NEO_C_60 = Q17.1_60
) %>%
# replace unclear/inappropriate responses
# remove non-numeric responses to "age_first_phone"
# this will force all non-numeric value to "NA"
mutate(age_first_phone = as.numeric(age_first_phone)) %>%
# change variables to numeric or factor as needed
# demographic qs
mutate(age = as.numeric(age), age = as.numeric(age), gender = factor(gender, levels = c(1:4), labels = c("Male", "Female", "Other", "Prefer not to say")), lang = factor(lang, levels = c(1:2), labels = c("English", "Other")), prof = factor(prof, levels = c(1:3), labels = c("Low", "Moderate", "High")), live = factor(live, levels = c(1:11), labels = c("North America", "Central America", "South America", "Europe", "Africa", "Asia", "Australia", "Pacific Islander", "Caribbean Islands", "Other", "Prefer not to say")), edu = factor(edu, levels = c(1:9), labels = c("some high school", "high school or equivalent", "some college, no degree", "associate degree", "bachelor’s degree", "master’s degree", "professional degree", "doctorate or higher", "Prefer not to say")),
# employ = factor(employ, levels = c(1:8), labels = c("full-time", "patt-time", "self-employed", "unemployed", "student", "retired", "unable to work", "Prefer not to say")), industry = factor(industry, levels = c(1:16), labels = c("student", "goods-producing sector", "utilities", "construction", "manufacturing", "services-producing sector", "finance, insurance, real estate, rental and leasing", "professional, scientific and technical services", "business, building and other support services", "educational services", "health care and social assistance", "information, culture and recreation", "accommodation and food services", "public administration", "other", "does not apply to me")),
# phone use -- general
phone_for_work = factor(phone_for_work, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), specific_app = factor(specific_app, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), app_most_used = factor(app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")),
# phone use -- iphone / ST
iphone = factor(iphone, levels = c(1:2), labels = c("yes", "no")), ST = factor(ST, levels = c(1:3), labels = c("had ST", "ST new", "no ST")), ST_app_most_used = factor(ST_app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), ST_app_text_mess = as.numeric(ST_app_text_mess), ST_weekly_tot_hours = as.numeric(ST_weekly_tot_hours), ST_daily_pickups = as.numeric(ST_daily_pickups), ST_daily_not = as.numeric(ST_daily_not),
# comfort level qs
com_gen = as.numeric(com_gen), com_unattended = as.numeric(com_unattended), com_leave_with_others = as.numeric(com_leave_with_others), com_locked = as.numeric(com_locked), com_room_task = as.numeric(com_room_task),
# privacy qs
passcode = factor(passcode, levels = c(1:2), labels = c("yes", "no")), passcode_others = factor(passcode_others, levels = c(1:2), labels = c("yes", "no")), passcode_relation = factor(passcode_relation, levels = c(1:7), labels = c("parents", "other family", "significant other", "friends", "co-worker / supervisor", "other", "DNA")), other_use = factor(other_use, levels = c(1:4), labels = c("Yes, only in my presence.", "Yes, depending on the individual and only in my presence.", "Yes, depending on the individual and does not need to be in my presence.", "No")), other_room = factor(other_room, levels = c(1:2), labels = c("yes", "no")),
# dustraction qs
dist_daily = as.numeric(dist_daily), dist_study = as.numeric(dist_study), dist_device = factor(dist_device, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_studywork = factor(dist_device_studywork, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_social = factor(dist_device_social, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")),
# location & power questions
pow_not_using = as.numeric(pow_not_using), pow_notifications_on = as.numeric(pow_notifications_on), pow_vibrate = as.numeric(pow_vibrate), pow_study = as.numeric(pow_study), pow_exam = as.numeric(pow_exam), pow_lec = as.numeric(pow_lec), pow_work = as.numeric(pow_work), pow_meeting = as.numeric(pow_meeting), pow_sleep = as.numeric(pow_sleep), loc_typical = factor(loc_typical, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_study = factor(loc_study, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_exam = factor(loc_exam, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_lec = factor(loc_lec, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_work = factor(loc_work, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_meeting = factor(loc_meeting, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_social = factor(loc_social, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")),
# exploratory qs
communicate = factor(communicate, levels = c(1:4), labels = c("Family", "Friends", "Work", "Other")), phone_use = factor(phone_use, levels = c(1:5), labels = c("Calling/Texting", "Social Media", "Games", "Email", "Other")), phone_value = factor(phone_value, levels = c(1:4), labels = c("$0-$20", "$21-$40", "$41-$60", ">$60")), phantom = factor(phantom, levels = c(1:2), labels = c("yes", "no")),
) %>%
mutate_at(vars(starts_with("employ_")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("industry_")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("NMPQ")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SRS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SAD")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("DASS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("BIS")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("SSEIT")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("RSES")),funs(as.numeric)) %>%
mutate_at(vars(starts_with("NEO")),funs(as.numeric)) %>%
# reverse code items...
# this REPLACES original items with new reverse-coded values
# the subtraction is made based on the likert scale
# so if the scale is 1-4, then it should be 5-score
# for SRS: 5, 7, 9
mutate(SRS_5 = 5-SRS_5) %>%
mutate(SRS_7 = 5-SRS_7) %>%
mutate(SRS_9 = 5-SRS_9) %>%
# for BIS: 1, 4, 5, 6
mutate(BIS_1 = 5-BIS_1) %>%
mutate(BIS_4 = 5-BIS_4) %>%
mutate(BIS_5 = 5-BIS_5) %>%
mutate(BIS_6 = 5-BIS_6) %>%
# for SSEIT: 5, 28, 33
mutate(SSEIT_5 = 6-SSEIT_5) %>%
mutate(SSEIT_28 = 6-SSEIT_28) %>%
mutate(SSEIT_33 = 6-SSEIT_33) %>%
# for RSES: 3, 5, 8, 9, 10
mutate(RSES_3 = 5-RSES_3) %>%
mutate(RSES_5 = 5-RSES_5) %>%
mutate(RSES_8 = 5-RSES_8) %>%
mutate(RSES_9 = 5-RSES_9) %>%
mutate(RSES_10 = 5-RSES_10) %>%
# for NEO: 9-12; 16; 28, 30-34, 36; 39, 40, 43-46; 52, 58-60
mutate(NEO_N_9 = 6-NEO_N_9) %>%
mutate(NEO_N_10 = 6-NEO_N_10) %>%
mutate(NEO_N_11 = 6-NEO_N_11) %>%
mutate(NEO_N_12 = 6-NEO_N_12) %>%
mutate(NEO_E_16 = 6-NEO_E_16) %>%
mutate(NEO_O_28 = 6-NEO_O_28) %>%
mutate(NEO_O_30 = 6-NEO_O_30) %>%
mutate(NEO_O_31 = 6-NEO_O_31) %>%
mutate(NEO_O_32 = 6-NEO_O_32) %>%
mutate(NEO_O_33 = 6-NEO_O_33) %>%
mutate(NEO_O_34 = 6-NEO_O_34) %>%
mutate(NEO_O_36 = 6-NEO_O_36) %>%
mutate(NEO_A_39 = 6-NEO_A_39) %>%
mutate(NEO_A_40 = 6-NEO_A_40) %>%
mutate(NEO_A_43 = 6-NEO_A_43) %>%
mutate(NEO_A_44 = 6-NEO_A_44) %>%
mutate(NEO_A_45 = 6-NEO_A_45) %>%
mutate(NEO_A_46 = 6-NEO_A_46) %>%
mutate(NEO_C_52 = 6-NEO_C_52) %>%
mutate(NEO_C_58 = 6-NEO_C_58) %>%
mutate(NEO_C_59 = 6-NEO_C_59) %>%
mutate(NEO_C_60 = 6-NEO_C_60) %>%
# add scores for each questionnaire... rowwise() %>% dplyr::mutate(SAD_score = sum(c_across(SAD_1:SAD_13)))
# for each p....
# get NMPQ score -- SUM
rowwise() %>% dplyr::mutate(NMPQ_sum = sum(c_across(NMPQ_1:NMPQ_20))) %>%
# get SRS score -- SUM
rowwise() %>% dplyr::mutate(SRS_sum = sum(c_across(SRS_1:SRS_10))) %>%
# get SAD score -- SUM
# overall (1-13)
rowwise() %>% dplyr::mutate(SAD_sum = sum(c_across(starts_with("SAD")))) %>%
# dependency (1-3)
rowwise() %>% dplyr::mutate(SAD_dep_sum = sum(c_across(SAD_dep_1:SAD_dep_3))) %>%
# accessibility (5-7)
rowwise() %>% dplyr::mutate(SAD_access_sum = sum(c_across(SAD_access_5:SAD_access_7))) %>%
# emotional attachment (8-11)
rowwise() %>% dplyr::mutate(SAD_ea_sum = sum(c_across(SAD_ea_8:SAD_ea_11))) %>%
# distractibility (12-13)
rowwise() %>% dplyr::mutate(SAD_dist_sum = sum(c_across(SAD_dist_12:SAD_dist_13))) %>%
# get DASS score -- SUM
# depression (3, 5, 10, 13, 16, 17, 21)
rowwise() %>% dplyr::mutate(DASS_D_sum = sum(c_across(starts_with("DASS_D")))) %>%
# anxiety (2, 4, 7, 9, 15, 19, 20)
rowwise() %>% dplyr::mutate(DASS_A_sum = sum(c_across(starts_with("DASS_A")))) %>%
# stress (1, 6, 8, 11, 12, 14, 18)
rowwise() %>% dplyr::mutate(DASS_S_sum = sum(c_across(starts_with("DASS_S")))) %>%
# get BIS score -- SUM
rowwise() %>% dplyr::mutate(BIS_sum = sum(c_across(starts_with("BIS")))) %>%
# get SSEIT score -- SUM
rowwise() %>% dplyr::mutate(SSEIT_sum = sum(c_across(starts_with("SSEIT")))) %>%
# get RSES score -- SUM
rowwise() %>% dplyr::mutate(RSES_sum = sum(c_across(starts_with("RSES")))) %>%
# get NEO score -- SUM
# Neuroticism (1-12)
rowwise() %>% dplyr::mutate(NEO_N_sum = sum(c_across(starts_with("NEO_N")))) %>%
# Extraversion (13-24)
rowwise() %>% dplyr::mutate(NEO_E_sum = sum(c_across(starts_with("NEO_E")))) %>%
# Openness (25-36)
rowwise() %>% dplyr::mutate(NEO_O_sum = sum(c_across(starts_with("NEO_O")))) %>%
# Agreeableness (37-48)
rowwise() %>% dplyr::mutate(NEO_A_sum = sum(c_across(starts_with("NEO_A")))) %>%
# Conscientiousness (49-60)
rowwise() %>% dplyr::mutate(NEO_C_sum = sum(c_across(starts_with("NEO_C"))))
# example to get measn: get NMPQ score -- MEAN
# rowwise() %>% dplyr::mutate(mean(c_across(NMPQ_1:NMPQ_20))) %>%
# export final data file
write.csv(Mturk_data,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/Mturk_data(oct28).csv", row.names = FALSE)Descriptive Statistics
NOMINAL (study 1 & study 2)
Demographic & Typical Smartphone Use
# output is a list of tibbles, each with: $xxx = var name, x = level(s), n = count, pct = percentage
## FOR SONA DATA
sona_nom_demo <-
SONA_data %>%
# get subset of data with only nominal vars
select(gender, lang, prof, live, edu,
phone_for_work, specific_app, app_most_used, iphone, ST, ST_app_most_used, passcode, passcode_others, starts_with("passcode_relation_"), other_use, other_room, dist_device, dist_device_studywork, dist_device_social, loc_typical:loc_social, communicate, phone_use, phone_value, phantom) %>%
# get freq & proportion for each var
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
# make list into 1 long data frame to show as table...
sona_nom_demo_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(sona_nom_demo, bind_rows, .id = "tib")
# for the employ and industry counts
# employ
sona_nom_demo_employ <-
SONA_data %>%
select(starts_with("employ_")) %>%
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
sona_nom_demo_employ_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(sona_nom_demo_employ, bind_rows, .id = "tib")
#industry
sona_nom_demo_industry <-
SONA_data %>%
select(starts_with("industry_")) %>%
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
sona_nom_demo_industry_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(sona_nom_demo_industry, bind_rows, .id = "tib")
## FOR MTURK DATA
mturk_nom_demo <-
Mturk_data %>%
# get subset of data with only nominal vars
select(gender, lang, prof, live, edu,
phone_for_work, specific_app, app_most_used, iphone, ST, ST_app_most_used, passcode, passcode_others, starts_with("passcode_relation_"), other_use, other_room, dist_device, dist_device_studywork, dist_device_social, loc_typical:loc_social, communicate, phone_use, phone_value, phantom) %>%
# get freq & proportion for each var
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
# make list into 1 long data frame to show as table...
mturk_nom_demo_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(mturk_nom_demo, bind_rows, .id = "tib")
# for the employ and industry counts
# employ
mturk_nom_demo_employ <-
Mturk_data %>%
select(starts_with("employ_")) %>%
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
mturk_nom_demo_employ_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(mturk_nom_demo_employ, bind_rows, .id = "tib")
#industry
mturk_nom_demo_industry <-
Mturk_data %>%
select(starts_with("industry_")) %>%
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
mturk_nom_demo_industry_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(mturk_nom_demo_industry, bind_rows, .id = "tib")# SONA
kable(sona_nom_demo_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | gender | Male | 114 | 47.5000000 |
| 2 | gender | Female | 126 | 52.5000000 |
| 3 | lang | English | 190 | 79.1666667 |
| 4 | lang | Other | 50 | 20.8333333 |
| 5 | prof | Moderate | 21 | 8.7500000 |
| 6 | prof | High | 219 | 91.2500000 |
| 7 | live | North America | 236 | 98.3333333 |
| 8 | live | Asia | 3 | 1.2500000 |
| 9 | live | Other | 1 | 0.4166667 |
| 10 | edu | some high school | 12 | 5.0000000 |
| 11 | edu | high school or equivalent | 127 | 52.9166667 |
| 12 | edu | some college, no degree | 79 | 32.9166667 |
| 13 | edu | associate degree | 1 | 0.4166667 |
| 14 | edu | bachelor’s degree | 14 | 5.8333333 |
| 15 | edu | master’s degree | 1 | 0.4166667 |
| 16 | edu | Prefer not to say | 6 | 2.5000000 |
| 17 | phone_for_work | yes | 97 | 40.4166667 |
| 18 | phone_for_work | no | 68 | 28.3333333 |
| 19 | phone_for_work | does not apply to me | 75 | 31.2500000 |
| 20 | specific_app | yes | 76 | 31.6666667 |
| 21 | specific_app | no | 79 | 32.9166667 |
| 22 | specific_app | does not apply to me | 85 | 35.4166667 |
| 23 | app_most_used | Games | 6 | 2.5000000 |
| 24 | app_most_used | Social Networking | 181 | 75.4166667 |
| 25 | app_most_used | Entertainment | 46 | 19.1666667 |
| 26 | app_most_used | Business/Productivity | 2 | 0.8333333 |
| 27 | app_most_used | Other | 5 | 2.0833333 |
| 28 | iphone | yes | 209 | 87.0833333 |
| 29 | iphone | no | 31 | 12.9166667 |
| 30 | ST | had ST | 175 | 72.9166667 |
| 31 | ST | ST new | 10 | 4.1666667 |
| 32 | ST | no ST | 24 | 10.0000000 |
| 33 | ST | NA | 31 | 12.9166667 |
| 34 | ST_app_most_used | Games | 6 | 2.5000000 |
| 35 | ST_app_most_used | Social Networking | 129 | 53.7500000 |
| 36 | ST_app_most_used | Entertainment | 34 | 14.1666667 |
| 37 | ST_app_most_used | Business/Productivity | 1 | 0.4166667 |
| 38 | ST_app_most_used | Other | 4 | 1.6666667 |
| 39 | ST_app_most_used | NA | 66 | 27.5000000 |
| 40 | passcode | yes | 233 | 97.0833333 |
| 41 | passcode | no | 7 | 2.9166667 |
| 42 | passcode_others | yes | 154 | 64.1666667 |
| 43 | passcode_others | no | 79 | 32.9166667 |
| 44 | passcode_others | NA | 7 | 2.9166667 |
| 45 | passcode_relation_parents | 194 | 80.8333333 | |
| 46 | passcode_relation_parents | 1 | 46 | 19.1666667 |
| 47 | passcode_relation_other_fam | 199 | 82.9166667 | |
| 48 | passcode_relation_other_fam | 1 | 41 | 17.0833333 |
| 49 | passcode_relation_sig_other | 178 | 74.1666667 | |
| 50 | passcode_relation_sig_other | 1 | 62 | 25.8333333 |
| 51 | passcode_relation_friend | 119 | 49.5833333 | |
| 52 | passcode_relation_friend | 1 | 121 | 50.4166667 |
| 53 | passcode_relation_work | 239 | 99.5833333 | |
| 54 | passcode_relation_work | 1 | 1 | 0.4166667 |
| 55 | passcode_relation_other | 239 | 99.5833333 | |
| 56 | passcode_relation_other | 1 | 1 | 0.4166667 |
| 57 | passcode_relation_DNA | 240 | 100.0000000 | |
| 58 | passcode_relation_overall | 239 | 99.5833333 | |
| 59 | passcode_relation_overall | boyfriend | 1 | 0.4166667 |
| 60 | other_use | Yes, only in my presence. | 51 | 21.2500000 |
| 61 | other_use | Yes, depending on the individual and only in my presence. | 113 | 47.0833333 |
| 62 | other_use | Yes, depending on the individual and does not need to be in my presence. | 68 | 28.3333333 |
| 63 | other_use | No | 8 | 3.3333333 |
| 64 | other_room | yes | 173 | 72.0833333 |
| 65 | other_room | no | 67 | 27.9166667 |
| 66 | dist_device | Computer | 27 | 11.2500000 |
| 67 | dist_device | Phone | 205 | 85.4166667 |
| 68 | dist_device | iPad/Tablet | 6 | 2.5000000 |
| 69 | dist_device | Smartwatch | 2 | 0.8333333 |
| 70 | dist_device_studywork | Computer | 37 | 15.4166667 |
| 71 | dist_device_studywork | Phone | 196 | 81.6666667 |
| 72 | dist_device_studywork | iPad/Tablet | 4 | 1.6666667 |
| 73 | dist_device_studywork | Smartwatch | 2 | 0.8333333 |
| 74 | dist_device_studywork | Other | 1 | 0.4166667 |
| 75 | dist_device_social | Computer | 8 | 3.3333333 |
| 76 | dist_device_social | Phone | 227 | 94.5833333 |
| 77 | dist_device_social | iPad/Tablet | 1 | 0.4166667 |
| 78 | dist_device_social | Smartwatch | 3 | 1.2500000 |
| 79 | dist_device_social | Other | 1 | 0.4166667 |
| 80 | loc_typical | On my desk | 90 | 37.5000000 |
| 81 | loc_typical | In my pocket or bag | 149 | 62.0833333 |
| 82 | loc_typical | In another room | 1 | 0.4166667 |
| 83 | loc_study | On my desk | 153 | 63.7500000 |
| 84 | loc_study | In my pocket or bag | 33 | 13.7500000 |
| 85 | loc_study | In another room | 11 | 4.5833333 |
| 86 | loc_study | NA | 43 | 17.9166667 |
| 87 | loc_exam | On my desk | 4 | 1.6666667 |
| 88 | loc_exam | In my pocket or bag | 174 | 72.5000000 |
| 89 | loc_exam | In another room | 18 | 7.5000000 |
| 90 | loc_exam | NA | 44 | 18.3333333 |
| 91 | loc_lec | On my desk | 76 | 31.6666667 |
| 92 | loc_lec | In my pocket or bag | 118 | 49.1666667 |
| 93 | loc_lec | In another room | 4 | 1.6666667 |
| 94 | loc_lec | NA | 42 | 17.5000000 |
| 95 | loc_work | On my desk | 105 | 43.7500000 |
| 96 | loc_work | In my pocket or bag | 105 | 43.7500000 |
| 97 | loc_work | In another room | 18 | 7.5000000 |
| 98 | loc_work | NA | 12 | 5.0000000 |
| 99 | loc_meeting | On my desk | 25 | 10.4166667 |
| 100 | loc_meeting | In my pocket or bag | 179 | 74.5833333 |
| 101 | loc_meeting | In another room | 11 | 4.5833333 |
| 102 | loc_meeting | NA | 25 | 10.4166667 |
| 103 | loc_social | On my desk | 43 | 17.9166667 |
| 104 | loc_social | In my pocket or bag | 192 | 80.0000000 |
| 105 | loc_social | In another room | 4 | 1.6666667 |
| 106 | loc_social | NA | 1 | 0.4166667 |
| 107 | communicate | Family | 17 | 7.0833333 |
| 108 | communicate | Friends | 219 | 91.2500000 |
| 109 | communicate | Work | 2 | 0.8333333 |
| 110 | communicate | Other | 2 | 0.8333333 |
| 111 | phone_use | Calling/Texting | 55 | 22.9166667 |
| 112 | phone_use | Social Media | 170 | 70.8333333 |
| 113 | phone_use | Games | 6 | 2.5000000 |
| 114 | phone_use | 1 | 0.4166667 | |
| 115 | phone_use | Other | 8 | 3.3333333 |
| 116 | phone_value | $0-$20 | 78 | 32.5000000 |
| 117 | phone_value | $21-$40 | 46 | 19.1666667 |
| 118 | phone_value | $41-$60 | 42 | 17.5000000 |
| 119 | phone_value | >$60 | 74 | 30.8333333 |
| 120 | phantom | yes | 210 | 87.5000000 |
| 121 | phantom | no | 30 | 12.5000000 |
kable(sona_nom_demo_employ_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA(Employment Status).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | employ_FT | 1 | 3 | 1.2500000 |
| 2 | employ_FT | NA | 237 | 98.7500000 |
| 3 | employ_PT | 1 | 44 | 18.3333333 |
| 4 | employ_PT | NA | 196 | 81.6666667 |
| 5 | employ_SE | 1 | 9 | 3.7500000 |
| 6 | employ_SE | NA | 231 | 96.2500000 |
| 7 | employ_UE | 1 | 37 | 15.4166667 |
| 8 | employ_UE | NA | 203 | 84.5833333 |
| 9 | employ_student | 1 | 198 | 82.5000000 |
| 10 | employ_student | NA | 42 | 17.5000000 |
| 11 | employ_retired | NA | 240 | 100.0000000 |
| 12 | employ_unable | 1 | 2 | 0.8333333 |
| 13 | employ_unable | NA | 238 | 99.1666667 |
| 14 | employ_PNTS | 1 | 1 | 0.4166667 |
| 15 | employ_PNTS | NA | 239 | 99.5833333 |
kable(sona_nom_demo_industry_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA (Industry).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | industry_student | 1 | 219 | 91.2500000 |
| 2 | industry_student | NA | 21 | 8.7500000 |
| 3 | industry_goods | 1 | 2 | 0.8333333 |
| 4 | industry_goods | NA | 238 | 99.1666667 |
| 5 | industry_utilities | 1 | 1 | 0.4166667 |
| 6 | industry_utilities | NA | 239 | 99.5833333 |
| 7 | industry_const | 1 | 2 | 0.8333333 |
| 8 | industry_const | NA | 238 | 99.1666667 |
| 9 | industry_manuf | NA | 240 | 100.0000000 |
| 10 | industry_service | 1 | 13 | 5.4166667 |
| 11 | industry_service | NA | 227 | 94.5833333 |
| 12 | industry_finance_realtor | 1 | 6 | 2.5000000 |
| 13 | industry_finance_realtor | NA | 234 | 97.5000000 |
| 14 | industry_tech | 1 | 3 | 1.2500000 |
| 15 | industry_tech | NA | 237 | 98.7500000 |
| 16 | industry_business | 1 | 8 | 3.3333333 |
| 17 | industry_business | NA | 232 | 96.6666667 |
| 18 | industry_educ | 1 | 4 | 1.6666667 |
| 19 | industry_educ | NA | 236 | 98.3333333 |
| 20 | industry_health | 1 | 8 | 3.3333333 |
| 21 | industry_health | NA | 232 | 96.6666667 |
| 22 | industry_info_culture | 1 | 4 | 1.6666667 |
| 23 | industry_info_culture | NA | 236 | 98.3333333 |
| 24 | industry_accomod | 1 | 9 | 3.7500000 |
| 25 | industry_accomod | NA | 231 | 96.2500000 |
| 26 | industry_public_admin | NA | 240 | 100.0000000 |
| 27 | industry_other | 1 | 6 | 2.5000000 |
| 28 | industry_other | NA | 234 | 97.5000000 |
| 29 | industry_DNA | 1 | 3 | 1.2500000 |
| 30 | industry_DNA | NA | 237 | 98.7500000 |
kable(plyr::count(as.factor(SONA_data$langO)), caption = "Frequency & percentage for language other var STUDY 1 - SONA.", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| x | freq | |
|---|---|---|
| 1 | 190 | |
| 2 | arabic | 1 |
| 3 | Arabic | 1 |
| 4 | Arabic | 2 |
| 5 | Bengali | 1 |
| 6 | Cantonese | 2 |
| 7 | chinese | 6 |
| 8 | Chinese | 10 |
| 9 | Chinese - Sichuan dialect | 1 |
| 10 | farsi | 1 |
| 11 | Farsi | 2 |
| 12 | filipino | 1 |
| 13 | Gujarati | 2 |
| 14 | Korean | 5 |
| 15 | Kurdish | 1 |
| 16 | Mandarin | 3 |
| 17 | mandarin | 1 |
| 18 | Mandarin | 1 |
| 19 | Mandarin Chinese | 1 |
| 20 | Persian | 1 |
| 21 | Polish | 1 |
| 22 | Romanian | 1 |
| 23 | Spanish | 2 |
| 24 | Turkish | 1 |
| 25 | Urdu | 1 |
| 26 | Vietnamese | 1 |
# MTURK
kable(mturk_nom_demo_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | gender | Male | 311 | 61.5841584 |
| 2 | gender | Female | 190 | 37.6237624 |
| 3 | gender | Other | 2 | 0.3960396 |
| 4 | gender | Prefer not to say | 2 | 0.3960396 |
| 5 | lang | English | 484 | 95.8415842 |
| 6 | lang | Other | 21 | 4.1584158 |
| 7 | prof | Low | 8 | 1.5841584 |
| 8 | prof | Moderate | 87 | 17.2277228 |
| 9 | prof | High | 410 | 81.1881188 |
| 10 | live | North America | 303 | 60.0000000 |
| 11 | live | Central America | 23 | 4.5544554 |
| 12 | live | South America | 34 | 6.7326733 |
| 13 | live | Europe | 16 | 3.1683168 |
| 14 | live | Africa | 1 | 0.1980198 |
| 15 | live | Asia | 115 | 22.7722772 |
| 16 | live | Australia | 3 | 0.5940594 |
| 17 | live | Pacific Islander | 3 | 0.5940594 |
| 18 | live | Other | 4 | 0.7920792 |
| 19 | live | Prefer not to say | 3 | 0.5940594 |
| 20 | edu | some high school | 1 | 0.1980198 |
| 21 | edu | high school or equivalent | 41 | 8.1188119 |
| 22 | edu | some college, no degree | 74 | 14.6534653 |
| 23 | edu | associate degree | 37 | 7.3267327 |
| 24 | edu | bachelor’s degree | 281 | 55.6435644 |
| 25 | edu | master’s degree | 59 | 11.6831683 |
| 26 | edu | professional degree | 8 | 1.5841584 |
| 27 | edu | doctorate or higher | 2 | 0.3960396 |
| 28 | edu | Prefer not to say | 2 | 0.3960396 |
| 29 | phone_for_work | yes | 356 | 70.4950495 |
| 30 | phone_for_work | no | 130 | 25.7425743 |
| 31 | phone_for_work | does not apply to me | 19 | 3.7623762 |
| 32 | specific_app | yes | 291 | 57.6237624 |
| 33 | specific_app | no | 190 | 37.6237624 |
| 34 | specific_app | does not apply to me | 24 | 4.7524752 |
| 35 | app_most_used | Games | 40 | 7.9207921 |
| 36 | app_most_used | Social Networking | 279 | 55.2475248 |
| 37 | app_most_used | Entertainment | 107 | 21.1881188 |
| 38 | app_most_used | Business/Productivity | 51 | 10.0990099 |
| 39 | app_most_used | Other | 28 | 5.5445545 |
| 40 | iphone | yes | 248 | 49.1089109 |
| 41 | iphone | no | 257 | 50.8910891 |
| 42 | ST | had ST | 162 | 32.0792079 |
| 43 | ST | ST new | 35 | 6.9306931 |
| 44 | ST | no ST | 51 | 10.0990099 |
| 45 | ST | NA | 257 | 50.8910891 |
| 46 | ST_app_most_used | Games | 14 | 2.7722772 |
| 47 | ST_app_most_used | Social Networking | 96 | 19.0099010 |
| 48 | ST_app_most_used | Entertainment | 34 | 6.7326733 |
| 49 | ST_app_most_used | Business/Productivity | 11 | 2.1782178 |
| 50 | ST_app_most_used | Other | 6 | 1.1881188 |
| 51 | ST_app_most_used | NA | 344 | 68.1188119 |
| 52 | passcode | yes | 431 | 85.3465347 |
| 53 | passcode | no | 74 | 14.6534653 |
| 54 | passcode_others | yes | 195 | 38.6138614 |
| 55 | passcode_others | no | 236 | 46.7326733 |
| 56 | passcode_others | NA | 74 | 14.6534653 |
| 57 | passcode_relation_parents | 427 | 84.5544554 | |
| 58 | passcode_relation_parents | 1 | 78 | 15.4455446 |
| 59 | passcode_relation_other_fam | 447 | 88.5148515 | |
| 60 | passcode_relation_other_fam | 1 | 58 | 11.4851485 |
| 61 | passcode_relation_sig_other | 425 | 84.1584158 | |
| 62 | passcode_relation_sig_other | 1 | 80 | 15.8415842 |
| 63 | passcode_relation_friend | 425 | 84.1584158 | |
| 64 | passcode_relation_friend | 1 | 80 | 15.8415842 |
| 65 | passcode_relation_work | 484 | 95.8415842 | |
| 66 | passcode_relation_work | 1 | 21 | 4.1584158 |
| 67 | passcode_relation_other | 500 | 99.0099010 | |
| 68 | passcode_relation_other | 1 | 5 | 0.9900990 |
| 69 | passcode_relation_DNA | 503 | 99.6039604 | |
| 70 | passcode_relation_DNA | 1 | 2 | 0.3960396 |
| 71 | passcode_relation_overall | 501 | 99.2079208 | |
| 72 | passcode_relation_overall | 123456 | 2 | 0.3960396 |
| 73 | passcode_relation_overall | Under the Screen Security section tap Screen lock | 1 | 0.1980198 |
| 74 | passcode_relation_overall | 1 | 0.1980198 | |
| 75 | other_use | Yes, only in my presence. | 199 | 39.4059406 |
| 76 | other_use | Yes, depending on the individual and only in my presence. | 175 | 34.6534653 |
| 77 | other_use | Yes, depending on the individual and does not need to be in my presence. | 77 | 15.2475248 |
| 78 | other_use | No | 54 | 10.6930693 |
| 79 | other_room | yes | 290 | 57.4257426 |
| 80 | other_room | no | 215 | 42.5742574 |
| 81 | dist_device | Computer | 125 | 24.7524752 |
| 82 | dist_device | Phone | 315 | 62.3762376 |
| 83 | dist_device | iPad/Tablet | 32 | 6.3366337 |
| 84 | dist_device | Smartwatch | 18 | 3.5643564 |
| 85 | dist_device | Other | 15 | 2.9702970 |
| 86 | dist_device_studywork | Computer | 146 | 28.9108911 |
| 87 | dist_device_studywork | Phone | 301 | 59.6039604 |
| 88 | dist_device_studywork | iPad/Tablet | 28 | 5.5445545 |
| 89 | dist_device_studywork | Smartwatch | 15 | 2.9702970 |
| 90 | dist_device_studywork | Other | 15 | 2.9702970 |
| 91 | dist_device_social | Computer | 74 | 14.6534653 |
| 92 | dist_device_social | Phone | 388 | 76.8316832 |
| 93 | dist_device_social | iPad/Tablet | 19 | 3.7623762 |
| 94 | dist_device_social | Smartwatch | 12 | 2.3762376 |
| 95 | dist_device_social | Other | 12 | 2.3762376 |
| 96 | loc_typical | On my desk | 274 | 54.2574257 |
| 97 | loc_typical | In my pocket or bag | 209 | 41.3861386 |
| 98 | loc_typical | In another room | 17 | 3.3663366 |
| 99 | loc_typical | NA | 5 | 0.9900990 |
| 100 | loc_study | On my desk | 10 | 1.9801980 |
| 101 | loc_study | In my pocket or bag | 6 | 1.1881188 |
| 102 | loc_study | In another room | 3 | 0.5940594 |
| 103 | loc_study | NA | 486 | 96.2376238 |
| 104 | loc_exam | On my desk | 3 | 0.5940594 |
| 105 | loc_exam | In my pocket or bag | 11 | 2.1782178 |
| 106 | loc_exam | In another room | 5 | 0.9900990 |
| 107 | loc_exam | NA | 486 | 96.2376238 |
| 108 | loc_lec | On my desk | 5 | 0.9900990 |
| 109 | loc_lec | In my pocket or bag | 12 | 2.3762376 |
| 110 | loc_lec | In another room | 2 | 0.3960396 |
| 111 | loc_lec | NA | 486 | 96.2376238 |
| 112 | loc_work | On my desk | 286 | 56.6336634 |
| 113 | loc_work | In my pocket or bag | 184 | 36.4356436 |
| 114 | loc_work | In another room | 31 | 6.1386139 |
| 115 | loc_work | NA | 4 | 0.7920792 |
| 116 | loc_meeting | On my desk | 117 | 23.1683168 |
| 117 | loc_meeting | In my pocket or bag | 316 | 62.5742574 |
| 118 | loc_meeting | In another room | 55 | 10.8910891 |
| 119 | loc_meeting | NA | 17 | 3.3663366 |
| 120 | loc_social | On my desk | 105 | 20.7920792 |
| 121 | loc_social | In my pocket or bag | 371 | 73.4653465 |
| 122 | loc_social | In another room | 23 | 4.5544554 |
| 123 | loc_social | NA | 6 | 1.1881188 |
| 124 | communicate | Family | 219 | 43.3663366 |
| 125 | communicate | Friends | 233 | 46.1386139 |
| 126 | communicate | Work | 49 | 9.7029703 |
| 127 | communicate | Other | 4 | 0.7920792 |
| 128 | phone_use | Calling/Texting | 217 | 42.9702970 |
| 129 | phone_use | Social Media | 223 | 44.1584158 |
| 130 | phone_use | Games | 27 | 5.3465347 |
| 131 | phone_use | 25 | 4.9504950 | |
| 132 | phone_use | Other | 13 | 2.5742574 |
| 133 | phone_value | $0-$20 | 130 | 25.7425743 |
| 134 | phone_value | $21-$40 | 175 | 34.6534653 |
| 135 | phone_value | $41-$60 | 77 | 15.2475248 |
| 136 | phone_value | >$60 | 123 | 24.3564356 |
| 137 | phantom | yes | 363 | 71.8811881 |
| 138 | phantom | no | 142 | 28.1188119 |
kable(mturk_nom_demo_employ_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk(Employment Status).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | employ_FT | 1 | 385 | 76.237624 |
| 2 | employ_FT | NA | 120 | 23.762376 |
| 3 | employ_PT | 1 | 34 | 6.732673 |
| 4 | employ_PT | NA | 471 | 93.267327 |
| 5 | employ_SE | 1 | 85 | 16.831683 |
| 6 | employ_SE | NA | 420 | 83.168317 |
| 7 | employ_UE | 1 | 23 | 4.554455 |
| 8 | employ_UE | NA | 482 | 95.445545 |
| 9 | employ_student | 1 | 19 | 3.762376 |
| 10 | employ_student | NA | 486 | 96.237624 |
| 11 | employ_retired | 1 | 9 | 1.782178 |
| 12 | employ_retired | NA | 496 | 98.217822 |
| 13 | employ_unable | 1 | 9 | 1.782178 |
| 14 | employ_unable | NA | 496 | 98.217822 |
| 15 | employ_PNTS | 1 | 5 | 0.990099 |
| 16 | employ_PNTS | NA | 500 | 99.009901 |
kable(mturk_nom_demo_industry_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk (Industry).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | industry_student | 1 | 28 | 5.544555 |
| 2 | industry_student | NA | 477 | 94.455445 |
| 3 | industry_goods | 1 | 21 | 4.158416 |
| 4 | industry_goods | NA | 484 | 95.841584 |
| 5 | industry_utilities | 1 | 14 | 2.772277 |
| 6 | industry_utilities | NA | 491 | 97.227723 |
| 7 | industry_const | 1 | 32 | 6.336634 |
| 8 | industry_const | NA | 473 | 93.663366 |
| 9 | industry_manuf | 1 | 83 | 16.435644 |
| 10 | industry_manuf | NA | 422 | 83.564356 |
| 11 | industry_service | 1 | 58 | 11.485148 |
| 12 | industry_service | NA | 447 | 88.514852 |
| 13 | industry_finance_realtor | 1 | 66 | 13.069307 |
| 14 | industry_finance_realtor | NA | 439 | 86.930693 |
| 15 | industry_tech | 1 | 100 | 19.801980 |
| 16 | industry_tech | NA | 405 | 80.198020 |
| 17 | industry_business | 1 | 22 | 4.356436 |
| 18 | industry_business | NA | 483 | 95.643564 |
| 19 | industry_educ | 1 | 41 | 8.118812 |
| 20 | industry_educ | NA | 464 | 91.881188 |
| 21 | industry_health | 1 | 36 | 7.128713 |
| 22 | industry_health | NA | 469 | 92.871287 |
| 23 | industry_info_culture | 1 | 59 | 11.683168 |
| 24 | industry_info_culture | NA | 446 | 88.316832 |
| 25 | industry_accomod | 1 | 18 | 3.564356 |
| 26 | industry_accomod | NA | 487 | 96.435644 |
| 27 | industry_public_admin | 1 | 18 | 3.564356 |
| 28 | industry_public_admin | NA | 487 | 96.435644 |
| 29 | industry_other | 1 | 25 | 4.950495 |
| 30 | industry_other | NA | 480 | 95.049505 |
| 31 | industry_DNA | 1 | 15 | 2.970297 |
| 32 | industry_DNA | NA | 490 | 97.029703 |
kable(plyr::count(as.factor(Mturk_data$langO)), caption = "Frequency & percentage for language other var STUDY 2 - mturk", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| x | freq | |
|---|---|---|
| 1 | 484 | |
| 2 | German | 1 |
| 3 | italian | 2 |
| 4 | malayalam | 1 |
| 5 | Malayalam | 1 |
| 6 | MALAYALAM | 1 |
| 7 | portuguese | 2 |
| 8 | Portuguese | 3 |
| 9 | tamil | 3 |
| 10 | Tamil | 4 |
| 11 | TAMIL | 1 |
| 12 | Turkish | 1 |
| 13 | Urdu | 1 |
# export tables as csv files
write.csv(sona_nom_demo_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_t(oct28).csv", row.names = FALSE)
write.csv(sona_nom_demo_employ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_employ_t(oct28).csv", row.names = FALSE)
write.csv(sona_nom_demo_industry_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_industry_t(oct28).csv", row.names = FALSE)
write.csv(plyr::count(as.factor(SONA_data$langO)),"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_employ_t(oct28).csv", row.names = FALSE)
write.csv(mturk_nom_demo_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_t(oct28).csv", row.names = FALSE)
write.csv(mturk_nom_demo_employ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_employ_t(oct28).csv", row.names = FALSE)
write.csv(mturk_nom_demo_industry_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_industry_t(oct28).csv", row.names = FALSE)
write.csv(plyr::count(as.factor(Mturk_data$langO)),"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_employ_t(oct28).csv", row.names = FALSE)Questionnaires (levels)
Tables
# output is a list of tibbles, each with: $xxx = var name, x = level(s), n = count, pct = percentage
## FOR STUDY 1 DATA -- SONA
sona_nom_ques_all <-
SONA_data %>%
# get subset of data with only nominal vars
select(ends_with("_sum")) %>%
# divide sum scores into L>M>H
# details provided for 1st instance...
mutate(NMPQ_sum = # name of var, this replaces existing b/c its the same
cut(NMPQ_sum, # state var
breaks = c(20, 43, 67, 91, 115, 140), # this is providing a seq from 20 >> 140, with breaks of 40 (40 det by breaking up the range into 3: 140-20 = 120, 120/3 = 40)
labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% # state the new labels for the levels
mutate(SAD_sum = cut(SAD_sum, breaks = c(7, 23, 40, 57, 74, 91), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_dep_sum = cut(SAD_dep_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_ea_sum = cut(SAD_ea_sum, breaks = c(4, 8, 13, 18, 23, 28), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_access_sum = cut(SAD_access_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_dist_sum = cut(SAD_dist_sum, breaks = c(2, 4, 6, 8, 11, 14), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(BIS_sum = cut(BIS_sum, breaks = c(8, 12, 17, 22, 27, 32), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_N_sum = cut(NEO_N_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_E_sum = cut(NEO_E_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_O_sum = cut(NEO_O_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_A_sum = cut(NEO_A_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_C_sum = cut(NEO_C_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(RSES_sum = cut(RSES_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SRS_sum = cut(SRS_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SSEIT_sum = cut(SSEIT_sum, breaks = c(33, 59, 86, 113, 140, 165), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 9, 13, 20, 27, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 7, 9, 14, 19, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 14, 18, 25, 33, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T))
# get freq & proportion for each var
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
# make list into 1 long data frame to show as table...
sona_nom_ques_all_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(sona_nom_ques_all, bind_rows, .id = "tib")
## FOR STUDY 2 DATA -- MTURK
mturk_nom_ques_all <-
Mturk_data %>%
# get subset of data with only nominal vars
select(ends_with("_sum")) %>%
# divide sum scores into L>M>H
# details provided for 1st instance...
mutate(NMPQ_sum = # name of var, this replaces existing b/c its the same
cut(NMPQ_sum, # state var
breaks = c(20, 43, 67, 91, 115, 140), # this is providing a seq from 20 >> 140, with breaks of 40 (40 det by breaking up the range into 3: 140-20 = 120, 120/3 = 40)
labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% # state the new labels for the levels
mutate(SAD_sum = cut(SAD_sum, breaks = c(7, 23, 40, 57, 74, 91), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_dep_sum = cut(SAD_dep_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_ea_sum = cut(SAD_ea_sum, breaks = c(4, 8, 13, 18, 23, 28), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_access_sum = cut(SAD_access_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SAD_dist_sum = cut(SAD_dist_sum, breaks = c(2, 4, 6, 8, 11, 14), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(BIS_sum = cut(BIS_sum, breaks = c(8, 12, 17, 22, 27, 32), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_N_sum = cut(NEO_N_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_E_sum = cut(NEO_E_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_O_sum = cut(NEO_O_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_A_sum = cut(NEO_A_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(NEO_C_sum = cut(NEO_C_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(RSES_sum = cut(RSES_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SRS_sum = cut(SRS_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(SSEIT_sum = cut(SSEIT_sum, breaks = c(33, 59, 86, 113, 140, 165), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 9, 13, 20, 27, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 7, 9, 14, 19, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 14, 18, 25, 33, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
# mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T))
# get freq & proportion for each var
purrr::map(~ count(tibble(x = .x), x) %>%
mutate(pct = (n / sum(n) * 100)))
# make list into 1 long data frame to show as table...
mturk_nom_ques_all_t <-
# this keeps the var name in the 1st column (using-- .id = "tib")
map_dfr(mturk_nom_ques_all, bind_rows, .id = "tib")kable(sona_nom_ques_all_t, caption = "Frequency & percentage for nominal vars - ques- STUDY 1 - SONA.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
footnote(general = "There was no task completed during study 1.") %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | NMPQ_sum | Low | 7 | 2.9166667 |
| 2 | NMPQ_sum | Mild | 36 | 15.0000000 |
| 3 | NMPQ_sum | Moderate | 86 | 35.8333333 |
| 4 | NMPQ_sum | High | 84 | 35.0000000 |
| 5 | NMPQ_sum | Very High | 27 | 11.2500000 |
| 6 | SRS_sum | Low | 4 | 1.6666667 |
| 7 | SRS_sum | Mild | 24 | 10.0000000 |
| 8 | SRS_sum | Moderate | 124 | 51.6666667 |
| 9 | SRS_sum | High | 74 | 30.8333333 |
| 10 | SRS_sum | Very High | 14 | 5.8333333 |
| 11 | SAD_sum | Low | 5 | 2.0833333 |
| 12 | SAD_sum | Mild | 14 | 5.8333333 |
| 13 | SAD_sum | Moderate | 83 | 34.5833333 |
| 14 | SAD_sum | High | 111 | 46.2500000 |
| 15 | SAD_sum | Very High | 27 | 11.2500000 |
| 16 | SAD_dep_sum | Low | 25 | 10.4166667 |
| 17 | SAD_dep_sum | Mild | 39 | 16.2500000 |
| 18 | SAD_dep_sum | Moderate | 75 | 31.2500000 |
| 19 | SAD_dep_sum | High | 80 | 33.3333333 |
| 20 | SAD_dep_sum | Very High | 21 | 8.7500000 |
| 21 | SAD_access_sum | Low | 8 | 3.3333333 |
| 22 | SAD_access_sum | Mild | 31 | 12.9166667 |
| 23 | SAD_access_sum | Moderate | 85 | 35.4166667 |
| 24 | SAD_access_sum | High | 85 | 35.4166667 |
| 25 | SAD_access_sum | Very High | 31 | 12.9166667 |
| 26 | SAD_ea_sum | Low | 6 | 2.5000000 |
| 27 | SAD_ea_sum | Mild | 31 | 12.9166667 |
| 28 | SAD_ea_sum | Moderate | 96 | 40.0000000 |
| 29 | SAD_ea_sum | High | 87 | 36.2500000 |
| 30 | SAD_ea_sum | Very High | 20 | 8.3333333 |
| 31 | SAD_dist_sum | Low | 11 | 4.5833333 |
| 32 | SAD_dist_sum | Mild | 16 | 6.6666667 |
| 33 | SAD_dist_sum | Moderate | 44 | 18.3333333 |
| 34 | SAD_dist_sum | High | 102 | 42.5000000 |
| 35 | SAD_dist_sum | Very High | 67 | 27.9166667 |
| 36 | DASS_D_sum | Low | 186 | 77.5000000 |
| 37 | DASS_D_sum | Mild | 31 | 12.9166667 |
| 38 | DASS_D_sum | Moderate | 21 | 8.7500000 |
| 39 | DASS_D_sum | High | 2 | 0.8333333 |
| 40 | DASS_A_sum | Low | 163 | 67.9166667 |
| 41 | DASS_A_sum | Mild | 35 | 14.5833333 |
| 42 | DASS_A_sum | Moderate | 40 | 16.6666667 |
| 43 | DASS_A_sum | High | 2 | 0.8333333 |
| 44 | DASS_S_sum | Low | 228 | 95.0000000 |
| 45 | DASS_S_sum | Mild | 12 | 5.0000000 |
| 46 | BIS_sum | Low | 18 | 7.5000000 |
| 47 | BIS_sum | Mild | 100 | 41.6666667 |
| 48 | BIS_sum | Moderate | 96 | 40.0000000 |
| 49 | BIS_sum | High | 24 | 10.0000000 |
| 50 | BIS_sum | Very High | 2 | 0.8333333 |
| 51 | SSEIT_sum | Low | 1 | 0.4166667 |
| 52 | SSEIT_sum | Mild | 5 | 2.0833333 |
| 53 | SSEIT_sum | Moderate | 48 | 20.0000000 |
| 54 | SSEIT_sum | High | 163 | 67.9166667 |
| 55 | SSEIT_sum | Very High | 23 | 9.5833333 |
| 56 | RSES_sum | Low | 28 | 11.6666667 |
| 57 | RSES_sum | Mild | 61 | 25.4166667 |
| 58 | RSES_sum | Moderate | 121 | 50.4166667 |
| 59 | RSES_sum | High | 26 | 10.8333333 |
| 60 | RSES_sum | Very High | 4 | 1.6666667 |
| 61 | NEO_N_sum | Low | 7 | 2.9166667 |
| 62 | NEO_N_sum | Mild | 57 | 23.7500000 |
| 63 | NEO_N_sum | Moderate | 124 | 51.6666667 |
| 64 | NEO_N_sum | High | 48 | 20.0000000 |
| 65 | NEO_N_sum | Very High | 4 | 1.6666667 |
| 66 | NEO_E_sum | Low | 1 | 0.4166667 |
| 67 | NEO_E_sum | Mild | 15 | 6.2500000 |
| 68 | NEO_E_sum | Moderate | 89 | 37.0833333 |
| 69 | NEO_E_sum | High | 101 | 42.0833333 |
| 70 | NEO_E_sum | Very High | 34 | 14.1666667 |
| 71 | NEO_O_sum | Mild | 9 | 3.7500000 |
| 72 | NEO_O_sum | Moderate | 106 | 44.1666667 |
| 73 | NEO_O_sum | High | 106 | 44.1666667 |
| 74 | NEO_O_sum | Very High | 19 | 7.9166667 |
| 75 | NEO_A_sum | Mild | 5 | 2.0833333 |
| 76 | NEO_A_sum | Moderate | 71 | 29.5833333 |
| 77 | NEO_A_sum | High | 110 | 45.8333333 |
| 78 | NEO_A_sum | Very High | 54 | 22.5000000 |
| 79 | NEO_C_sum | Mild | 17 | 7.0833333 |
| 80 | NEO_C_sum | Moderate | 123 | 51.2500000 |
| 81 | NEO_C_sum | High | 93 | 38.7500000 |
| 82 | NEO_C_sum | Very High | 7 | 2.9166667 |
| Note: | ||||
| There was no task completed during study 1. |
kable(mturk_nom_ques_all_t, caption = "Frequency & percentage for nominal vars - ques- STUDY 2 - MTURK", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>%
footnote(general = "There was no task completed during study 2.") %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic()| Var | level | n | % | |
|---|---|---|---|---|
| 1 | NMPQ_sum | Low | 38 | 7.5247525 |
| 2 | NMPQ_sum | Mild | 62 | 12.2772277 |
| 3 | NMPQ_sum | Moderate | 125 | 24.7524752 |
| 4 | NMPQ_sum | High | 198 | 39.2079208 |
| 5 | NMPQ_sum | Very High | 82 | 16.2376238 |
| 6 | SRS_sum | Low | 3 | 0.5940594 |
| 7 | SRS_sum | Mild | 22 | 4.3564356 |
| 8 | SRS_sum | Moderate | 183 | 36.2376238 |
| 9 | SRS_sum | High | 183 | 36.2376238 |
| 10 | SRS_sum | Very High | 114 | 22.5742574 |
| 11 | SAD_sum | Low | 21 | 4.1584158 |
| 12 | SAD_sum | Mild | 68 | 13.4653465 |
| 13 | SAD_sum | Moderate | 126 | 24.9504950 |
| 14 | SAD_sum | High | 196 | 38.8118812 |
| 15 | SAD_sum | Very High | 94 | 18.6138614 |
| 16 | SAD_dep_sum | Low | 63 | 12.4752475 |
| 17 | SAD_dep_sum | Mild | 72 | 14.2574257 |
| 18 | SAD_dep_sum | Moderate | 119 | 23.5643564 |
| 19 | SAD_dep_sum | High | 194 | 38.4158416 |
| 20 | SAD_dep_sum | Very High | 57 | 11.2871287 |
| 21 | SAD_access_sum | Low | 40 | 7.9207921 |
| 22 | SAD_access_sum | Mild | 54 | 10.6930693 |
| 23 | SAD_access_sum | Moderate | 129 | 25.5445545 |
| 24 | SAD_access_sum | High | 213 | 42.1782178 |
| 25 | SAD_access_sum | Very High | 69 | 13.6633663 |
| 26 | SAD_ea_sum | Low | 41 | 8.1188119 |
| 27 | SAD_ea_sum | Mild | 67 | 13.2673267 |
| 28 | SAD_ea_sum | Moderate | 138 | 27.3267327 |
| 29 | SAD_ea_sum | High | 170 | 33.6633663 |
| 30 | SAD_ea_sum | Very High | 89 | 17.6237624 |
| 31 | SAD_dist_sum | Low | 82 | 16.2376238 |
| 32 | SAD_dist_sum | Mild | 62 | 12.2772277 |
| 33 | SAD_dist_sum | Moderate | 89 | 17.6237624 |
| 34 | SAD_dist_sum | High | 174 | 34.4554455 |
| 35 | SAD_dist_sum | Very High | 98 | 19.4059406 |
| 36 | DASS_D_sum | Low | 278 | 55.0495050 |
| 37 | DASS_D_sum | Mild | 100 | 19.8019802 |
| 38 | DASS_D_sum | Moderate | 119 | 23.5643564 |
| 39 | DASS_D_sum | High | 8 | 1.5841584 |
| 40 | DASS_A_sum | Low | 258 | 51.0891089 |
| 41 | DASS_A_sum | Mild | 39 | 7.7227723 |
| 42 | DASS_A_sum | Moderate | 116 | 22.9702970 |
| 43 | DASS_A_sum | High | 88 | 17.4257426 |
| 44 | DASS_A_sum | Very High | 4 | 0.7920792 |
| 45 | DASS_S_sum | Low | 408 | 80.7920792 |
| 46 | DASS_S_sum | Mild | 79 | 15.6435644 |
| 47 | DASS_S_sum | Moderate | 18 | 3.5643564 |
| 48 | BIS_sum | Low | 150 | 29.7029703 |
| 49 | BIS_sum | Mild | 175 | 34.6534653 |
| 50 | BIS_sum | Moderate | 161 | 31.8811881 |
| 51 | BIS_sum | High | 18 | 3.5643564 |
| 52 | BIS_sum | Very High | 1 | 0.1980198 |
| 53 | SSEIT_sum | Low | 1 | 0.1980198 |
| 54 | SSEIT_sum | Mild | 17 | 3.3663366 |
| 55 | SSEIT_sum | Moderate | 131 | 25.9405941 |
| 56 | SSEIT_sum | High | 295 | 58.4158416 |
| 57 | SSEIT_sum | Very High | 61 | 12.0792079 |
| 58 | RSES_sum | Low | 89 | 17.6237624 |
| 59 | RSES_sum | Mild | 118 | 23.3663366 |
| 60 | RSES_sum | Moderate | 218 | 43.1683168 |
| 61 | RSES_sum | High | 44 | 8.7128713 |
| 62 | RSES_sum | Very High | 36 | 7.1287129 |
| 63 | NEO_N_sum | Low | 94 | 18.6138614 |
| 64 | NEO_N_sum | Mild | 113 | 22.3762376 |
| 65 | NEO_N_sum | Moderate | 228 | 45.1485149 |
| 66 | NEO_N_sum | High | 64 | 12.6732673 |
| 67 | NEO_N_sum | Very High | 6 | 1.1881188 |
| 68 | NEO_E_sum | Low | 16 | 3.1683168 |
| 69 | NEO_E_sum | Mild | 76 | 15.0495050 |
| 70 | NEO_E_sum | Moderate | 172 | 34.0594059 |
| 71 | NEO_E_sum | High | 205 | 40.5940594 |
| 72 | NEO_E_sum | Very High | 36 | 7.1287129 |
| 73 | NEO_O_sum | Mild | 42 | 8.3168317 |
| 74 | NEO_O_sum | Moderate | 284 | 56.2376238 |
| 75 | NEO_O_sum | High | 141 | 27.9207921 |
| 76 | NEO_O_sum | Very High | 38 | 7.5247525 |
| 77 | NEO_A_sum | Low | 1 | 0.1980198 |
| 78 | NEO_A_sum | Mild | 16 | 3.1683168 |
| 79 | NEO_A_sum | Moderate | 256 | 50.6930693 |
| 80 | NEO_A_sum | High | 138 | 27.3267327 |
| 81 | NEO_A_sum | Very High | 94 | 18.6138614 |
| 82 | NEO_C_sum | Mild | 15 | 2.9702970 |
| 83 | NEO_C_sum | Moderate | 235 | 46.5346535 |
| 84 | NEO_C_sum | High | 190 | 37.6237624 |
| 85 | NEO_C_sum | Very High | 65 | 12.8712871 |
| Note: | ||||
| There was no task completed during study 2. |
# export table as csv file
write.csv(sona_nom_ques_all_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_ques_all_t(oct28).csv", row.names = FALSE)
write.csv(mturk_nom_ques_all_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_ques_all_t(oct28).csv", row.names = FALSE)Stacked Bar Plots
Show the questionnaire data as a stacked bar plot for each study for visualization comparison.
# FOR SAD OVERALL ONLY
# create color pallet for stacked bar plots
# stacked_colours <- c("#7E1044", "#981570", "#6B138B", "#491296", "#170FA7")
stacked_colours <- c("#98410B", "#F0803C", "#3F826D", "#08605F", "#414535")
# Study 1
# Make data file
sona_nom_ques_stack_data <-
sona_nom_ques_all_t %>%
select(-n) %>%
mutate_at("tib", str_replace, "_sum", "") %>% # remove "_sum" from factor
# remove SAD subscales
filter(!tib == "SAD_dep") %>%
filter(!tib == "SAD_ea") %>%
filter(!tib == "SAD_access") %>%
filter(!tib == "SAD_dist")
sona_nom_ques_stack_plot <-
ggplot(sona_nom_ques_stack_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) +
geom_bar(position="stack", stat="identity") +
scale_x_discrete(limits = c("NMPQ", "SAD", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
scale_fill_manual(values=stacked_colours) +
# geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
# add labels
labs(
# title = "Stacked Barplot for Questionnaire Scores: Study 1",
# subtitle = "x",
# caption = "Note: ",
x = "Measure",
y = "Proportion (%)",
fill = "Level"
) +
theme_classic() +
# add specific formatting
theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "black", size = 13),
plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
text = element_text(size = 13),
axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text = element_text(color = "black"))
# show plot
sona_nom_ques_stack_plot
# Study 2
# Make data file
mturk_nom_ques_stack_data <-
mturk_nom_ques_all_t %>%
select(-n) %>%
mutate_at("tib", str_replace, "_sum", "") %>% # remove "_sum" from factor
# remove SAD subscales
filter(!tib == "SAD_dep") %>%
filter(!tib == "SAD_ea") %>%
filter(!tib == "SAD_access") %>%
filter(!tib == "SAD_dist")
mturk_nom_ques_stack_plot <-
ggplot(mturk_nom_ques_stack_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) +
geom_bar(position="stack", stat="identity") +
scale_x_discrete(limits = c("NMPQ", "SAD", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
scale_fill_manual(values=stacked_colours) +
# geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
# add labels
labs(
# title = "Stacked Barplot for Questionnaire Scores: Study 2",
# subtitle = "x",
# caption = "Note: ",
x = "Measure",
y = "Proportion (%)",
fill = "Level"
) +
theme_classic() +
# add specific formatting
theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "black", size = 13),
plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
text = element_text(size = 13),
axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text = element_text(color = "black"))
# show plot
mturk_nom_ques_stack_plot
# export plots
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_nom_ques_stack_plot(oct28)", plot = sona_nom_ques_stack_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_nom_ques_stack_plot(oct28)", plot = mturk_nom_ques_stack_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
# FOR ALL SUBSCALES + SAD OVERALL
# create color pallet for stacked bar plots
stacked_colours_exp <- c("#98410B", "#F0803C", "#3F826D", "#08605F", "#414535")
# Study 1
# Make data file
sona_nom_ques_stack_exp_data <-
sona_nom_ques_all_t %>%
select(-n) %>%
mutate_at("tib", str_replace, "_sum", "") # remove "_sum" from factor
# filter(!tib == "SAD") %>% # remove SAD general
sona_nom_ques_stack_exp_plot <-
ggplot(sona_nom_ques_stack_exp_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) +
geom_bar(position="stack", stat="identity") +
scale_x_discrete(limits = c("NMPQ", "SAD", "SAD_dep", "SAD_ea", "SAD_access", "SAD_dist", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
# geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
# add labels
labs(
title = "Stacked Barplot for Questionnaire Scores: Study 1 -- ALL Exploratory",
# subtitle = "x",
# caption = "Note: ",
x = "Measure",
y = "Proportion (%)",
fill = "Level"
) +
theme_classic() +
# add specific formatting
theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "black", size = 13),
plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
text = element_text(size = 13),
axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text = element_text(color = "black"))
# show plot
sona_nom_ques_stack_exp_plot
# Study 2
# Make data file
mturk_nom_ques_stack_exp_data <-
mturk_nom_ques_all_t %>%
select(-n) %>%
mutate_at("tib", str_replace, "_sum", "") # remove "_sum" from factor
# filter(!tib == "SAD") %>% # remove SAD general
mturk_nom_ques_stack_exp_plot <-
ggplot(mturk_nom_ques_stack_exp_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) +
geom_bar(position="stack", stat="identity") +
scale_x_discrete(limits = c("NMPQ", "SAD", "SAD_dep", "SAD_ea", "SAD_access", "SAD_dist", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
# geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
# add labels
labs(
title = "Stacked Barplot for Questionnaire Scores: Study 2 -- ALL Exploratory",
# subtitle = "x",
# caption = "Note: ",
x = "Measure",
y = "Proportion (%)",
fill = "Level"
) +
theme_classic() +
# add specific formatting
theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "black", size = 13),
plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
text = element_text(size = 13),
axis.ticks.x = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
axis.text = element_text(color = "black"))
# show plot
mturk_nom_ques_stack_exp_plot
# export plots
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_nom_ques_stack_exp_plot(oct28)", plot = sona_nom_ques_stack_exp_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_nom_ques_stack_exp_plot(oct28)", plot = mturk_nom_ques_stack_exp_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
CONTINUOUS data (pilot & main)
Demographic & Typical Smartphone Use
## FOR SONA
sona_cont_demo <-
SONA_data %>%
# select continuous vars
select(age, age_first_phone, ST_weekly_tot_hours:ST_daily_not, com_gen:com_room_task, dist_daily, dist_study, pow_not_using:pow_sleep) %>%
# apply describe fn for: n mean sd median trimmed mad min max range skew kurtosis se
psych::describe()
## FOR MTURK
mturk_cont_demo <-
Mturk_data %>%
# select continuous vars
select(age, age_first_phone, ST_weekly_tot_hours:ST_daily_not, com_gen:com_room_task, dist_daily, dist_study, pow_not_using:pow_sleep) %>%
# apply describe fn for: n mean sd median trimmed mad min max range skew kurtosis se
psych::describe()# show all with kable
## SONA
kable(sona_cont_demo, caption = "Descriptive statistics for continuous vars - STUDY 1 - SONA", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic() | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | 240 | 19.291667 | 2.196977 | 19 | 18.906250 | 1.4826 | 17 | 42 | 25 | 5.4163685 | 47.1638207 | 0.1418143 |
| age_first_phone | 2 | 240 | 13.170833 | 2.037305 | 13 | 13.177083 | 1.4826 | 7 | 25 | 18 | 0.5762169 | 4.6074785 | 0.1315074 |
| ST_weekly_tot_hours | 3 | 174 | 19.523678 | 19.244227 | 10 | 16.168714 | 8.8956 | 2 | 75 | 73 | 1.3555478 | 0.7717960 | 1.4589011 |
| ST_daily_pickups | 4 | 175 | 126.280000 | 161.059910 | 94 | 95.404255 | 66.7170 | 1 | 1306 | 1305 | 4.1568352 | 21.7944571 | 12.1749848 |
| ST_daily_not | 5 | 175 | 222.548571 | 219.552017 | 180 | 187.000000 | 139.3644 | 5 | 1931 | 1926 | 3.8290616 | 22.8882623 | 16.5965725 |
| com_gen | 6 | 240 | 3.808333 | 1.798632 | 4 | 3.833333 | 2.9652 | 1 | 7 | 6 | -0.0527964 | -1.1977960 | 0.1161012 |
| com_unattended | 7 | 240 | 3.712500 | 1.880491 | 4 | 3.687500 | 2.9652 | 1 | 7 | 6 | 0.0749738 | -1.2427677 | 0.1213851 |
| com_leave_with_others | 8 | 240 | 3.579167 | 1.852846 | 3 | 3.552083 | 2.9652 | 1 | 7 | 6 | 0.1249584 | -1.2759083 | 0.1196007 |
| com_locked | 9 | 240 | 5.445833 | 1.596670 | 6 | 5.697917 | 1.4826 | 1 | 7 | 6 | -1.1886413 | 0.7694822 | 0.1030646 |
| com_room_task | 10 | 240 | 4.762500 | 1.773188 | 5 | 4.901042 | 1.4826 | 1 | 7 | 6 | -0.5772299 | -0.7324053 | 0.1144588 |
| dist_daily | 11 | 240 | 5.883333 | 1.357966 | 6 | 6.130208 | 1.4826 | 1 | 7 | 6 | -1.5064133 | 2.1283299 | 0.0876563 |
| dist_study | 12 | 240 | 3.708333 | 2.034826 | 3 | 3.635417 | 2.9652 | 1 | 7 | 6 | 0.2570851 | -1.3566423 | 0.1313474 |
| pow_not_using | 13 | 240 | 3.337500 | 2.137200 | 3 | 3.197917 | 2.9652 | 1 | 7 | 6 | 0.3809293 | -1.3957052 | 0.1379557 |
| pow_notifications_on | 14 | 240 | 5.033333 | 1.822378 | 6 | 5.234375 | 1.4826 | 1 | 7 | 6 | -0.7307107 | -0.6131207 | 0.1176340 |
| pow_vibrate | 15 | 240 | 5.262500 | 1.893020 | 6 | 5.526042 | 1.4826 | 1 | 7 | 6 | -0.9273508 | -0.4058821 | 0.1221939 |
| pow_study | 16 | 198 | 5.237374 | 1.639988 | 6 | 5.431250 | 1.4826 | 1 | 7 | 6 | -0.8965055 | -0.2430940 | 0.1165489 |
| pow_exam | 17 | 198 | 2.333333 | 1.830370 | 1 | 2.000000 | 0.0000 | 1 | 7 | 6 | 1.2688986 | 0.3357456 | 0.1300787 |
| pow_lec | 18 | 198 | 5.585859 | 1.467136 | 6 | 5.818750 | 1.4826 | 1 | 7 | 6 | -1.2619849 | 0.9292651 | 0.1042648 |
| pow_work | 19 | 240 | 5.466667 | 1.448897 | 6 | 5.656250 | 1.4826 | 1 | 7 | 6 | -1.0641316 | 0.3857319 | 0.0935259 |
| pow_meeting | 20 | 240 | 4.070833 | 1.974519 | 4 | 4.088542 | 2.9652 | 1 | 7 | 6 | -0.0395616 | -1.3252132 | 0.1274547 |
| pow_sleep | 21 | 240 | 5.308333 | 1.853622 | 6 | 5.588542 | 1.4826 | 1 | 7 | 6 | -1.1047394 | -0.0154955 | 0.1196508 |
## MTURK
kable(mturk_cont_demo, caption = "Descriptive statistics for continuous vars - STUDY 2 - MTURK", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic() | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | 505 | 34.176238 | 9.999138 | 31.0 | 32.632099 | 7.4130 | 18.0 | 69 | 51.0 | 1.3860606 | 1.5732548 | 0.4449558 |
| age_first_phone | 2 | 505 | 37.980000 | 166.497525 | 21.0 | 21.920988 | 5.9304 | 2.0 | 2012 | 2010.0 | 11.2199805 | 125.8396767 | 7.4090426 |
| ST_weekly_tot_hours | 3 | 161 | 12.093168 | 15.897543 | 7.0 | 8.744186 | 4.4478 | 1.0 | 145 | 144.0 | 4.7196505 | 31.2038203 | 1.2529019 |
| ST_daily_pickups | 4 | 162 | 44.746914 | 102.317209 | 21.0 | 26.007692 | 13.3434 | 2.0 | 1107 | 1105.0 | 7.7651389 | 72.2610502 | 8.0387992 |
| ST_daily_not | 5 | 162 | 160.367284 | 148.390734 | 101.5 | 140.707692 | 124.5384 | 2.5 | 757 | 754.5 | 1.1835390 | 1.4041257 | 11.6586771 |
| com_gen | 6 | 505 | 4.049505 | 1.978938 | 4.0 | 4.061728 | 2.9652 | 1.0 | 7 | 6.0 | -0.0930562 | -1.3356833 | 0.0880616 |
| com_unattended | 7 | 505 | 3.679208 | 2.018777 | 4.0 | 3.612346 | 2.9652 | 1.0 | 7 | 6.0 | 0.1259784 | -1.3642733 | 0.0898344 |
| com_leave_with_others | 8 | 505 | 3.514851 | 2.008977 | 3.0 | 3.437037 | 2.9652 | 1.0 | 7 | 6.0 | 0.2054496 | -1.3665436 | 0.0893983 |
| com_locked | 9 | 505 | 5.338614 | 1.651777 | 6.0 | 5.577778 | 1.4826 | 1.0 | 7 | 6.0 | -1.0059877 | 0.1744308 | 0.0735031 |
| com_room_task | 10 | 505 | 4.562376 | 1.825897 | 5.0 | 4.681482 | 1.4826 | 1.0 | 7 | 6.0 | -0.5428155 | -0.8274297 | 0.0812513 |
| dist_daily | 11 | 505 | 4.499010 | 1.747021 | 5.0 | 4.575309 | 1.4826 | 1.0 | 7 | 6.0 | -0.4885664 | -0.8460976 | 0.0777414 |
| dist_study | 12 | 505 | 3.318812 | 2.185664 | 2.0 | 3.158025 | 1.4826 | 1.0 | 7 | 6.0 | 0.3531052 | -1.4378461 | 0.0972608 |
| pow_not_using | 13 | 505 | 3.459406 | 2.089950 | 3.0 | 3.345679 | 2.9652 | 1.0 | 7 | 6.0 | 0.2652943 | -1.3984467 | 0.0930016 |
| pow_notifications_on | 14 | 505 | 5.190099 | 1.629192 | 6.0 | 5.404938 | 1.4826 | 1.0 | 7 | 6.0 | -0.9828771 | 0.1326688 | 0.0724981 |
| pow_vibrate | 15 | 505 | 4.996040 | 1.823563 | 6.0 | 5.197531 | 1.4826 | 1.0 | 7 | 6.0 | -0.8131320 | -0.4420760 | 0.0811475 |
| pow_study | 16 | 19 | 5.578947 | 1.346427 | 6.0 | 5.647059 | 1.4826 | 3.0 | 7 | 4.0 | -0.5468857 | -0.9825520 | 0.3088915 |
| pow_exam | 17 | 19 | 4.631579 | 1.920953 | 5.0 | 4.705882 | 1.4826 | 1.0 | 7 | 6.0 | -0.4353807 | -1.2832642 | 0.4406967 |
| pow_lec | 18 | 19 | 5.263158 | 1.851189 | 6.0 | 5.411765 | 1.4826 | 1.0 | 7 | 6.0 | -1.1618756 | 0.3016434 | 0.4246919 |
| pow_work | 19 | 505 | 5.540594 | 1.423595 | 6.0 | 5.748148 | 1.4826 | 1.0 | 7 | 6.0 | -1.1814237 | 1.1641249 | 0.0633491 |
| pow_meeting | 20 | 505 | 4.895049 | 1.791424 | 5.0 | 5.064197 | 1.4826 | 1.0 | 7 | 6.0 | -0.6755025 | -0.5803137 | 0.0797173 |
| pow_sleep | 21 | 505 | 5.437624 | 1.651289 | 6.0 | 5.713580 | 1.4826 | 1.0 | 7 | 6.0 | -1.2268137 | 0.7212494 | 0.0734814 |
# export tables as csv files
write.csv(sona_cont_demo,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_cont_demo(oct28).csv", row.names = T)
write.csv(mturk_cont_demo,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_cont_demo(oct28).csv", row.names = T)Questionnaires
## FOR SONA
sona_cont_ques <-
SONA_data %>%
# select continuous vars
select(starts_with("NMPQ"), starts_with("SRS"), starts_with("SAD"), starts_with("DASS"), starts_with("BIS"), starts_with("SSEIT"), starts_with("RSES"), starts_with("NEO")) %>%
# apply describe fn for: n mean sd median trimmed mad min max range skew kurtosis se
psych::describe()
## FOR MTURK
mturk_cont_ques <-
Mturk_data %>%
# select continuous vars
select(starts_with("NMPQ"), starts_with("SRS"), starts_with("SAD"), starts_with("DASS"), starts_with("BIS"), starts_with("SSEIT"), starts_with("RSES"), starts_with("NEO")) %>%
# apply describe fn for: n mean sd median trimmed mad min max range skew kurtosis se
psych::describe()# show all with kable
## SONA
kable(sona_cont_ques, caption = "Descriptive statistics for questionnaires - STUDY 1 - SONA", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic() | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NMPQ_1 | 1 | 240 | 4.6333333 | 1.5628176 | 5.0 | 4.7239583 | 1.4826 | 1 | 7 | 6 | -0.5546012 | -0.3963184 | 0.1008794 |
| NMPQ_2 | 2 | 240 | 5.4083333 | 1.2968958 | 6.0 | 5.5677083 | 1.4826 | 1 | 7 | 6 | -1.1857798 | 1.4939168 | 0.0837143 |
| NMPQ_3 | 3 | 240 | 4.2916667 | 1.6662831 | 4.0 | 4.3229167 | 1.4826 | 1 | 7 | 6 | -0.2058734 | -0.9189510 | 0.1075581 |
| NMPQ_4 | 4 | 240 | 5.2041667 | 1.4303872 | 6.0 | 5.3541667 | 1.4826 | 1 | 7 | 6 | -0.9489913 | 0.5321203 | 0.0923311 |
| NMPQ_5 | 5 | 240 | 4.1375000 | 1.7790770 | 4.0 | 4.1458333 | 1.4826 | 1 | 7 | 6 | -0.1092650 | -1.0783173 | 0.1148389 |
| NMPQ_6 | 6 | 240 | 3.5958333 | 1.7688564 | 4.0 | 3.5520833 | 2.9652 | 1 | 7 | 6 | 0.1788387 | -1.0695255 | 0.1141792 |
| NMPQ_7 | 7 | 240 | 4.3875000 | 1.6854813 | 5.0 | 4.4739583 | 1.4826 | 1 | 7 | 6 | -0.4405439 | -0.7476440 | 0.1087974 |
| NMPQ_8 | 8 | 240 | 4.3750000 | 1.7599092 | 5.0 | 4.4166667 | 1.4826 | 1 | 7 | 6 | -0.3160297 | -0.9738900 | 0.1136016 |
| NMPQ_9 | 9 | 240 | 5.1166667 | 1.5561100 | 5.0 | 5.2968750 | 1.4826 | 1 | 7 | 6 | -0.8569090 | 0.2121639 | 0.1004465 |
| NMPQ_10 | 10 | 240 | 4.6041667 | 1.5703794 | 5.0 | 4.7135417 | 1.4826 | 1 | 7 | 6 | -0.5919725 | -0.4185462 | 0.1013676 |
| NMPQ_11 | 11 | 240 | 4.9708333 | 1.5828525 | 5.0 | 5.1197917 | 1.4826 | 1 | 7 | 6 | -0.6898711 | -0.2576809 | 0.1021727 |
| NMPQ_12 | 12 | 240 | 4.6916667 | 1.5019977 | 5.0 | 4.8229167 | 1.4826 | 1 | 7 | 6 | -0.6937925 | -0.1015623 | 0.0969535 |
| NMPQ_13 | 13 | 240 | 4.8208333 | 1.5541762 | 5.0 | 4.9427083 | 1.4826 | 1 | 7 | 6 | -0.6540960 | -0.2468093 | 0.1003216 |
| NMPQ_14 | 14 | 240 | 4.9500000 | 1.4513015 | 5.0 | 5.0833333 | 1.4826 | 1 | 7 | 6 | -0.8375474 | 0.1346325 | 0.0936811 |
| NMPQ_15 | 15 | 240 | 4.3583333 | 1.7030410 | 5.0 | 4.3697917 | 1.4826 | 1 | 7 | 6 | -0.1503339 | -1.0813947 | 0.1099308 |
| NMPQ_16 | 16 | 240 | 3.1958333 | 1.7135881 | 3.0 | 3.0729167 | 1.4826 | 1 | 7 | 6 | 0.4263693 | -0.7897426 | 0.1106116 |
| NMPQ_17 | 17 | 240 | 3.6791667 | 1.7287815 | 3.5 | 3.6614583 | 2.2239 | 1 | 7 | 6 | 0.1358321 | -0.9757487 | 0.1115924 |
| NMPQ_18 | 18 | 240 | 3.9750000 | 1.7112109 | 4.0 | 4.0208333 | 1.4826 | 1 | 7 | 6 | -0.1409506 | -1.0016452 | 0.1104582 |
| NMPQ_19 | 19 | 240 | 3.6833333 | 1.7737431 | 4.0 | 3.6770833 | 2.9652 | 1 | 7 | 6 | 0.0611937 | -1.1743649 | 0.1144946 |
| NMPQ_20 | 20 | 240 | 3.8958333 | 1.8231379 | 4.0 | 3.9010417 | 1.4826 | 1 | 7 | 6 | -0.0855913 | -1.0730102 | 0.1176830 |
| NMPQ_sum | 21 | 240 | 87.9750000 | 22.4568251 | 89.0 | 88.6927083 | 21.4977 | 20 | 140 | 120 | -0.3767653 | 0.3604556 | 1.4495818 |
| SRS_1 | 22 | 240 | 2.9916667 | 0.8078675 | 3.0 | 3.0572917 | 0.0000 | 1 | 4 | 3 | -0.6013877 | 0.0265380 | 0.0521476 |
| SRS_2 | 23 | 240 | 2.7291667 | 0.8011389 | 3.0 | 2.7447917 | 1.4826 | 1 | 4 | 3 | -0.2560508 | -0.3704818 | 0.0517133 |
| SRS_3 | 24 | 240 | 2.8291667 | 0.7598608 | 3.0 | 2.8541667 | 0.0000 | 1 | 4 | 3 | -0.4471246 | 0.0553488 | 0.0490488 |
| SRS_4 | 25 | 240 | 3.1541667 | 0.6824023 | 3.0 | 3.2135417 | 0.0000 | 1 | 4 | 3 | -0.5160039 | 0.3291114 | 0.0440489 |
| SRS_5 | 26 | 240 | 2.4083333 | 0.8181603 | 2.0 | 2.3854167 | 1.4826 | 1 | 4 | 3 | 0.2258091 | -0.4632397 | 0.0528120 |
| SRS_6 | 27 | 240 | 2.6875000 | 0.7419371 | 3.0 | 2.6770833 | 0.0000 | 1 | 4 | 3 | -0.2278723 | -0.1989583 | 0.0478918 |
| SRS_7 | 28 | 240 | 2.0500000 | 0.8063555 | 2.0 | 2.0104167 | 1.4826 | 1 | 4 | 3 | 0.3867063 | -0.3987605 | 0.0520500 |
| SRS_8 | 29 | 240 | 2.7041667 | 0.7974747 | 3.0 | 2.7187500 | 1.4826 | 1 | 4 | 3 | -0.2619945 | -0.3460868 | 0.0514768 |
| SRS_9 | 30 | 240 | 2.3041667 | 0.8551978 | 2.0 | 2.2864583 | 1.4826 | 1 | 4 | 3 | 0.0982279 | -0.6944265 | 0.0552028 |
| SRS_10 | 31 | 240 | 2.5083333 | 0.7653132 | 3.0 | 2.5208333 | 1.4826 | 1 | 4 | 3 | -0.0836566 | -0.3856831 | 0.0494008 |
| SRS_sum | 32 | 240 | 26.3666667 | 4.4031813 | 26.0 | 26.4114583 | 4.4478 | 10 | 39 | 29 | -0.2017195 | 0.7388282 | 0.2842241 |
| SAD_dep_1 | 33 | 240 | 4.7125000 | 1.7005568 | 5.0 | 4.8593750 | 1.4826 | 1 | 7 | 6 | -0.7330209 | -0.3297001 | 0.1097705 |
| SAD_dep_2 | 34 | 240 | 4.2708333 | 1.7349422 | 5.0 | 4.3437500 | 1.4826 | 1 | 7 | 6 | -0.3608595 | -0.8490900 | 0.1119900 |
| SAD_dep_3 | 35 | 240 | 4.0166667 | 1.8990565 | 4.0 | 4.0364583 | 2.9652 | 1 | 7 | 6 | -0.1624970 | -1.1808819 | 0.1225836 |
| SAD_4 | 36 | 240 | 4.7750000 | 1.6285245 | 5.0 | 4.9114583 | 1.4826 | 1 | 7 | 6 | -0.6496662 | -0.2970324 | 0.1051208 |
| SAD_access_5 | 37 | 240 | 4.2458333 | 1.8001579 | 4.0 | 4.2864583 | 2.9652 | 1 | 7 | 6 | -0.2312465 | -1.0249841 | 0.1161997 |
| SAD_access_6 | 38 | 240 | 4.4958333 | 1.6184034 | 5.0 | 4.5833333 | 1.4826 | 1 | 7 | 6 | -0.5144971 | -0.5479850 | 0.1044675 |
| SAD_access_7 | 39 | 240 | 5.3625000 | 1.3834988 | 6.0 | 5.5416667 | 1.4826 | 1 | 7 | 6 | -1.0887045 | 1.1891885 | 0.0893045 |
| SAD_ea_8 | 40 | 240 | 3.8083333 | 1.6857554 | 4.0 | 3.8229167 | 1.4826 | 1 | 7 | 6 | -0.0328597 | -1.0656862 | 0.1088150 |
| SAD_ea_9 | 41 | 240 | 4.4500000 | 1.5703849 | 5.0 | 4.5260417 | 1.4826 | 1 | 7 | 6 | -0.4600102 | -0.5187517 | 0.1013679 |
| SAD_ea_10 | 42 | 240 | 4.7208333 | 1.4264328 | 5.0 | 4.8437500 | 1.4826 | 1 | 7 | 6 | -0.6926873 | 0.1172655 | 0.0920758 |
| SAD_ea_11 | 43 | 240 | 4.7875000 | 1.3290486 | 5.0 | 4.8489583 | 1.4826 | 1 | 7 | 6 | -0.5565427 | 0.4181962 | 0.0857897 |
| SAD_dist_12 | 44 | 240 | 4.6625000 | 1.5054469 | 5.0 | 4.7708333 | 1.4826 | 1 | 7 | 6 | -0.5829356 | -0.2587305 | 0.0971762 |
| SAD_dist_13 | 45 | 240 | 4.9791667 | 1.4006810 | 5.0 | 5.1197917 | 1.4826 | 1 | 7 | 6 | -0.9547708 | 0.6061696 | 0.0904136 |
| SAD_sum | 46 | 240 | 59.2875000 | 13.7854393 | 60.0 | 60.0104167 | 11.8608 | 13 | 91 | 78 | -0.6258436 | 0.9193452 | 0.8898463 |
| SAD_dep_sum | 47 | 240 | 13.0000000 | 4.5647366 | 13.0 | 13.2447917 | 4.4478 | 3 | 21 | 18 | -0.3879531 | -0.4534349 | 0.2946525 |
| SAD_access_sum | 48 | 240 | 14.1041667 | 3.9220415 | 14.0 | 14.3177083 | 4.4478 | 3 | 21 | 18 | -0.5417486 | 0.2150910 | 0.2531667 |
| SAD_ea_sum | 49 | 240 | 17.7666667 | 4.6270685 | 18.0 | 18.0104167 | 4.4478 | 4 | 28 | 24 | -0.4824763 | 0.2767665 | 0.2986760 |
| SAD_dist_sum | 50 | 240 | 9.6416667 | 2.5737517 | 10.0 | 9.8333333 | 2.9652 | 2 | 14 | 12 | -0.6830871 | 0.2471243 | 0.1661350 |
| DASS_S_1 | 51 | 240 | 1.2291667 | 0.8089350 | 1.0 | 1.1979167 | 0.0000 | 0 | 3 | 3 | 0.3667204 | -0.2720591 | 0.0522165 |
| DASS_A_2 | 52 | 240 | 1.1916667 | 0.8896977 | 1.0 | 1.1562500 | 1.4826 | 0 | 3 | 3 | 0.1865441 | -0.8467164 | 0.0574297 |
| DASS_D_3 | 53 | 240 | 0.8250000 | 0.7886485 | 1.0 | 0.7447917 | 1.4826 | 0 | 3 | 3 | 0.6738481 | -0.1134913 | 0.0509070 |
| DASS_A_4 | 54 | 240 | 0.5791667 | 0.7935299 | 0.0 | 0.4479167 | 0.0000 | 0 | 3 | 3 | 1.1414707 | 0.3204723 | 0.0512221 |
| DASS_D_5 | 55 | 240 | 1.4458333 | 0.8612918 | 1.0 | 1.4322917 | 1.4826 | 0 | 3 | 3 | 0.0691180 | -0.6590652 | 0.0555961 |
| DASS_S_6 | 56 | 240 | 1.1083333 | 0.8363683 | 1.0 | 1.0677083 | 1.4826 | 0 | 3 | 3 | 0.3515392 | -0.5066854 | 0.0539873 |
| DASS_A_7 | 57 | 240 | 0.5083333 | 0.7317753 | 0.0 | 0.3593750 | 0.0000 | 0 | 3 | 3 | 1.3748488 | 1.3683725 | 0.0472359 |
| DASS_S_8 | 58 | 240 | 0.9875000 | 0.8938715 | 1.0 | 0.9166667 | 1.4826 | 0 | 3 | 3 | 0.4793273 | -0.7096192 | 0.0576992 |
| DASS_A_9 | 59 | 240 | 0.9708333 | 0.9165969 | 1.0 | 0.9010417 | 1.4826 | 0 | 3 | 3 | 0.4467919 | -0.9126361 | 0.0591661 |
| DASS_D_10 | 60 | 240 | 1.0083333 | 0.9147406 | 1.0 | 0.9218750 | 1.4826 | 0 | 3 | 3 | 0.5389287 | -0.6158918 | 0.0590463 |
| DASS_S_11 | 61 | 240 | 1.1250000 | 0.8390321 | 1.0 | 1.0885417 | 1.4826 | 0 | 3 | 3 | 0.3134748 | -0.5622096 | 0.0541593 |
| DASS_S_12 | 62 | 240 | 1.1625000 | 0.8646050 | 1.0 | 1.1145833 | 1.4826 | 0 | 3 | 3 | 0.3408496 | -0.5680930 | 0.0558100 |
| DASS_D_13 | 63 | 240 | 1.0791667 | 0.8066041 | 1.0 | 1.0156250 | 0.0000 | 0 | 3 | 3 | 0.6189097 | 0.1347322 | 0.0520661 |
| DASS_S_14 | 64 | 240 | 0.9458333 | 0.7930024 | 1.0 | 0.8906250 | 1.4826 | 0 | 3 | 3 | 0.4968199 | -0.2896751 | 0.0511881 |
| DASS_A_15 | 65 | 240 | 0.8041667 | 0.8378989 | 1.0 | 0.7083333 | 1.4826 | 0 | 3 | 3 | 0.7605846 | -0.2091454 | 0.0540861 |
| DASS_D_16 | 66 | 240 | 0.8458333 | 0.8757444 | 1.0 | 0.7447917 | 1.4826 | 0 | 3 | 3 | 0.7485097 | -0.3000785 | 0.0565291 |
| DASS_D_17 | 67 | 240 | 0.7125000 | 0.8943395 | 0.0 | 0.5729167 | 0.0000 | 0 | 3 | 3 | 1.0460243 | 0.1071675 | 0.0577294 |
| DASS_S_18 | 68 | 240 | 0.8041667 | 0.8527480 | 1.0 | 0.7135417 | 1.4826 | 0 | 3 | 3 | 0.7049093 | -0.4719511 | 0.0550446 |
| DASS_A_19 | 69 | 240 | 0.8458333 | 0.8612918 | 1.0 | 0.7552083 | 1.4826 | 0 | 3 | 3 | 0.6904726 | -0.4039024 | 0.0555961 |
| DASS_A_20 | 70 | 240 | 0.6916667 | 0.8057932 | 0.0 | 0.5989583 | 0.0000 | 0 | 3 | 3 | 0.7532099 | -0.6284447 | 0.0520137 |
| DASS_D_21 | 71 | 240 | 0.6791667 | 0.8781300 | 0.0 | 0.5364583 | 0.0000 | 0 | 3 | 3 | 1.1095164 | 0.2796753 | 0.0566830 |
| DASS_D_sum | 72 | 240 | 6.5958333 | 4.6391980 | 6.5 | 6.1510417 | 5.1891 | 0 | 21 | 21 | 0.8009745 | 0.2402705 | 0.2994589 |
| DASS_A_sum | 73 | 240 | 5.5916667 | 3.8333394 | 5.0 | 5.3958333 | 4.4478 | 0 | 17 | 17 | 0.4005132 | -0.7342841 | 0.2474410 |
| DASS_S_sum | 74 | 240 | 7.3625000 | 4.0722875 | 7.0 | 7.2760417 | 4.4478 | 0 | 18 | 18 | 0.2077570 | -0.3786219 | 0.2628650 |
| BIS_1 | 75 | 240 | 2.2458333 | 0.8245598 | 2.0 | 2.2343750 | 1.4826 | 1 | 4 | 3 | 0.1456260 | -0.6029615 | 0.0532251 |
| BIS_2 | 76 | 240 | 2.1375000 | 0.7934420 | 2.0 | 2.1197917 | 1.4826 | 1 | 4 | 3 | 0.2528329 | -0.4645146 | 0.0512165 |
| BIS_3 | 77 | 240 | 2.0875000 | 0.8312981 | 2.0 | 2.0364583 | 1.4826 | 1 | 4 | 3 | 0.4459447 | -0.3312258 | 0.0536601 |
| BIS_4 | 78 | 240 | 2.1500000 | 0.7560080 | 2.0 | 2.1197917 | 0.0000 | 1 | 4 | 3 | 0.4987330 | 0.1684318 | 0.0488001 |
| BIS_5 | 79 | 240 | 2.6000000 | 0.7642646 | 3.0 | 2.5885417 | 1.4826 | 1 | 4 | 3 | -0.1411267 | -0.3419178 | 0.0493331 |
| BIS_6 | 80 | 240 | 2.1333333 | 0.7424187 | 2.0 | 2.1250000 | 0.0000 | 1 | 4 | 3 | 0.2722497 | -0.2074721 | 0.0479229 |
| BIS_7 | 81 | 240 | 2.0541667 | 0.8138339 | 2.0 | 2.0156250 | 1.4826 | 1 | 4 | 3 | 0.3651574 | -0.4736800 | 0.0525328 |
| BIS_8 | 82 | 240 | 2.3291667 | 0.7948470 | 2.0 | 2.3177083 | 1.4826 | 1 | 4 | 3 | 0.2433764 | -0.3547932 | 0.0513072 |
| BIS_sum | 83 | 240 | 17.7375000 | 3.7564241 | 18.0 | 17.6510417 | 4.4478 | 8 | 31 | 23 | 0.2550941 | 0.2700315 | 0.2424761 |
| SSEIT_1 | 84 | 240 | 3.7541667 | 0.8593464 | 4.0 | 3.8333333 | 0.0000 | 1 | 5 | 4 | -0.8881397 | 0.8898084 | 0.0554706 |
| SSEIT_2 | 85 | 240 | 3.7833333 | 0.8302173 | 4.0 | 3.8541667 | 0.0000 | 1 | 5 | 4 | -0.9792154 | 1.3196187 | 0.0535903 |
| SSEIT_3 | 86 | 240 | 3.6208333 | 0.9340062 | 4.0 | 3.6718750 | 1.4826 | 1 | 5 | 4 | -0.5970465 | -0.1030607 | 0.0602898 |
| SSEIT_4 | 87 | 240 | 3.8250000 | 0.9343514 | 4.0 | 3.9166667 | 1.4826 | 1 | 5 | 4 | -0.6904725 | 0.2081065 | 0.0603121 |
| SSEIT_5 | 88 | 240 | 3.6000000 | 1.0969719 | 4.0 | 3.6822917 | 1.4826 | 1 | 5 | 4 | -0.6802834 | -0.3443269 | 0.0708092 |
| SSEIT_6 | 89 | 240 | 3.8791667 | 0.8809842 | 4.0 | 3.9739583 | 0.0000 | 1 | 5 | 4 | -0.7881307 | 0.4182796 | 0.0568673 |
| SSEIT_7 | 90 | 240 | 3.7125000 | 0.8706332 | 4.0 | 3.7604167 | 1.4826 | 1 | 5 | 4 | -0.5116870 | 0.1640800 | 0.0561991 |
| SSEIT_8 | 91 | 240 | 3.7250000 | 0.9806709 | 4.0 | 3.8177083 | 1.4826 | 1 | 5 | 4 | -0.7041408 | 0.2031156 | 0.0633020 |
| SSEIT_9 | 92 | 240 | 3.9500000 | 0.8268507 | 4.0 | 4.0416667 | 0.0000 | 1 | 5 | 4 | -1.0573977 | 1.6073330 | 0.0533730 |
| SSEIT_10 | 93 | 240 | 3.6208333 | 0.9777778 | 4.0 | 3.6875000 | 1.4826 | 1 | 5 | 4 | -0.6596889 | 0.0290126 | 0.0631153 |
| SSEIT_11 | 94 | 240 | 3.3708333 | 1.1091024 | 3.5 | 3.4010417 | 0.7413 | 1 | 5 | 4 | -0.2880241 | -0.7426565 | 0.0715923 |
| SSEIT_12 | 95 | 240 | 3.3833333 | 0.9478744 | 3.0 | 3.3750000 | 1.4826 | 1 | 5 | 4 | -0.2371045 | -0.5957027 | 0.0611850 |
| SSEIT_13 | 96 | 240 | 3.5750000 | 0.9388188 | 4.0 | 3.6302083 | 1.4826 | 1 | 5 | 4 | -0.5790710 | 0.1666124 | 0.0606005 |
| SSEIT_14 | 97 | 240 | 3.9583333 | 0.8811920 | 4.0 | 4.0625000 | 0.0000 | 1 | 5 | 4 | -1.0886983 | 1.5489357 | 0.0568807 |
| SSEIT_15 | 98 | 240 | 3.6458333 | 0.9482330 | 4.0 | 3.7187500 | 0.7413 | 1 | 5 | 4 | -0.7428813 | 0.3197924 | 0.0612082 |
| SSEIT_16 | 99 | 240 | 3.8291667 | 0.8383981 | 4.0 | 3.8958333 | 0.0000 | 1 | 5 | 4 | -0.7332296 | 0.5630996 | 0.0541184 |
| SSEIT_17 | 100 | 240 | 3.8500000 | 0.8349079 | 4.0 | 3.9114583 | 0.0000 | 1 | 5 | 4 | -0.7891027 | 0.9038198 | 0.0538931 |
| SSEIT_18 | 101 | 240 | 4.0000000 | 0.8182029 | 4.0 | 4.0729167 | 0.0000 | 1 | 5 | 4 | -1.0041060 | 1.8344596 | 0.0528148 |
| SSEIT_19 | 102 | 240 | 3.4958333 | 0.9681018 | 4.0 | 3.5208333 | 1.4826 | 1 | 5 | 4 | -0.4015926 | -0.4217825 | 0.0624907 |
| SSEIT_20 | 103 | 240 | 3.8333333 | 0.7851481 | 4.0 | 3.8802083 | 0.0000 | 1 | 5 | 4 | -0.6819949 | 0.6011769 | 0.0506811 |
| SSEIT_21 | 104 | 240 | 3.3541667 | 0.9700447 | 4.0 | 3.3854167 | 1.4826 | 1 | 5 | 4 | -0.4501433 | -0.3193233 | 0.0626161 |
| SSEIT_22 | 105 | 240 | 3.8125000 | 0.8041797 | 4.0 | 3.8697917 | 0.0000 | 1 | 5 | 4 | -0.9011383 | 1.2813923 | 0.0519096 |
| SSEIT_23 | 106 | 240 | 3.7375000 | 0.9604360 | 4.0 | 3.8385417 | 0.0000 | 1 | 5 | 4 | -0.9267194 | 0.6913098 | 0.0619959 |
| SSEIT_24 | 107 | 240 | 4.1208333 | 0.8014000 | 4.0 | 4.2031250 | 1.4826 | 1 | 5 | 4 | -0.8019806 | 0.6303605 | 0.0517301 |
| SSEIT_25 | 108 | 240 | 3.7541667 | 0.9021032 | 4.0 | 3.8489583 | 0.0000 | 1 | 5 | 4 | -0.9307807 | 0.9281106 | 0.0582305 |
| SSEIT_26 | 109 | 240 | 3.4333333 | 1.0040365 | 4.0 | 3.4427083 | 1.4826 | 1 | 5 | 4 | -0.2994517 | -0.6868561 | 0.0648103 |
| SSEIT_27 | 110 | 240 | 3.4333333 | 0.8652601 | 4.0 | 3.4687500 | 1.4826 | 1 | 5 | 4 | -0.4125653 | -0.1074251 | 0.0558523 |
| SSEIT_28 | 111 | 240 | 3.5875000 | 0.9982464 | 4.0 | 3.6250000 | 1.4826 | 1 | 5 | 4 | -0.3913403 | -0.6554922 | 0.0644365 |
| SSEIT_29 | 112 | 240 | 3.5541667 | 0.9446963 | 4.0 | 3.5989583 | 1.4826 | 1 | 5 | 4 | -0.5853861 | -0.0395016 | 0.0609799 |
| SSEIT_30 | 113 | 240 | 4.0208333 | 0.8050464 | 4.0 | 4.1041667 | 0.0000 | 1 | 5 | 4 | -0.8519652 | 0.8834906 | 0.0519655 |
| SSEIT_31 | 114 | 240 | 3.7875000 | 0.8633943 | 4.0 | 3.8645833 | 0.0000 | 1 | 5 | 4 | -0.8617859 | 0.8921882 | 0.0557319 |
| SSEIT_32 | 115 | 240 | 3.8750000 | 0.8538615 | 4.0 | 3.9479167 | 0.0000 | 1 | 5 | 4 | -0.7241083 | 0.4765148 | 0.0551165 |
| SSEIT_33 | 116 | 240 | 3.5041667 | 1.0628686 | 4.0 | 3.5468750 | 1.4826 | 1 | 5 | 4 | -0.5001373 | -0.6023394 | 0.0686079 |
| SSEIT_sum | 117 | 240 | 122.3875000 | 16.4872161 | 124.0 | 123.3229167 | 12.6021 | 45 | 160 | 115 | -0.7864116 | 1.8855300 | 1.0642452 |
| RSES_1 | 118 | 240 | 1.8625000 | 0.7216395 | 2.0 | 1.7968750 | 0.0000 | 1 | 4 | 3 | 0.6094128 | 0.3344134 | 0.0465816 |
| RSES_2 | 119 | 240 | 1.8791667 | 0.6830152 | 2.0 | 1.8229167 | 0.0000 | 1 | 4 | 3 | 0.5471371 | 0.5496631 | 0.0440884 |
| RSES_3 | 120 | 240 | 2.0875000 | 0.7952855 | 2.0 | 2.0416667 | 0.0000 | 1 | 4 | 3 | 0.4900505 | -0.0696325 | 0.0513355 |
| RSES_4 | 121 | 240 | 2.0291667 | 0.6684643 | 2.0 | 2.0104167 | 0.0000 | 1 | 4 | 3 | 0.3856914 | 0.4062236 | 0.0431492 |
| RSES_5 | 122 | 240 | 2.1708333 | 0.8078999 | 2.0 | 2.1510417 | 1.4826 | 1 | 4 | 3 | 0.2517108 | -0.4794614 | 0.0521497 |
| RSES_6 | 123 | 240 | 2.0916667 | 0.7374709 | 2.0 | 2.0781250 | 0.0000 | 1 | 4 | 3 | 0.2916032 | -0.1948101 | 0.0476035 |
| RSES_7 | 124 | 240 | 2.2416667 | 0.7761706 | 2.0 | 2.2395833 | 1.4826 | 1 | 4 | 3 | 0.1980403 | -0.3681761 | 0.0501016 |
| RSES_8 | 125 | 240 | 2.8250000 | 0.7336788 | 3.0 | 2.8333333 | 0.0000 | 1 | 4 | 3 | -0.3499848 | 0.0042358 | 0.0473588 |
| RSES_9 | 126 | 240 | 2.6625000 | 0.7753053 | 3.0 | 2.6770833 | 0.0000 | 1 | 4 | 3 | -0.3604462 | -0.1945849 | 0.0500457 |
| RSES_10 | 127 | 240 | 2.5000000 | 0.8283674 | 3.0 | 2.5156250 | 1.4826 | 1 | 4 | 3 | -0.2199080 | -0.5665100 | 0.0534709 |
| RSES_sum | 128 | 240 | 22.3500000 | 4.9620316 | 23.0 | 22.4739583 | 4.4478 | 10 | 40 | 30 | -0.1077398 | 0.3600781 | 0.3202978 |
| NEO_N_1 | 129 | 240 | 3.8666667 | 1.0139886 | 4.0 | 4.0104167 | 0.0000 | 1 | 5 | 4 | -1.1235922 | 0.9799712 | 0.0654527 |
| NEO_N_2 | 130 | 240 | 3.5541667 | 1.1594542 | 4.0 | 3.6250000 | 1.4826 | 1 | 5 | 4 | -0.4758223 | -0.7707457 | 0.0748424 |
| NEO_N_3 | 131 | 240 | 2.8416667 | 1.1961992 | 3.0 | 2.8177083 | 1.4826 | 1 | 5 | 4 | 0.1299800 | -1.0092634 | 0.0772143 |
| NEO_N_4 | 132 | 240 | 2.4666667 | 1.1816826 | 2.0 | 2.3802083 | 1.4826 | 1 | 5 | 4 | 0.5110443 | -0.6819719 | 0.0762773 |
| NEO_N_5 | 133 | 240 | 3.1500000 | 1.1177533 | 3.0 | 3.1406250 | 1.4826 | 1 | 5 | 4 | -0.1169002 | -0.9524097 | 0.0721507 |
| NEO_N_6 | 134 | 240 | 2.7583333 | 1.1714586 | 3.0 | 2.7239583 | 1.4826 | 1 | 5 | 4 | 0.2570463 | -0.9067204 | 0.0756173 |
| NEO_N_7 | 135 | 240 | 2.8625000 | 1.1865045 | 3.0 | 2.8541667 | 1.4826 | 1 | 5 | 4 | 0.0559131 | -1.0257078 | 0.0765885 |
| NEO_N_8 | 136 | 240 | 2.7875000 | 1.1610169 | 3.0 | 2.7968750 | 1.4826 | 1 | 5 | 4 | -0.0141598 | -1.0446122 | 0.0749433 |
| NEO_N_9 | 137 | 240 | 3.2458333 | 0.9691817 | 3.0 | 3.2031250 | 1.4826 | 1 | 5 | 4 | 0.1000913 | -0.6613634 | 0.0625604 |
| NEO_N_10 | 138 | 240 | 2.9083333 | 1.0589000 | 3.0 | 2.8750000 | 1.4826 | 1 | 5 | 4 | 0.2241768 | -0.8793075 | 0.0683517 |
| NEO_N_11 | 139 | 240 | 2.6750000 | 1.0443111 | 2.0 | 2.6822917 | 1.4826 | 1 | 5 | 4 | 0.3013570 | -0.9077998 | 0.0674100 |
| NEO_N_12 | 140 | 240 | 2.6750000 | 1.0362670 | 2.5 | 2.6770833 | 0.7413 | 1 | 5 | 4 | 0.2713607 | -0.7637941 | 0.0668907 |
| NEO_E_13 | 141 | 240 | 3.6250000 | 1.0672963 | 4.0 | 3.7135417 | 1.4826 | 1 | 5 | 4 | -0.6959267 | -0.1262620 | 0.0688937 |
| NEO_E_14 | 142 | 240 | 3.7875000 | 0.8919972 | 4.0 | 3.8697917 | 0.0000 | 1 | 5 | 4 | -0.6661520 | 0.1100950 | 0.0575782 |
| NEO_E_15 | 143 | 240 | 3.4583333 | 1.2701651 | 4.0 | 3.5677083 | 1.4826 | 1 | 5 | 4 | -0.4621452 | -0.8547105 | 0.0819888 |
| NEO_E_16 | 144 | 240 | 3.2708333 | 1.1632371 | 3.0 | 3.3177083 | 1.4826 | 1 | 5 | 4 | -0.3137624 | -0.7839538 | 0.0750866 |
| NEO_E_17 | 145 | 240 | 3.3708333 | 0.9462452 | 3.0 | 3.3541667 | 1.4826 | 1 | 5 | 4 | -0.1146328 | -0.6691973 | 0.0610799 |
| NEO_E_18 | 146 | 240 | 3.3916667 | 0.9835112 | 4.0 | 3.3958333 | 1.4826 | 1 | 5 | 4 | -0.2893285 | -0.5549224 | 0.0634854 |
| NEO_E_19 | 147 | 240 | 3.2791667 | 1.0355518 | 3.0 | 3.2656250 | 1.4826 | 1 | 5 | 4 | -0.2133789 | -0.8216811 | 0.0668446 |
| NEO_E_20 | 148 | 240 | 3.1375000 | 1.0677127 | 3.0 | 3.0989583 | 1.4826 | 1 | 5 | 4 | 0.0341812 | -0.8823526 | 0.0689206 |
| NEO_E_21 | 149 | 240 | 3.9708333 | 0.8887861 | 4.0 | 4.0885417 | 0.0000 | 1 | 5 | 4 | -1.0829265 | 1.3185158 | 0.0573709 |
| NEO_E_22 | 150 | 240 | 3.8208333 | 1.0335296 | 4.0 | 3.9375000 | 1.4826 | 1 | 5 | 4 | -0.8393657 | 0.1338900 | 0.0667140 |
| NEO_E_23 | 151 | 240 | 3.9166667 | 0.9859544 | 4.0 | 4.0468750 | 1.4826 | 1 | 5 | 4 | -0.8771859 | 0.3379919 | 0.0636431 |
| NEO_E_24 | 152 | 240 | 3.7541667 | 0.9905322 | 4.0 | 3.8437500 | 1.4826 | 1 | 5 | 4 | -0.6795086 | -0.0600569 | 0.0639386 |
| NEO_O_25 | 153 | 240 | 3.8666667 | 0.9973465 | 4.0 | 3.9791667 | 1.4826 | 1 | 5 | 4 | -0.6902206 | -0.1325231 | 0.0643784 |
| NEO_O_26 | 154 | 240 | 3.9041667 | 1.0405901 | 4.0 | 4.0364583 | 1.4826 | 1 | 5 | 4 | -0.8075556 | 0.0099263 | 0.0671698 |
| NEO_O_27 | 155 | 240 | 3.7166667 | 1.1548213 | 4.0 | 3.8333333 | 1.4826 | 1 | 5 | 4 | -0.6699250 | -0.4175729 | 0.0745434 |
| NEO_O_28 | 156 | 240 | 3.9000000 | 1.2094468 | 4.0 | 4.0572917 | 1.4826 | 1 | 5 | 4 | -0.8122626 | -0.4508389 | 0.0780695 |
| NEO_O_29 | 157 | 240 | 3.4000000 | 1.1007795 | 4.0 | 3.4270833 | 1.4826 | 1 | 5 | 4 | -0.3276276 | -0.7988374 | 0.0710550 |
| NEO_O_30 | 158 | 240 | 3.5291667 | 1.0781121 | 4.0 | 3.5781250 | 1.4826 | 1 | 5 | 4 | -0.4241525 | -0.6392063 | 0.0695918 |
| NEO_O_31 | 159 | 240 | 2.4541667 | 0.9495558 | 2.0 | 2.4062500 | 1.4826 | 1 | 5 | 4 | 0.6273255 | 0.0439058 | 0.0612936 |
| NEO_O_32 | 160 | 240 | 2.9833333 | 1.1014128 | 3.0 | 2.9322917 | 1.4826 | 1 | 5 | 4 | 0.2011329 | -0.9789600 | 0.0710959 |
| NEO_O_33 | 161 | 240 | 3.7208333 | 1.1357556 | 4.0 | 3.8333333 | 1.4826 | 1 | 5 | 4 | -0.6699984 | -0.3799326 | 0.0733127 |
| NEO_O_34 | 162 | 240 | 3.7000000 | 1.0754001 | 4.0 | 3.7812500 | 1.4826 | 1 | 5 | 4 | -0.5117858 | -0.5716129 | 0.0694168 |
| NEO_O_35 | 163 | 240 | 3.1875000 | 1.1362467 | 3.0 | 3.2343750 | 1.4826 | 1 | 5 | 4 | -0.3014980 | -0.6121613 | 0.0733444 |
| NEO_O_36 | 164 | 240 | 3.5833333 | 1.4176611 | 4.0 | 3.7291667 | 1.4826 | 1 | 5 | 4 | -0.5150961 | -1.0777893 | 0.0915096 |
| NEO_A_37 | 165 | 240 | 3.5291667 | 0.9974776 | 4.0 | 3.5833333 | 0.0000 | 1 | 5 | 4 | -0.7727624 | -0.0694604 | 0.0643869 |
| NEO_A_38 | 166 | 240 | 3.6458333 | 0.9304155 | 4.0 | 3.7187500 | 0.0000 | 1 | 5 | 4 | -0.7993154 | 0.4840701 | 0.0600581 |
| NEO_A_39 | 167 | 240 | 3.8291667 | 1.0746135 | 4.0 | 3.9375000 | 1.4826 | 1 | 5 | 4 | -0.6458574 | -0.4659596 | 0.0693660 |
| NEO_A_40 | 168 | 240 | 3.9916667 | 1.0469788 | 4.0 | 4.1302083 | 1.4826 | 1 | 5 | 4 | -0.8112524 | -0.2867509 | 0.0675822 |
| NEO_A_41 | 169 | 240 | 4.1083333 | 0.8658643 | 4.0 | 4.2135417 | 1.4826 | 1 | 5 | 4 | -0.8635544 | 0.3776972 | 0.0558913 |
| NEO_A_42 | 170 | 240 | 4.1625000 | 0.8499631 | 4.0 | 4.2760417 | 1.4826 | 1 | 5 | 4 | -1.0463594 | 1.1171112 | 0.0548649 |
| NEO_A_43 | 171 | 240 | 3.8416667 | 1.0823485 | 4.0 | 3.9531250 | 1.4826 | 1 | 5 | 4 | -0.7099900 | -0.4165580 | 0.0698653 |
| NEO_A_44 | 172 | 240 | 3.6250000 | 1.1282786 | 4.0 | 3.6770833 | 1.4826 | 1 | 5 | 4 | -0.3279883 | -1.0486458 | 0.0728301 |
| NEO_A_45 | 173 | 240 | 3.6666667 | 1.1191250 | 4.0 | 3.7343750 | 1.4826 | 1 | 5 | 4 | -0.3739012 | -0.9180642 | 0.0722392 |
| NEO_A_46 | 174 | 240 | 2.8375000 | 1.1510637 | 3.0 | 2.7864583 | 1.4826 | 1 | 5 | 4 | 0.3508045 | -0.8445593 | 0.0743008 |
| NEO_A_47 | 175 | 240 | 3.8416667 | 0.9978708 | 4.0 | 3.9583333 | 1.4826 | 1 | 5 | 4 | -0.8890219 | 0.3357461 | 0.0644123 |
| NEO_A_48 | 176 | 240 | 4.0958333 | 0.9391437 | 4.0 | 4.2343750 | 1.4826 | 1 | 5 | 4 | -1.0353082 | 0.6864888 | 0.0606215 |
| NEO_C_49 | 177 | 240 | 3.5083333 | 0.8677951 | 4.0 | 3.5364583 | 1.4826 | 1 | 5 | 4 | -0.4463068 | -0.1400536 | 0.0560159 |
| NEO_C_50 | 178 | 240 | 3.7083333 | 0.8619898 | 4.0 | 3.7760417 | 0.0000 | 1 | 5 | 4 | -0.6938774 | 0.3900470 | 0.0556412 |
| NEO_C_51 | 179 | 240 | 3.5208333 | 1.1051341 | 4.0 | 3.5781250 | 1.4826 | 1 | 5 | 4 | -0.5155241 | -0.6307845 | 0.0713361 |
| NEO_C_52 | 180 | 240 | 3.0250000 | 1.2406133 | 3.0 | 3.0312500 | 1.4826 | 1 | 5 | 4 | -0.0209320 | -1.1292615 | 0.0800812 |
| NEO_C_53 | 181 | 240 | 3.9541667 | 0.7987853 | 4.0 | 4.0260417 | 0.0000 | 1 | 5 | 4 | -0.8012413 | 0.8185973 | 0.0515614 |
| NEO_C_54 | 182 | 240 | 2.0291667 | 0.8934814 | 2.0 | 1.9479167 | 1.4826 | 1 | 5 | 4 | 0.6092761 | -0.1895782 | 0.0576740 |
| NEO_C_55 | 183 | 240 | 3.8583333 | 0.9129091 | 4.0 | 3.9479167 | 1.4826 | 1 | 5 | 4 | -0.8360742 | 0.7278260 | 0.0589280 |
| NEO_C_56 | 184 | 240 | 4.0416667 | 0.9138253 | 4.0 | 4.1666667 | 1.4826 | 2 | 5 | 3 | -0.8024425 | -0.0936861 | 0.0589872 |
| NEO_C_57 | 185 | 240 | 3.8000000 | 0.8783781 | 4.0 | 3.8750000 | 0.0000 | 1 | 5 | 4 | -0.6359643 | 0.1539995 | 0.0566991 |
| NEO_C_58 | 186 | 240 | 2.5041667 | 1.0862315 | 2.0 | 2.4583333 | 1.4826 | 1 | 5 | 4 | 0.5062675 | -0.6553987 | 0.0701159 |
| NEO_C_59 | 187 | 240 | 3.1375000 | 1.0948004 | 3.0 | 3.1302083 | 1.4826 | 1 | 5 | 4 | -0.1199934 | -0.7980528 | 0.0706691 |
| NEO_C_60 | 188 | 240 | 3.2666667 | 1.1332431 | 3.0 | 3.2812500 | 1.4826 | 1 | 5 | 4 | -0.2407709 | -0.9245075 | 0.0731505 |
| NEO_N_sum | 189 | 240 | 35.7916667 | 7.2934840 | 36.0 | 35.8072917 | 7.4130 | 16 | 57 | 41 | -0.0001701 | 0.0508250 | 0.4707924 |
| NEO_E_sum | 190 | 240 | 42.7833333 | 7.5258921 | 43.0 | 42.8958333 | 7.4130 | 17 | 60 | 43 | -0.2477822 | 0.0345163 | 0.4857942 |
| NEO_O_sum | 191 | 240 | 41.9458333 | 6.4237712 | 42.0 | 41.8802083 | 7.4130 | 26 | 60 | 34 | 0.1009092 | -0.4695492 | 0.4146526 |
| NEO_A_sum | 192 | 240 | 45.1750000 | 6.8671341 | 45.0 | 45.2656250 | 7.4130 | 28 | 60 | 32 | -0.1262515 | -0.8654505 | 0.4432716 |
| NEO_C_sum | 193 | 240 | 40.3541667 | 5.8841308 | 40.0 | 40.3489583 | 5.9304 | 25 | 55 | 30 | -0.0071121 | -0.3502920 | 0.3798190 |
## MTURK
kable(mturk_cont_ques, caption = "Descriptive statistics for questionnaires - STUDY 1 - MTURK", align = rep('crcc'), row.names = TRUE) %>%
kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
kable_classic() | vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NMPQ_1 | 1 | 505 | 4.564356 | 1.7481047 | 5 | 4.6592593 | 1.4826 | 1 | 7 | 6 | -0.5053812 | -0.7564556 | 0.0777896 |
| NMPQ_2 | 2 | 505 | 4.946535 | 1.6254160 | 5 | 5.1135802 | 1.4826 | 1 | 7 | 6 | -0.8352502 | -0.0453865 | 0.0723301 |
| NMPQ_3 | 3 | 505 | 4.400000 | 1.8165902 | 5 | 4.4913580 | 1.4826 | 1 | 7 | 6 | -0.4208833 | -0.8897788 | 0.0808372 |
| NMPQ_4 | 4 | 505 | 4.988119 | 1.6317343 | 5 | 5.1753086 | 1.4826 | 1 | 7 | 6 | -0.9134702 | 0.1266639 | 0.0726112 |
| NMPQ_5 | 5 | 505 | 4.300990 | 1.8464087 | 5 | 4.3827160 | 1.4826 | 1 | 7 | 6 | -0.4181650 | -0.9919509 | 0.0821641 |
| NMPQ_6 | 6 | 505 | 4.019802 | 1.9240551 | 4 | 4.0246914 | 2.9652 | 1 | 7 | 6 | -0.1097694 | -1.2024132 | 0.0856193 |
| NMPQ_7 | 7 | 505 | 4.443564 | 1.7731695 | 5 | 4.5358025 | 1.4826 | 1 | 7 | 6 | -0.4591665 | -0.8030854 | 0.0789050 |
| NMPQ_8 | 8 | 505 | 4.594059 | 1.7982477 | 5 | 4.6987654 | 1.4826 | 1 | 7 | 6 | -0.5041377 | -0.8548189 | 0.0800210 |
| NMPQ_9 | 9 | 505 | 4.988119 | 1.6047602 | 5 | 5.1679012 | 1.4826 | 1 | 7 | 6 | -0.8863148 | 0.1163296 | 0.0714109 |
| NMPQ_10 | 10 | 505 | 4.570297 | 1.7860540 | 5 | 4.6839506 | 1.4826 | 1 | 7 | 6 | -0.5110330 | -0.7903171 | 0.0794784 |
| NMPQ_11 | 11 | 505 | 4.839604 | 1.7066676 | 5 | 4.9950617 | 1.4826 | 1 | 7 | 6 | -0.7060817 | -0.4071257 | 0.0759457 |
| NMPQ_12 | 12 | 505 | 4.732673 | 1.7176205 | 5 | 4.8691358 | 1.4826 | 1 | 7 | 6 | -0.6451227 | -0.5006782 | 0.0764331 |
| NMPQ_13 | 13 | 505 | 4.762376 | 1.7351897 | 5 | 4.9012346 | 1.4826 | 1 | 7 | 6 | -0.6403446 | -0.5490660 | 0.0772149 |
| NMPQ_14 | 14 | 505 | 4.873267 | 1.7251001 | 5 | 5.0296296 | 1.4826 | 1 | 7 | 6 | -0.7323623 | -0.4661023 | 0.0767659 |
| NMPQ_15 | 15 | 505 | 4.465346 | 1.7647584 | 5 | 4.5604938 | 1.4826 | 1 | 7 | 6 | -0.5134764 | -0.7900441 | 0.0785307 |
| NMPQ_16 | 16 | 505 | 3.853465 | 1.9523879 | 4 | 3.8345679 | 2.9652 | 1 | 7 | 6 | -0.0643546 | -1.2606793 | 0.0868801 |
| NMPQ_17 | 17 | 505 | 4.039604 | 1.9826669 | 4 | 4.0543210 | 2.9652 | 1 | 7 | 6 | -0.2056528 | -1.2818544 | 0.0882275 |
| NMPQ_18 | 18 | 505 | 4.138614 | 1.9056604 | 4 | 4.1728395 | 2.9652 | 1 | 7 | 6 | -0.2376239 | -1.1313384 | 0.0848008 |
| NMPQ_19 | 19 | 505 | 4.388119 | 1.8246160 | 5 | 4.4839506 | 1.4826 | 1 | 7 | 6 | -0.4515612 | -0.9053164 | 0.0811943 |
| NMPQ_20 | 20 | 505 | 4.136634 | 1.9362725 | 5 | 4.1703704 | 1.4826 | 1 | 7 | 6 | -0.2582731 | -1.1814585 | 0.0861630 |
| NMPQ_sum | 21 | 505 | 90.045545 | 27.1725802 | 95 | 92.3456790 | 26.6868 | 20 | 140 | 120 | -0.6690097 | -0.2654139 | 1.2091639 |
| SRS_1 | 22 | 505 | 3.243564 | 0.7677032 | 3 | 3.3333333 | 1.4826 | 1 | 4 | 3 | -0.7586386 | 0.0349536 | 0.0341623 |
| SRS_2 | 23 | 505 | 3.079208 | 0.8669776 | 3 | 3.1580247 | 1.4826 | 1 | 4 | 3 | -0.5903255 | -0.4860296 | 0.0385800 |
| SRS_3 | 24 | 505 | 3.112871 | 0.7824543 | 3 | 3.1728395 | 1.4826 | 1 | 4 | 3 | -0.5217096 | -0.3357595 | 0.0348188 |
| SRS_4 | 25 | 505 | 3.178218 | 0.7867459 | 3 | 3.2641975 | 1.4826 | 1 | 4 | 3 | -0.7386502 | 0.1090677 | 0.0350097 |
| SRS_5 | 26 | 505 | 2.540594 | 0.9974350 | 3 | 2.5506173 | 1.4826 | 1 | 4 | 3 | -0.0877478 | -1.0518842 | 0.0443853 |
| SRS_6 | 27 | 505 | 3.138614 | 0.7871702 | 3 | 3.2049383 | 1.4826 | 1 | 4 | 3 | -0.5651665 | -0.3233032 | 0.0350286 |
| SRS_7 | 28 | 505 | 2.483168 | 0.9657908 | 2 | 2.4790123 | 1.4826 | 1 | 4 | 3 | 0.0079474 | -0.9720493 | 0.0429771 |
| SRS_8 | 29 | 505 | 3.023762 | 0.8748184 | 3 | 3.0962963 | 1.4826 | 1 | 4 | 3 | -0.5250024 | -0.5469121 | 0.0389289 |
| SRS_9 | 30 | 505 | 2.556436 | 1.0026172 | 3 | 2.5703704 | 1.4826 | 1 | 4 | 3 | 0.0044330 | -1.0867102 | 0.0446159 |
| SRS_10 | 31 | 505 | 3.079208 | 0.8028121 | 3 | 3.1432099 | 1.4826 | 1 | 4 | 3 | -0.5566046 | -0.2454258 | 0.0357247 |
| SRS_sum | 32 | 505 | 29.435644 | 5.2990263 | 29 | 29.2790123 | 4.4478 | 14 | 40 | 26 | 0.2270166 | -0.3040975 | 0.2358036 |
| SAD_dep_1 | 33 | 505 | 4.465346 | 1.8277206 | 5 | 4.5679012 | 1.4826 | 1 | 7 | 6 | -0.4574013 | -0.9176665 | 0.0813325 |
| SAD_dep_2 | 34 | 505 | 4.580198 | 1.8110124 | 5 | 4.7160494 | 1.4826 | 1 | 7 | 6 | -0.5983697 | -0.6715725 | 0.0805890 |
| SAD_dep_3 | 35 | 505 | 4.219802 | 1.9254983 | 5 | 4.2740741 | 1.4826 | 1 | 7 | 6 | -0.2858511 | -1.1226121 | 0.0856835 |
| SAD_4 | 36 | 505 | 4.800000 | 1.7754946 | 5 | 4.9728395 | 1.4826 | 1 | 7 | 6 | -0.7202116 | -0.4306089 | 0.0790085 |
| SAD_access_5 | 37 | 505 | 4.576238 | 1.7521613 | 5 | 4.6962963 | 1.4826 | 1 | 7 | 6 | -0.5773884 | -0.6234456 | 0.0779702 |
| SAD_access_6 | 38 | 505 | 4.685149 | 1.7095313 | 5 | 4.8074074 | 1.4826 | 1 | 7 | 6 | -0.6182911 | -0.5578879 | 0.0760731 |
| SAD_access_7 | 39 | 505 | 4.902970 | 1.6703801 | 5 | 5.0716049 | 1.4826 | 1 | 7 | 6 | -0.8001256 | -0.1734047 | 0.0743309 |
| SAD_ea_8 | 40 | 505 | 3.825743 | 2.0092373 | 4 | 3.7827160 | 2.9652 | 1 | 7 | 6 | 0.0220541 | -1.3186785 | 0.0894099 |
| SAD_ea_9 | 41 | 505 | 4.633663 | 1.6968854 | 5 | 4.7506173 | 1.4826 | 1 | 7 | 6 | -0.5857290 | -0.4837387 | 0.0755104 |
| SAD_ea_10 | 42 | 505 | 4.554455 | 1.6919232 | 5 | 4.6518519 | 1.4826 | 1 | 7 | 6 | -0.5314689 | -0.5622305 | 0.0752896 |
| SAD_ea_11 | 43 | 505 | 4.857426 | 1.5037880 | 5 | 4.9777778 | 1.4826 | 1 | 7 | 6 | -0.6865616 | 0.1142011 | 0.0669177 |
| SAD_dist_12 | 44 | 505 | 3.988119 | 1.9241205 | 4 | 3.9851852 | 2.9652 | 1 | 7 | 6 | -0.0849200 | -1.2296710 | 0.0856222 |
| SAD_dist_13 | 45 | 505 | 4.459406 | 1.7869777 | 5 | 4.5604938 | 1.4826 | 1 | 7 | 6 | -0.4646885 | -0.7900502 | 0.0795195 |
| SAD_sum | 46 | 505 | 58.548515 | 17.3201106 | 62 | 59.8222222 | 16.3086 | 13 | 91 | 78 | -0.5905283 | -0.4150254 | 0.7707348 |
| SAD_dep_sum | 47 | 505 | 13.265346 | 4.9216971 | 14 | 13.6518519 | 5.9304 | 3 | 21 | 18 | -0.5848943 | -0.6365966 | 0.2190126 |
| SAD_access_sum | 48 | 505 | 14.164356 | 4.4103410 | 15 | 14.6000000 | 4.4478 | 3 | 21 | 18 | -0.7854445 | 0.0064499 | 0.1962576 |
| SAD_ea_sum | 49 | 505 | 17.871287 | 5.6485413 | 19 | 18.2469136 | 5.9304 | 4 | 28 | 24 | -0.5069485 | -0.4373834 | 0.2513568 |
| SAD_dist_sum | 50 | 505 | 8.447525 | 3.3371583 | 9 | 8.6123457 | 2.9652 | 2 | 14 | 12 | -0.3695057 | -0.8118146 | 0.1485016 |
| DASS_S_1 | 51 | 505 | 1.245545 | 1.0015390 | 1 | 1.1827160 | 1.4826 | 0 | 3 | 3 | 0.2525289 | -1.0448443 | 0.0445679 |
| DASS_A_2 | 52 | 505 | 1.104950 | 1.0703782 | 1 | 1.0074074 | 1.4826 | 0 | 3 | 3 | 0.4595636 | -1.1093384 | 0.0476312 |
| DASS_D_3 | 53 | 505 | 1.035644 | 1.0758518 | 1 | 0.9209877 | 1.4826 | 0 | 3 | 3 | 0.5304872 | -1.0962444 | 0.0478748 |
| DASS_A_4 | 54 | 505 | 1.013861 | 1.0717355 | 1 | 0.8938272 | 1.4826 | 0 | 3 | 3 | 0.5226723 | -1.1323773 | 0.0476916 |
| DASS_D_5 | 55 | 505 | 1.241584 | 1.0933844 | 1 | 1.1777778 | 1.4826 | 0 | 3 | 3 | 0.3032826 | -1.2406066 | 0.0486550 |
| DASS_S_6 | 56 | 505 | 1.154455 | 1.0596799 | 1 | 1.0691358 | 1.4826 | 0 | 3 | 3 | 0.2993682 | -1.2449872 | 0.0471551 |
| DASS_A_7 | 57 | 505 | 1.029703 | 1.0769542 | 1 | 0.9135802 | 1.4826 | 0 | 3 | 3 | 0.5309201 | -1.1067861 | 0.0479238 |
| DASS_S_8 | 58 | 505 | 1.069307 | 1.0489737 | 1 | 0.9629630 | 1.4826 | 0 | 3 | 3 | 0.4795507 | -1.0643653 | 0.0466787 |
| DASS_A_9 | 59 | 505 | 1.041584 | 1.0802398 | 1 | 0.9283951 | 1.4826 | 0 | 3 | 3 | 0.5303249 | -1.0997192 | 0.0480700 |
| DASS_D_10 | 60 | 505 | 1.087129 | 1.0966818 | 1 | 0.9851852 | 1.4826 | 0 | 3 | 3 | 0.4762453 | -1.1713573 | 0.0488017 |
| DASS_S_11 | 61 | 505 | 1.225743 | 1.0711451 | 1 | 1.1580247 | 1.4826 | 0 | 3 | 3 | 0.3360643 | -1.1625126 | 0.0476653 |
| DASS_S_12 | 62 | 505 | 1.211881 | 1.0238512 | 1 | 1.1407407 | 1.4826 | 0 | 3 | 3 | 0.2552893 | -1.1359823 | 0.0455608 |
| DASS_D_13 | 63 | 505 | 1.144554 | 1.0355272 | 1 | 1.0567901 | 1.4826 | 0 | 3 | 3 | 0.3514923 | -1.1245246 | 0.0460804 |
| DASS_S_14 | 64 | 505 | 1.106931 | 1.0543050 | 1 | 1.0098765 | 1.4826 | 0 | 3 | 3 | 0.4556559 | -1.0701359 | 0.0469160 |
| DASS_A_15 | 65 | 505 | 1.000000 | 1.0856203 | 1 | 0.8765432 | 1.4826 | 0 | 3 | 3 | 0.5850144 | -1.0754439 | 0.0483095 |
| DASS_D_16 | 66 | 505 | 1.146535 | 1.1401375 | 1 | 1.0592593 | 1.4826 | 0 | 3 | 3 | 0.3853317 | -1.3355721 | 0.0507355 |
| DASS_D_17 | 67 | 505 | 1.089109 | 1.1100105 | 1 | 0.9876543 | 1.4826 | 0 | 3 | 3 | 0.4931934 | -1.1765343 | 0.0493948 |
| DASS_S_18 | 68 | 505 | 1.186139 | 1.0713652 | 1 | 1.1086420 | 1.4826 | 0 | 3 | 3 | 0.3602118 | -1.1654761 | 0.0476751 |
| DASS_A_19 | 69 | 505 | 1.085149 | 1.0665724 | 1 | 0.9827160 | 1.4826 | 0 | 3 | 3 | 0.4279789 | -1.1718465 | 0.0474618 |
| DASS_A_20 | 70 | 505 | 0.980198 | 1.0725674 | 1 | 0.8518519 | 1.4826 | 0 | 3 | 3 | 0.6266105 | -0.9926589 | 0.0477286 |
| DASS_D_21 | 71 | 505 | 1.053465 | 1.0961299 | 1 | 0.9432099 | 1.4826 | 0 | 3 | 3 | 0.4898527 | -1.1908113 | 0.0487771 |
| DASS_D_sum | 72 | 505 | 7.798020 | 6.5875360 | 8 | 7.4617284 | 10.3782 | 0 | 21 | 21 | 0.1521353 | -1.4033567 | 0.2931415 |
| DASS_A_sum | 73 | 505 | 7.255446 | 6.3901039 | 7 | 6.8962963 | 8.8956 | 0 | 21 | 21 | 0.2340703 | -1.4345546 | 0.2843559 |
| DASS_S_sum | 74 | 505 | 8.200000 | 6.1764401 | 9 | 7.9802469 | 8.8956 | 0 | 21 | 21 | 0.0841834 | -1.2884914 | 0.2748480 |
| BIS_1 | 75 | 505 | 1.869307 | 0.7471829 | 2 | 1.8098765 | 1.4826 | 1 | 4 | 3 | 0.5290557 | -0.1328825 | 0.0332492 |
| BIS_2 | 76 | 505 | 2.005941 | 1.0069029 | 2 | 1.8839506 | 1.4826 | 1 | 4 | 3 | 0.6282720 | -0.7591601 | 0.0448066 |
| BIS_3 | 77 | 505 | 1.584158 | 0.9136023 | 1 | 1.4049383 | 0.0000 | 1 | 4 | 3 | 1.3947740 | 0.7571790 | 0.0406548 |
| BIS_4 | 78 | 505 | 1.893069 | 0.7820224 | 2 | 1.8271605 | 1.4826 | 1 | 4 | 3 | 0.5858528 | -0.1128193 | 0.0347995 |
| BIS_5 | 79 | 505 | 1.950495 | 0.8234682 | 2 | 1.8839506 | 1.4826 | 1 | 4 | 3 | 0.5596606 | -0.2730471 | 0.0366439 |
| BIS_6 | 80 | 505 | 1.871287 | 0.7901096 | 2 | 1.8098765 | 1.4826 | 1 | 4 | 3 | 0.5201732 | -0.4477796 | 0.0351594 |
| BIS_7 | 81 | 505 | 2.100990 | 1.0614544 | 2 | 2.0024691 | 1.4826 | 1 | 4 | 3 | 0.4941504 | -1.0381764 | 0.0472341 |
| BIS_8 | 82 | 505 | 2.200000 | 1.0204263 | 2 | 2.1259259 | 1.4826 | 1 | 4 | 3 | 0.3766058 | -0.9972775 | 0.0454084 |
| BIS_sum | 83 | 505 | 15.475248 | 4.3784149 | 16 | 15.4543210 | 5.9304 | 8 | 30 | 22 | 0.0683769 | -0.7761368 | 0.1948369 |
| SSEIT_1 | 84 | 505 | 3.792079 | 0.9227808 | 4 | 3.8987654 | 0.0000 | 1 | 5 | 4 | -0.9696272 | 0.9837409 | 0.0410632 |
| SSEIT_2 | 85 | 505 | 3.760396 | 0.9301049 | 4 | 3.8419753 | 1.4826 | 1 | 5 | 4 | -0.5873088 | 0.0492575 | 0.0413891 |
| SSEIT_3 | 86 | 505 | 3.803960 | 0.9402719 | 4 | 3.8962963 | 1.4826 | 1 | 5 | 4 | -0.6172669 | -0.0149486 | 0.0418416 |
| SSEIT_4 | 87 | 505 | 3.821782 | 0.9469672 | 4 | 3.9185185 | 1.4826 | 1 | 5 | 4 | -0.6331003 | -0.0258016 | 0.0421395 |
| SSEIT_5 | 88 | 505 | 3.061386 | 1.3010199 | 3 | 3.0765432 | 1.4826 | 1 | 5 | 4 | -0.0541515 | -1.1598166 | 0.0578946 |
| SSEIT_6 | 89 | 505 | 3.728713 | 0.9757310 | 4 | 3.8148148 | 1.4826 | 1 | 5 | 4 | -0.5891896 | -0.0060122 | 0.0434195 |
| SSEIT_7 | 90 | 505 | 3.619802 | 0.9311646 | 4 | 3.6691358 | 1.4826 | 1 | 5 | 4 | -0.3576021 | -0.2325471 | 0.0414363 |
| SSEIT_8 | 91 | 505 | 3.641584 | 1.0099964 | 4 | 3.7160494 | 1.4826 | 1 | 5 | 4 | -0.5437956 | -0.1288949 | 0.0449442 |
| SSEIT_9 | 92 | 505 | 3.865346 | 0.9331245 | 4 | 3.9679012 | 1.4826 | 1 | 5 | 4 | -0.7689138 | 0.3279414 | 0.0415235 |
| SSEIT_10 | 93 | 505 | 3.699010 | 1.0486665 | 4 | 3.7901235 | 1.4826 | 1 | 5 | 4 | -0.6349140 | -0.1813406 | 0.0466650 |
| SSEIT_11 | 94 | 505 | 3.473267 | 1.0928058 | 4 | 3.5259259 | 1.4826 | 1 | 5 | 4 | -0.4143750 | -0.5482673 | 0.0486292 |
| SSEIT_12 | 95 | 505 | 3.546535 | 1.0645407 | 4 | 3.6148148 | 1.4826 | 1 | 5 | 4 | -0.5004904 | -0.3049000 | 0.0473714 |
| SSEIT_13 | 96 | 505 | 3.489109 | 1.1039163 | 4 | 3.5530864 | 1.4826 | 1 | 5 | 4 | -0.4758992 | -0.4864657 | 0.0491236 |
| SSEIT_14 | 97 | 505 | 3.897030 | 0.8848881 | 4 | 3.9777778 | 1.4826 | 1 | 5 | 4 | -0.7074343 | 0.3229228 | 0.0393770 |
| SSEIT_15 | 98 | 505 | 3.716832 | 0.9496312 | 4 | 3.7950617 | 1.4826 | 1 | 5 | 4 | -0.6877414 | 0.1172100 | 0.0422580 |
| SSEIT_16 | 99 | 505 | 3.728713 | 0.9858459 | 4 | 3.8222222 | 1.4826 | 1 | 5 | 4 | -0.6660221 | 0.1577178 | 0.0438696 |
| SSEIT_17 | 100 | 505 | 3.835644 | 0.9400253 | 4 | 3.9283951 | 1.4826 | 1 | 5 | 4 | -0.6416661 | 0.0267044 | 0.0418306 |
| SSEIT_18 | 101 | 505 | 3.863366 | 0.9569838 | 4 | 3.9703704 | 1.4826 | 1 | 5 | 4 | -0.7015757 | 0.0253834 | 0.0425852 |
| SSEIT_19 | 102 | 505 | 3.845545 | 0.9448613 | 4 | 3.9506173 | 1.4826 | 1 | 5 | 4 | -0.7315879 | 0.1919795 | 0.0420458 |
| SSEIT_20 | 103 | 505 | 3.833663 | 0.9129011 | 4 | 3.9111111 | 1.4826 | 1 | 5 | 4 | -0.6977479 | 0.4028583 | 0.0406236 |
| SSEIT_21 | 104 | 505 | 3.798020 | 0.9547272 | 4 | 3.8913580 | 1.4826 | 1 | 5 | 4 | -0.6404780 | -0.0081469 | 0.0424848 |
| SSEIT_22 | 105 | 505 | 3.885149 | 0.9143245 | 4 | 3.9827160 | 1.4826 | 1 | 5 | 4 | -0.8447071 | 0.6515975 | 0.0406869 |
| SSEIT_23 | 106 | 505 | 3.706931 | 1.0144059 | 4 | 3.7925926 | 1.4826 | 1 | 5 | 4 | -0.6209127 | -0.1213563 | 0.0451405 |
| SSEIT_24 | 107 | 505 | 3.966337 | 0.9262783 | 4 | 4.0666667 | 1.4826 | 1 | 5 | 4 | -0.7407871 | 0.1150190 | 0.0412188 |
| SSEIT_25 | 108 | 505 | 3.869307 | 0.9208500 | 4 | 3.9654321 | 1.4826 | 1 | 5 | 4 | -0.7133853 | 0.1862798 | 0.0409773 |
| SSEIT_26 | 109 | 505 | 3.415842 | 1.0752199 | 4 | 3.4592593 | 1.4826 | 1 | 5 | 4 | -0.3651378 | -0.4759906 | 0.0478467 |
| SSEIT_27 | 110 | 505 | 3.659406 | 0.9463986 | 4 | 3.7135802 | 1.4826 | 1 | 5 | 4 | -0.3709604 | -0.4016210 | 0.0421142 |
| SSEIT_28 | 111 | 505 | 3.154455 | 1.3539716 | 3 | 3.1925926 | 1.4826 | 1 | 5 | 4 | -0.1037021 | -1.2136499 | 0.0602509 |
| SSEIT_29 | 112 | 505 | 3.536634 | 0.9955977 | 4 | 3.5777778 | 1.4826 | 1 | 5 | 4 | -0.4320238 | -0.3676381 | 0.0443035 |
| SSEIT_30 | 113 | 505 | 3.829703 | 0.9567580 | 4 | 3.9333333 | 1.4826 | 1 | 5 | 4 | -0.8089982 | 0.5059584 | 0.0425752 |
| SSEIT_31 | 114 | 505 | 3.734653 | 0.9348240 | 4 | 3.8148148 | 1.4826 | 1 | 5 | 4 | -0.6155669 | 0.0956510 | 0.0415991 |
| SSEIT_32 | 115 | 505 | 3.772277 | 0.9352148 | 4 | 3.8617284 | 1.4826 | 1 | 5 | 4 | -0.7259941 | 0.2495079 | 0.0416165 |
| SSEIT_33 | 116 | 505 | 3.140594 | 1.2917764 | 3 | 3.1753086 | 1.4826 | 1 | 5 | 4 | -0.0965767 | -1.1608460 | 0.0574833 |
| SSEIT_sum | 117 | 505 | 121.493069 | 17.7078488 | 124 | 122.0913580 | 16.3086 | 56 | 165 | 109 | -0.3676759 | 0.1002348 | 0.7879889 |
| RSES_1 | 118 | 505 | 2.077228 | 0.9880116 | 2 | 1.9728395 | 1.4826 | 1 | 4 | 3 | 0.6462974 | -0.5891529 | 0.0439659 |
| RSES_2 | 119 | 505 | 2.089109 | 0.9728355 | 2 | 1.9876543 | 1.4826 | 1 | 4 | 3 | 0.6734661 | -0.4827028 | 0.0432906 |
| RSES_3 | 120 | 505 | 2.241584 | 1.0470354 | 2 | 2.1777778 | 1.4826 | 1 | 4 | 3 | 0.3043745 | -1.1211891 | 0.0465925 |
| RSES_4 | 121 | 505 | 2.156436 | 0.9307805 | 2 | 2.0716049 | 1.4826 | 1 | 4 | 3 | 0.4669367 | -0.6213323 | 0.0414192 |
| RSES_5 | 122 | 505 | 2.318812 | 1.0612879 | 2 | 2.2740741 | 1.4826 | 1 | 4 | 3 | 0.2347269 | -1.1785906 | 0.0472267 |
| RSES_6 | 123 | 505 | 2.168317 | 1.0164257 | 2 | 2.0864198 | 1.4826 | 1 | 4 | 3 | 0.5085230 | -0.8394929 | 0.0452303 |
| RSES_7 | 124 | 505 | 2.213861 | 0.9929343 | 2 | 2.1432099 | 1.4826 | 1 | 4 | 3 | 0.4617087 | -0.8093873 | 0.0441850 |
| RSES_8 | 125 | 505 | 2.554455 | 1.0125722 | 3 | 2.5679012 | 1.4826 | 1 | 4 | 3 | -0.0793964 | -1.0947990 | 0.0450589 |
| RSES_9 | 126 | 505 | 2.354455 | 1.0498984 | 2 | 2.3185185 | 1.4826 | 1 | 4 | 3 | 0.1193552 | -1.2051048 | 0.0467199 |
| RSES_10 | 127 | 505 | 2.308911 | 1.0501416 | 2 | 2.2617284 | 1.4826 | 1 | 4 | 3 | 0.2018106 | -1.1774662 | 0.0467307 |
| RSES_sum | 128 | 505 | 22.483168 | 6.9522331 | 23 | 22.3160494 | 5.9304 | 10 | 40 | 30 | 0.2166436 | 0.2107325 | 0.3093703 |
| NEO_N_1 | 129 | 505 | 3.140594 | 1.3620551 | 3 | 3.1753086 | 1.4826 | 1 | 5 | 4 | -0.2733209 | -1.2198453 | 0.0606107 |
| NEO_N_2 | 130 | 505 | 2.980198 | 1.3391258 | 3 | 2.9753086 | 1.4826 | 1 | 5 | 4 | -0.0431307 | -1.2205718 | 0.0595903 |
| NEO_N_3 | 131 | 505 | 2.544554 | 1.3223112 | 2 | 2.4444444 | 1.4826 | 1 | 5 | 4 | 0.3612465 | -1.1113756 | 0.0588421 |
| NEO_N_4 | 132 | 505 | 2.510891 | 1.3701684 | 2 | 2.4024691 | 1.4826 | 1 | 5 | 4 | 0.3698651 | -1.2358449 | 0.0609717 |
| NEO_N_5 | 133 | 505 | 2.732673 | 1.3965028 | 3 | 2.6666667 | 1.4826 | 1 | 5 | 4 | 0.1722621 | -1.2900416 | 0.0621436 |
| NEO_N_6 | 134 | 505 | 2.588119 | 1.4228358 | 2 | 2.4864198 | 1.4826 | 1 | 5 | 4 | 0.3478859 | -1.2670919 | 0.0633154 |
| NEO_N_7 | 135 | 505 | 2.924752 | 1.3548418 | 3 | 2.9061728 | 1.4826 | 1 | 5 | 4 | -0.0644485 | -1.2603014 | 0.0602897 |
| NEO_N_8 | 136 | 505 | 2.706931 | 1.3646804 | 2 | 2.6345679 | 1.4826 | 1 | 5 | 4 | 0.2617591 | -1.2116097 | 0.0607275 |
| NEO_N_9 | 137 | 505 | 2.853465 | 1.2321394 | 3 | 2.8172840 | 1.4826 | 1 | 5 | 4 | 0.1777958 | -0.9521002 | 0.0548295 |
| NEO_N_10 | 138 | 505 | 2.392079 | 1.0730911 | 2 | 2.3111111 | 1.4826 | 1 | 5 | 4 | 0.6456664 | -0.2479501 | 0.0477519 |
| NEO_N_11 | 139 | 505 | 2.326733 | 1.0811233 | 2 | 2.2444444 | 1.4826 | 1 | 5 | 4 | 0.5484074 | -0.4684476 | 0.0481094 |
| NEO_N_12 | 140 | 505 | 2.429703 | 1.1071148 | 2 | 2.3456790 | 1.4826 | 1 | 5 | 4 | 0.5670356 | -0.3566171 | 0.0492660 |
| NEO_E_13 | 141 | 505 | 3.419802 | 1.2384402 | 4 | 3.5209877 | 1.4826 | 1 | 5 | 4 | -0.4575453 | -0.7591977 | 0.0551099 |
| NEO_E_14 | 142 | 505 | 3.572277 | 1.1440078 | 4 | 3.6641975 | 1.4826 | 1 | 5 | 4 | -0.6137964 | -0.4507612 | 0.0509077 |
| NEO_E_15 | 143 | 505 | 2.986139 | 1.3115051 | 3 | 2.9827160 | 1.4826 | 1 | 5 | 4 | -0.0851026 | -1.1469255 | 0.0583612 |
| NEO_E_16 | 144 | 505 | 2.578218 | 1.2175636 | 2 | 2.5160494 | 1.4826 | 1 | 5 | 4 | 0.2987422 | -0.9538210 | 0.0541809 |
| NEO_E_17 | 145 | 505 | 3.261386 | 1.1831296 | 3 | 3.3160494 | 1.4826 | 1 | 5 | 4 | -0.3207268 | -0.8202461 | 0.0526486 |
| NEO_E_18 | 146 | 505 | 3.229703 | 1.2419219 | 3 | 3.2864198 | 1.4826 | 1 | 5 | 4 | -0.2727122 | -0.9066137 | 0.0552648 |
| NEO_E_19 | 147 | 505 | 3.439604 | 1.0619762 | 4 | 3.4814815 | 1.4826 | 1 | 5 | 4 | -0.4671121 | -0.4890003 | 0.0472573 |
| NEO_E_20 | 148 | 505 | 3.334653 | 1.1533625 | 3 | 3.3876543 | 1.4826 | 1 | 5 | 4 | -0.3343134 | -0.6982579 | 0.0513240 |
| NEO_E_21 | 149 | 505 | 3.344555 | 1.1426504 | 3 | 3.4074074 | 1.4826 | 1 | 5 | 4 | -0.3807442 | -0.5589036 | 0.0508473 |
| NEO_E_22 | 150 | 505 | 3.304950 | 1.1977472 | 3 | 3.3604938 | 1.4826 | 1 | 5 | 4 | -0.2694294 | -0.8530508 | 0.0532991 |
| NEO_E_23 | 151 | 505 | 3.566337 | 1.0983643 | 4 | 3.6395062 | 1.4826 | 1 | 5 | 4 | -0.5092585 | -0.4585487 | 0.0488766 |
| NEO_E_24 | 152 | 505 | 3.716832 | 1.0987971 | 4 | 3.8197531 | 1.4826 | 1 | 5 | 4 | -0.6248266 | -0.3281563 | 0.0488958 |
| NEO_O_25 | 153 | 505 | 3.736634 | 1.1001193 | 4 | 3.8518519 | 1.4826 | 1 | 5 | 4 | -0.7166199 | -0.1432410 | 0.0489547 |
| NEO_O_26 | 154 | 505 | 3.560396 | 1.0878185 | 4 | 3.6271605 | 1.4826 | 1 | 5 | 4 | -0.4407955 | -0.4945831 | 0.0484073 |
| NEO_O_27 | 155 | 505 | 3.825743 | 1.0898753 | 4 | 3.9580247 | 1.4826 | 1 | 5 | 4 | -0.8448939 | 0.0643924 | 0.0484988 |
| NEO_O_28 | 156 | 505 | 3.580198 | 1.3880170 | 4 | 3.7234568 | 1.4826 | 1 | 5 | 4 | -0.5122420 | -1.1135767 | 0.0617659 |
| NEO_O_29 | 157 | 505 | 3.429703 | 1.1354274 | 4 | 3.4962963 | 1.4826 | 1 | 5 | 4 | -0.4067464 | -0.5471411 | 0.0505259 |
| NEO_O_30 | 158 | 505 | 3.007921 | 1.2116894 | 3 | 3.0098765 | 1.4826 | 1 | 5 | 4 | 0.0850591 | -0.9447354 | 0.0539195 |
| NEO_O_31 | 159 | 505 | 2.530693 | 1.0576387 | 2 | 2.4839506 | 1.4826 | 1 | 5 | 4 | 0.4166833 | -0.4349744 | 0.0470643 |
| NEO_O_32 | 160 | 505 | 2.930693 | 1.2187151 | 3 | 2.9135802 | 1.4826 | 1 | 5 | 4 | 0.0337074 | -1.0342183 | 0.0542321 |
| NEO_O_33 | 161 | 505 | 3.253465 | 1.3180346 | 3 | 3.3160494 | 1.4826 | 1 | 5 | 4 | -0.1660403 | -1.2001412 | 0.0586518 |
| NEO_O_34 | 162 | 505 | 3.304950 | 1.2669781 | 3 | 3.3654321 | 1.4826 | 1 | 5 | 4 | -0.1873982 | -1.0734291 | 0.0563798 |
| NEO_O_35 | 163 | 505 | 3.433663 | 1.3540267 | 4 | 3.5407407 | 1.4826 | 1 | 5 | 4 | -0.4533491 | -0.9732405 | 0.0602534 |
| NEO_O_36 | 164 | 505 | 3.089109 | 1.5072511 | 3 | 3.1111111 | 1.4826 | 1 | 5 | 4 | 0.0118876 | -1.4548961 | 0.0670718 |
| NEO_A_37 | 165 | 505 | 3.500990 | 1.1668788 | 4 | 3.6024691 | 1.4826 | 1 | 5 | 4 | -0.6604444 | -0.4177379 | 0.0519254 |
| NEO_A_38 | 166 | 505 | 3.447525 | 1.1293869 | 4 | 3.5382716 | 1.4826 | 1 | 5 | 4 | -0.6245546 | -0.2875373 | 0.0502571 |
| NEO_A_39 | 167 | 505 | 3.613861 | 1.3316231 | 4 | 3.7432099 | 1.4826 | 1 | 5 | 4 | -0.4986807 | -1.0150923 | 0.0592565 |
| NEO_A_40 | 168 | 505 | 3.516832 | 1.3916609 | 4 | 3.6444444 | 1.4826 | 1 | 5 | 4 | -0.4600423 | -1.1305739 | 0.0619281 |
| NEO_A_41 | 169 | 505 | 3.774257 | 1.0353526 | 4 | 3.8740741 | 1.4826 | 1 | 5 | 4 | -0.6321565 | -0.2159256 | 0.0460726 |
| NEO_A_42 | 170 | 505 | 3.782178 | 1.0255076 | 4 | 3.8839506 | 1.4826 | 1 | 5 | 4 | -0.6801183 | -0.1080691 | 0.0456345 |
| NEO_A_43 | 171 | 505 | 3.657426 | 1.3226766 | 4 | 3.7827160 | 1.4826 | 1 | 5 | 4 | -0.5310095 | -1.0261259 | 0.0588583 |
| NEO_A_44 | 172 | 505 | 3.479208 | 1.3642485 | 4 | 3.5975309 | 1.4826 | 1 | 5 | 4 | -0.4243405 | -1.0941698 | 0.0607083 |
| NEO_A_45 | 173 | 505 | 3.336634 | 1.4006605 | 3 | 3.4197531 | 1.4826 | 1 | 5 | 4 | -0.1829143 | -1.3078405 | 0.0623286 |
| NEO_A_46 | 174 | 505 | 2.714851 | 1.1843378 | 3 | 2.6543210 | 1.4826 | 1 | 5 | 4 | 0.3414601 | -0.7832162 | 0.0527023 |
| NEO_A_47 | 175 | 505 | 3.851485 | 1.0726223 | 4 | 3.9802469 | 1.4826 | 1 | 5 | 4 | -0.7429187 | -0.0933843 | 0.0477311 |
| NEO_A_48 | 176 | 505 | 3.851485 | 1.0233983 | 4 | 3.9753086 | 1.4826 | 1 | 5 | 4 | -0.7983974 | 0.1774896 | 0.0455406 |
| NEO_C_49 | 177 | 505 | 3.801980 | 0.9740665 | 4 | 3.8962963 | 1.4826 | 1 | 5 | 4 | -0.5999320 | -0.1603921 | 0.0433454 |
| NEO_C_50 | 178 | 505 | 3.817822 | 0.9952662 | 4 | 3.9234568 | 1.4826 | 1 | 5 | 4 | -0.7152479 | 0.0282972 | 0.0442888 |
| NEO_C_51 | 179 | 505 | 3.572277 | 1.1405337 | 4 | 3.6493827 | 1.4826 | 1 | 5 | 4 | -0.4821251 | -0.6344332 | 0.0507531 |
| NEO_C_52 | 180 | 505 | 3.255445 | 1.3613482 | 3 | 3.3185185 | 1.4826 | 1 | 5 | 4 | -0.1704848 | -1.2328377 | 0.0605792 |
| NEO_C_53 | 181 | 505 | 4.015842 | 0.9083751 | 4 | 4.1234568 | 1.4826 | 1 | 5 | 4 | -0.7919462 | 0.1479149 | 0.0404222 |
| NEO_C_54 | 182 | 505 | 2.439604 | 1.3096164 | 2 | 2.3308642 | 1.4826 | 1 | 5 | 4 | 0.4382540 | -1.0513209 | 0.0582772 |
| NEO_C_55 | 183 | 505 | 3.994059 | 0.9849887 | 4 | 4.1283951 | 1.4826 | 1 | 5 | 4 | -0.8211508 | 0.0029880 | 0.0438314 |
| NEO_C_56 | 184 | 505 | 3.786139 | 1.0048523 | 4 | 3.8913580 | 1.4826 | 1 | 5 | 4 | -0.6997765 | 0.0775830 | 0.0447153 |
| NEO_C_57 | 185 | 505 | 3.801980 | 1.0372028 | 4 | 3.9259259 | 1.4826 | 1 | 5 | 4 | -0.8873011 | 0.3700760 | 0.0461549 |
| NEO_C_58 | 186 | 505 | 3.184158 | 1.3524473 | 3 | 3.2296296 | 1.4826 | 1 | 5 | 4 | -0.1244468 | -1.2235857 | 0.0601831 |
| NEO_C_59 | 187 | 505 | 3.431683 | 1.3377109 | 4 | 3.5283951 | 1.4826 | 1 | 5 | 4 | -0.3181664 | -1.1756431 | 0.0595274 |
| NEO_C_60 | 188 | 505 | 3.471287 | 1.3843181 | 4 | 3.5876543 | 1.4826 | 1 | 5 | 4 | -0.4242954 | -1.1378074 | 0.0616013 |
| NEO_N_sum | 189 | 505 | 32.130693 | 9.6803448 | 34 | 32.4962963 | 8.8956 | 12 | 59 | 47 | -0.2934942 | -0.6153351 | 0.4307697 |
| NEO_E_sum | 190 | 505 | 39.754455 | 9.1366991 | 41 | 40.2691358 | 8.8956 | 12 | 60 | 48 | -0.4859173 | -0.1793039 | 0.4065778 |
| NEO_O_sum | 191 | 505 | 39.683168 | 6.8971047 | 38 | 39.1925926 | 5.9304 | 24 | 59 | 35 | 0.5720891 | -0.3385599 | 0.3069171 |
| NEO_A_sum | 192 | 505 | 42.526733 | 7.8109977 | 41 | 42.1925926 | 7.4130 | 21 | 60 | 39 | 0.3360591 | -0.7909585 | 0.3475848 |
| NEO_C_sum | 193 | 505 | 42.572277 | 6.7851980 | 42 | 42.3580247 | 7.4130 | 25 | 57 | 32 | 0.2555865 | -0.7168260 | 0.3019373 |
# export tables as csv files
write.csv(sona_cont_ques,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_cont_ques(oct28).csv", row.names = T)
write.csv(mturk_cont_ques,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_cont_ques(oct28).csv", row.names = T)Analyses
Assumption Tests
All assumptions were met for the correlation analysis. Click here to see the assumption tests.
The assumptions for a correlation analysis were tested. There were two assumptions: (1) Linearity and (2) Normality.
Linearity was visually inspected from the scatter plots (see plots in the “Figures: Scatter Plots” section).
Normality was tested using (a) the Shapiro-Wilk normality test and
- visually inspected using a normality plot.
The Shapiro-Wilk normality test will show two values (i.e., W and p) and each test uses the following hypotheses: HN: the data are normally distributed and HA: the data are not normally distributed.
The normality plots used were Q-Q plots (quantile-quantile plots), which draw the correlation between a given sample and the theoretical normal distribution.
(a) Shapiro-Wilk Normality Test
The table depicts the results of the Shapiro-Wilks normality test for each measure. (click to see code)
# create a table to show results of all normality tests
corrs_reg_norm_test_sona_all <-
as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D",
"DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"),
rbind(c(shapiro.test(SONA_data$NMPQ_sum)$statistic, shapiro.test(SONA_data$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
c(shapiro.test(SONA_data$SAD_sum)$statistic, shapiro.test(SONA_data$SAD_sum)$p.value), # W & p-value for SAD_sum
c(shapiro.test(SONA_data$SAD_dep_sum)$statistic, shapiro.test(SONA_data$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
c(shapiro.test(SONA_data$SAD_access_sum)$statistic, shapiro.test(SONA_data$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
c(shapiro.test(SONA_data$SAD_ea_sum)$statistic, shapiro.test(SONA_data$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
c(shapiro.test(SONA_data$SAD_dist_sum)$statistic, shapiro.test(SONA_data$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
c(shapiro.test(SONA_data$age)$statistic, shapiro.test(SONA_data$age)$p.value), # W & p-value for age
c(shapiro.test(SONA_data$SRS_sum)$statistic, shapiro.test(SONA_data$SRS_sum)$p.value), # W & p-value for SRS_sum
c(shapiro.test(SONA_data$DASS_D_sum)$statistic, shapiro.test(SONA_data$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
c(shapiro.test(SONA_data$DASS_A_sum)$statistic, shapiro.test(SONA_data$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
c(shapiro.test(SONA_data$DASS_S_sum)$statistic, shapiro.test(SONA_data$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
c(shapiro.test(SONA_data$BIS_sum)$statistic, shapiro.test(SONA_data$BIS_sum)$p.value), # W & p-value for BIS_sum
c(shapiro.test(SONA_data$SSEIT_sum)$statistic, shapiro.test(SONA_data$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
c(shapiro.test(SONA_data$RSES_sum)$statistic, shapiro.test(SONA_data$RSES_sum)$p.value), # W & p-value for RSES_sum
c(shapiro.test(SONA_data$NEO_N_sum)$statistic, shapiro.test(SONA_data$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
c(shapiro.test(SONA_data$NEO_E_sum)$statistic, shapiro.test(SONA_data$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
c(shapiro.test(SONA_data$NEO_O_sum)$statistic, shapiro.test(SONA_data$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
c(shapiro.test(SONA_data$NEO_A_sum)$statistic, shapiro.test(SONA_data$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
c(shapiro.test(SONA_data$NEO_C_sum)$statistic, shapiro.test(SONA_data$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
)))
colnames(corrs_reg_norm_test_sona_all) <- c("Measure", "W", "p")
# make cols numeric & round to 3 decimals
corrs_reg_norm_test_sona_all$W <- as.numeric(corrs_reg_norm_test_sona_all$W) %>% round(.,3)
corrs_reg_norm_test_sona_all$p <- as.numeric(corrs_reg_norm_test_sona_all$p) %>% round(.,3)
corrs_reg_norm_test_mturk_all <-
as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D",
"DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"),
rbind(c(shapiro.test(Mturk_data$NMPQ_sum)$statistic, shapiro.test(Mturk_data$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
c(shapiro.test(Mturk_data$SAD_sum)$statistic, shapiro.test(Mturk_data$SAD_sum)$p.value), # W & p-value for SAD_sum
c(shapiro.test(Mturk_data$SAD_dep_sum)$statistic, shapiro.test(Mturk_data$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
c(shapiro.test(Mturk_data$SAD_access_sum)$statistic, shapiro.test(Mturk_data$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
c(shapiro.test(Mturk_data$SAD_ea_sum)$statistic, shapiro.test(Mturk_data$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
c(shapiro.test(Mturk_data$SAD_dist_sum)$statistic, shapiro.test(Mturk_data$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
c(shapiro.test(Mturk_data$age)$statistic, shapiro.test(Mturk_data$age)$p.value), # W & p-value for age
c(shapiro.test(Mturk_data$SRS_sum)$statistic, shapiro.test(Mturk_data$SRS_sum)$p.value), # W & p-value for SRS_sum
c(shapiro.test(Mturk_data$DASS_D_sum)$statistic, shapiro.test(Mturk_data$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
c(shapiro.test(Mturk_data$DASS_A_sum)$statistic, shapiro.test(Mturk_data$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
c(shapiro.test(Mturk_data$DASS_S_sum)$statistic, shapiro.test(Mturk_data$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
c(shapiro.test(Mturk_data$BIS_sum)$statistic, shapiro.test(Mturk_data$BIS_sum)$p.value), # W & p-value for BIS_sum
c(shapiro.test(Mturk_data$SSEIT_sum)$statistic, shapiro.test(Mturk_data$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
c(shapiro.test(Mturk_data$RSES_sum)$statistic, shapiro.test(Mturk_data$RSES_sum)$p.value), # W & p-value for RSES_sum
c(shapiro.test(Mturk_data$NEO_N_sum)$statistic, shapiro.test(Mturk_data$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
c(shapiro.test(Mturk_data$NEO_E_sum)$statistic, shapiro.test(Mturk_data$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
c(shapiro.test(Mturk_data$NEO_O_sum)$statistic, shapiro.test(Mturk_data$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
c(shapiro.test(Mturk_data$NEO_A_sum)$statistic, shapiro.test(Mturk_data$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
c(shapiro.test(Mturk_data$NEO_C_sum)$statistic, shapiro.test(Mturk_data$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
)))
colnames(corrs_reg_norm_test_mturk_all) <- c("Measure", "W", "p")
# make cols numeric & round to 3 decimals
corrs_reg_norm_test_mturk_all$W <- as.numeric(corrs_reg_norm_test_mturk_all$W) %>% round(.,3)
corrs_reg_norm_test_mturk_all$p <- as.numeric(corrs_reg_norm_test_mturk_all$p) %>% round(.,3)
# join into 1 large table
corrs_reg_norm_tests_t <- cbind(corrs_reg_norm_test_sona_all, corrs_reg_norm_test_mturk_all[,2:3])As seen in the table, most measures showed a significant result, indicating non-normality. The visual inspection of the Q-Q plots was completed next.
# show table using kable
kable(corrs_reg_norm_tests_t, caption = "Shapiro-Wilks Normality Test for All Measures", align = rep('lcccc')) %>%
kable_paper(full_width = F) %>%
add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
pack_rows("Criterion(s)", 1, 6) %>%
# column_spec(1, width = "10em") %>%
pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()SONA |
Mturk |
|||
|---|---|---|---|---|
| Measure | W | p | W | p |
| Criterion(s) | ||||
| NMPQ | 0.986 | 0.017 | 0.952 | 0 |
| SAD | 0.973 | 0.000 | 0.958 | 0 |
| SAD_dep | 0.966 | 0.000 | 0.935 | 0 |
| SAD_access | 0.968 | 0.000 | 0.936 | 0 |
| SAD_ea | 0.977 | 0.001 | 0.964 | 0 |
| SAD_dist | 0.950 | 0.000 | 0.951 | 0 |
| Predictor(s) | ||||
| age | 0.558 | 0.000 | 0.866 | 0 |
| SRS | 0.985 | 0.013 | 0.971 | 0 |
| DASS_D | 0.936 | 0.000 | 0.890 | 0 |
| DASS_A | 0.954 | 0.000 | 0.879 | 0 |
| DASS_S | 0.981 | 0.002 | 0.924 | 0 |
| BIS | 0.989 | 0.056 | 0.971 | 0 |
| SSEIT | 0.963 | 0.000 | 0.987 | 0 |
| RSES | 0.980 | 0.002 | 0.959 | 0 |
| NEO_N | 0.995 | 0.647 | 0.963 | 0 |
| NEO_E | 0.990 | 0.089 | 0.978 | 0 |
| NEO_O | 0.991 | 0.121 | 0.962 | 0 |
| NEO_A | 0.979 | 0.001 | 0.956 | 0 |
| NEO_C | 0.995 | 0.546 | 0.974 | 0 |
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). | ||||
(b) Q-Q Plots
Overall, the Q-Q plots show the data for all measures except age and age of first smartphone did fall along or close to the line. The age measures were not expected to be normally distributed; therefore, the analysis was completed for each measure. (click to see code)
# SONA - All
grid.arrange(ggqqplot(SONA_data$NMPQ_sum, ylab = "NMPQ"),
ggqqplot(SONA_data$SAD_sum, ylab = "SAD"),
ggqqplot(SONA_data$SAD_dep_sum, ylab = "SAD_dep"),
ggqqplot(SONA_data$SAD_access_sum, ylab = "SAD_access"),
ggqqplot(SONA_data$SAD_ea_sum, ylab = "SAD_ea"),
ggqqplot(SONA_data$SAD_dist_sum, ylab = "SAD_dist"),
ggqqplot(SONA_data$age, ylab = "age"),
ggqqplot(SONA_data$SRS_sum, ylab = "SRS"),
ggqqplot(SONA_data$DASS_D_sum, ylab = "DASS_D"),
ggqqplot(SONA_data$DASS_A_sum, ylab = "DASS_A"),
ggqqplot(SONA_data$DASS_S_sum, ylab = "DASS_S"),
ggqqplot(SONA_data$BIS_sum, ylab = "BIS"),
ggqqplot(SONA_data$SSEIT_sum, ylab = "SSEIT"),
ggqqplot(SONA_data$RSES_sum, ylab = "RSES"),
ggqqplot(SONA_data$NEO_N_sum, ylab = "NEO_N"),
ggqqplot(SONA_data$NEO_E_sum, ylab = "NEO_E"),
ggqqplot(SONA_data$NEO_O_sum, ylab = "NEO_O"),
ggqqplot(SONA_data$NEO_A_sum, ylab = "NEO_A"),
ggqqplot(SONA_data$NEO_C_sum, ylab = "NEO_C"),
nrow = 4,
top = text_grob("Q-Q Plots For SONA - ALL",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to the line.",
face = "italic",
x = 0.05,
hjust = 0)
)
# for MTURK - ALL
grid.arrange(ggqqplot(Mturk_data$NMPQ_sum, ylab = "NMPQ"),
ggqqplot(Mturk_data$SAD_sum, ylab = "SAD"),
ggqqplot(Mturk_data$SAD_dep_sum, ylab = "SAD_dep"),
ggqqplot(Mturk_data$SAD_access_sum, ylab = "SAD_access"),
ggqqplot(Mturk_data$SAD_ea_sum, ylab = "SAD_ea"),
ggqqplot(Mturk_data$SAD_dist_sum, ylab = "SAD_dist"),
ggqqplot(Mturk_data$age, ylab = "age"),
ggqqplot(Mturk_data$SRS_sum, ylab = "SRS"),
ggqqplot(Mturk_data$DASS_D_sum, ylab = "DASS_D"),
ggqqplot(Mturk_data$DASS_A_sum, ylab = "DASS_A"),
ggqqplot(Mturk_data$DASS_S_sum, ylab = "DASS_S"),
ggqqplot(Mturk_data$BIS_sum, ylab = "BIS"),
ggqqplot(Mturk_data$SSEIT_sum, ylab = "SSEIT"),
ggqqplot(Mturk_data$RSES_sum, ylab = "RSES"),
ggqqplot(Mturk_data$NEO_N_sum, ylab = "NEO_N"),
ggqqplot(Mturk_data$NEO_E_sum, ylab = "NEO_E"),
ggqqplot(Mturk_data$NEO_O_sum, ylab = "NEO_O"),
ggqqplot(Mturk_data$NEO_A_sum, ylab = "NEO_A"),
ggqqplot(Mturk_data$NEO_C_sum, ylab = "NEO_C"),
nrow = 4,
top = text_grob("Q-Q Plots For Mturk - ALL",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to the line.",
face = "italic",
x = 0.05,
hjust = 0)
)
(c) Histogram Plots
Overall, the Q-Q plots show the data for all measures except age and age of first smartphone did fall along or close to the line. The age measures were not expected to be normally distributed; therefore, the analysis was completed for each measure. (click to see code)
# SONA - All
grid.arrange(qplot(SONA_data$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
# qplot(SONA_data$NMPQ_sum, geom = "density"),
qplot(SONA_data$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(SONA_data$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
nrow = 4,
top = text_grob("Histogram For SONA Data - ALL",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.",
face = "italic",
x = 0.05,
hjust = 0)
)
# for MTURK - ALL
grid.arrange(qplot(Mturk_data$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
# qplot(Mturk_data$NMPQ_sum, geom = "density"),
qplot(Mturk_data$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot(Mturk_data$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
nrow = 4,
top = text_grob("Histogram For Mturk Data - ALL",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.",
face = "italic",
x = 0.05,
hjust = 0)
)
Correlations
Get correlations b/w all cont vars for (1) all data and (2) ST data only.
All Data
All data collected was analyzed. This was done for 240SONA ps and 505 Mturk ps. ####
Study 1: SONA
sona_corr <-
SONA_data %>%
# select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study,
NMPQ_sum,
SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>%
as.matrix() %>%
Hmisc::rcorr(type = c("pearson"))
# create new pilot_corr to shown only lower triangle...
sona_corr2 <- sona_corr
# round to 4 decimals...
sona_corr2$r <- round(sona_corr2$r, 4)
sona_corr2$P <- round(sona_corr2$P, 4)
sona_corr2$n <- round(sona_corr2$n, 4)
# remove upper triangle form r, p, and n
sona_corr2$r[upper.tri(sona_corr2$r)] <- "-"
sona_corr2$P[upper.tri(sona_corr2$P)] <- "-"
sona_corr2$n[upper.tri(sona_corr2$n)] <- "-"
# show corr table with flattenCorr
kable(flattenCorrMatrix(sona_corr$r, sona_corr$P), caption = "SONA Study - Correlation: Flat") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| row | column | cor | p |
|---|---|---|---|
| age | age_first_phone | 0.4300488 | 0.0000000 |
| age | com_gen | -0.0599132 | 0.3554048 |
| age_first_phone | com_gen | 0.0215331 | 0.7399748 |
| age | com_unattended | 0.0183562 | 0.7772438 |
| age_first_phone | com_unattended | 0.0074128 | 0.9090465 |
| com_gen | com_unattended | 0.4017642 | 0.0000000 |
| age | com_leave_with_others | 0.0117777 | 0.8559655 |
| age_first_phone | com_leave_with_others | 0.0191250 | 0.7681750 |
| com_gen | com_leave_with_others | 0.5682967 | 0.0000000 |
| com_unattended | com_leave_with_others | 0.6171960 | 0.0000000 |
| age | com_locked | 0.0069082 | 0.9152143 |
| age_first_phone | com_locked | -0.0633861 | 0.3281580 |
| com_gen | com_locked | -0.1784642 | 0.0055608 |
| com_unattended | com_locked | -0.1536191 | 0.0172383 |
| com_leave_with_others | com_locked | -0.1951349 | 0.0023936 |
| age | com_room_task | -0.0240317 | 0.7110788 |
| age_first_phone | com_room_task | 0.0008542 | 0.9894970 |
| com_gen | com_room_task | 0.3293886 | 0.0000002 |
| com_unattended | com_room_task | 0.4198734 | 0.0000000 |
| com_leave_with_others | com_room_task | 0.3999034 | 0.0000000 |
| com_locked | com_room_task | 0.0257332 | 0.6916322 |
| age | dist_daily | -0.1596456 | 0.0132786 |
| age_first_phone | dist_daily | -0.1394654 | 0.0307823 |
| com_gen | dist_daily | 0.0730332 | 0.2597325 |
| com_unattended | dist_daily | 0.1211658 | 0.0609024 |
| com_leave_with_others | dist_daily | 0.0402706 | 0.5346888 |
| com_locked | dist_daily | 0.2035550 | 0.0015231 |
| com_room_task | dist_daily | 0.0788017 | 0.2238670 |
| age | dist_study | -0.0304961 | 0.6382929 |
| age_first_phone | dist_study | -0.0535348 | 0.4090199 |
| com_gen | dist_study | -0.1113694 | 0.0851276 |
| com_unattended | dist_study | -0.1455671 | 0.0241102 |
| com_leave_with_others | dist_study | -0.1070472 | 0.0980326 |
| com_locked | dist_study | -0.0808652 | 0.2119347 |
| com_room_task | dist_study | -0.1514769 | 0.0188754 |
| dist_daily | dist_study | 0.2723053 | 0.0000189 |
| age | NMPQ_sum | 0.0093923 | 0.8849095 |
| age_first_phone | NMPQ_sum | -0.0411515 | 0.5257823 |
| com_gen | NMPQ_sum | -0.1420353 | 0.0278012 |
| com_unattended | NMPQ_sum | -0.1826748 | 0.0045235 |
| com_leave_with_others | NMPQ_sum | -0.1949332 | 0.0024191 |
| com_locked | NMPQ_sum | 0.1766327 | 0.0060751 |
| com_room_task | NMPQ_sum | -0.1548200 | 0.0163758 |
| dist_daily | NMPQ_sum | 0.2413819 | 0.0001594 |
| dist_study | NMPQ_sum | 0.2220665 | 0.0005290 |
| age | SAD_sum | 0.0274749 | 0.6719326 |
| age_first_phone | SAD_sum | -0.0583682 | 0.3679698 |
| com_gen | SAD_sum | -0.1258482 | 0.0515106 |
| com_unattended | SAD_sum | -0.1367342 | 0.0342453 |
| com_leave_with_others | SAD_sum | -0.1913247 | 0.0029198 |
| com_locked | SAD_sum | 0.2473563 | 0.0001078 |
| com_room_task | SAD_sum | -0.1610043 | 0.0125051 |
| dist_daily | SAD_sum | 0.3377313 | 0.0000001 |
| dist_study | SAD_sum | 0.1791606 | 0.0053757 |
| NMPQ_sum | SAD_sum | 0.7625426 | 0.0000000 |
| age | SAD_dep_sum | 0.0567414 | 0.3814926 |
| age_first_phone | SAD_dep_sum | -0.0350934 | 0.5885129 |
| com_gen | SAD_dep_sum | -0.1620583 | 0.0119327 |
| com_unattended | SAD_dep_sum | -0.1589033 | 0.0137187 |
| com_leave_with_others | SAD_dep_sum | -0.2325118 | 0.0002800 |
| com_locked | SAD_dep_sum | 0.2083905 | 0.0011653 |
| com_room_task | SAD_dep_sum | -0.2362370 | 0.0002216 |
| dist_daily | SAD_dep_sum | 0.2598712 | 0.0000460 |
| dist_study | SAD_dep_sum | 0.1382921 | 0.0322315 |
| NMPQ_sum | SAD_dep_sum | 0.6813126 | 0.0000000 |
| SAD_sum | SAD_dep_sum | 0.8510905 | 0.0000000 |
| age | SAD_ea_sum | 0.0482939 | 0.4564567 |
| age_first_phone | SAD_ea_sum | -0.0303744 | 0.6396346 |
| com_gen | SAD_ea_sum | -0.0853338 | 0.1876752 |
| com_unattended | SAD_ea_sum | -0.1452699 | 0.0244036 |
| com_leave_with_others | SAD_ea_sum | -0.1110620 | 0.0859978 |
| com_locked | SAD_ea_sum | 0.1897067 | 0.0031734 |
| com_room_task | SAD_ea_sum | -0.0802177 | 0.2156287 |
| dist_daily | SAD_ea_sum | 0.2087366 | 0.0011429 |
| dist_study | SAD_ea_sum | 0.1269488 | 0.0494873 |
| NMPQ_sum | SAD_ea_sum | 0.5726592 | 0.0000000 |
| SAD_sum | SAD_ea_sum | 0.8217898 | 0.0000000 |
| SAD_dep_sum | SAD_ea_sum | 0.5384308 | 0.0000000 |
| age | SAD_access_sum | -0.0040263 | 0.9505232 |
| age_first_phone | SAD_access_sum | -0.1122011 | 0.0828085 |
| com_gen | SAD_access_sum | -0.0956170 | 0.1396905 |
| com_unattended | SAD_access_sum | -0.0730763 | 0.2594502 |
| com_leave_with_others | SAD_access_sum | -0.1540071 | 0.0169554 |
| com_locked | SAD_access_sum | 0.1996798 | 0.0018795 |
| com_room_task | SAD_access_sum | -0.1059259 | 0.1016225 |
| dist_daily | SAD_access_sum | 0.2788222 | 0.0000116 |
| dist_study | SAD_access_sum | 0.1369898 | 0.0339077 |
| NMPQ_sum | SAD_access_sum | 0.6630604 | 0.0000000 |
| SAD_sum | SAD_access_sum | 0.8084499 | 0.0000000 |
| SAD_dep_sum | SAD_access_sum | 0.6060057 | 0.0000000 |
| SAD_ea_sum | SAD_access_sum | 0.5514611 | 0.0000000 |
| age | SAD_dist_sum | -0.0369365 | 0.5690682 |
| age_first_phone | SAD_dist_sum | 0.0173090 | 0.7896461 |
| com_gen | SAD_dist_sum | -0.0447252 | 0.4904412 |
| com_unattended | SAD_dist_sum | -0.0161877 | 0.8029846 |
| com_leave_with_others | SAD_dist_sum | -0.0677277 | 0.2960432 |
| com_locked | SAD_dist_sum | 0.1927820 | 0.0027073 |
| com_room_task | SAD_dist_sum | -0.0764851 | 0.2378228 |
| dist_daily | SAD_dist_sum | 0.3818491 | 0.0000000 |
| dist_study | SAD_dist_sum | 0.1756978 | 0.0063537 |
| NMPQ_sum | SAD_dist_sum | 0.4194250 | 0.0000000 |
| SAD_sum | SAD_dist_sum | 0.6585923 | 0.0000000 |
| SAD_dep_sum | SAD_dist_sum | 0.4259424 | 0.0000000 |
| SAD_ea_sum | SAD_dist_sum | 0.5276916 | 0.0000000 |
| SAD_access_sum | SAD_dist_sum | 0.3680577 | 0.0000000 |
| age | BIS_sum | 0.0240188 | 0.7112273 |
| age_first_phone | BIS_sum | -0.0619101 | 0.3395692 |
| com_gen | BIS_sum | -0.0830296 | 0.1999170 |
| com_unattended | BIS_sum | -0.0296826 | 0.6472819 |
| com_leave_with_others | BIS_sum | -0.0177417 | 0.7845156 |
| com_locked | BIS_sum | -0.0941162 | 0.1460410 |
| com_room_task | BIS_sum | -0.0156805 | 0.8090371 |
| dist_daily | BIS_sum | 0.0472866 | 0.4659137 |
| dist_study | BIS_sum | 0.2362691 | 0.0002211 |
| NMPQ_sum | BIS_sum | 0.0346417 | 0.5933242 |
| SAD_sum | BIS_sum | 0.0825858 | 0.2023398 |
| SAD_dep_sum | BIS_sum | 0.0915046 | 0.1576119 |
| SAD_ea_sum | BIS_sum | 0.0455693 | 0.4822828 |
| SAD_access_sum | BIS_sum | 0.0197556 | 0.7607588 |
| SAD_dist_sum | BIS_sum | 0.1079446 | 0.0952328 |
| age | NEO_O_sum | 0.0106113 | 0.8700966 |
| age_first_phone | NEO_O_sum | 0.0464285 | 0.4740539 |
| com_gen | NEO_O_sum | 0.0650061 | 0.3159216 |
| com_unattended | NEO_O_sum | 0.0755997 | 0.2433149 |
| com_leave_with_others | NEO_O_sum | 0.0448314 | 0.4894105 |
| com_locked | NEO_O_sum | 0.1023098 | 0.1139118 |
| com_room_task | NEO_O_sum | 0.1219217 | 0.0592972 |
| dist_daily | NEO_O_sum | 0.2223091 | 0.0005214 |
| dist_study | NEO_O_sum | -0.0361045 | 0.5778073 |
| NMPQ_sum | NEO_O_sum | -0.0605416 | 0.3503728 |
| SAD_sum | NEO_O_sum | -0.0186757 | 0.7734712 |
| SAD_dep_sum | NEO_O_sum | -0.1476851 | 0.0221057 |
| SAD_ea_sum | NEO_O_sum | -0.0268915 | 0.6785069 |
| SAD_access_sum | NEO_O_sum | 0.0699757 | 0.2802655 |
| SAD_dist_sum | NEO_O_sum | 0.1324435 | 0.0403514 |
| BIS_sum | NEO_O_sum | 0.0254176 | 0.6952244 |
| age | NEO_C_sum | 0.0385834 | 0.5519552 |
| age_first_phone | NEO_C_sum | 0.0378626 | 0.5594144 |
| com_gen | NEO_C_sum | 0.0479522 | 0.4596525 |
| com_unattended | NEO_C_sum | 0.0754146 | 0.2444740 |
| com_leave_with_others | NEO_C_sum | 0.0340683 | 0.5994572 |
| com_locked | NEO_C_sum | 0.1189557 | 0.0658020 |
| com_room_task | NEO_C_sum | 0.0273445 | 0.6734001 |
| dist_daily | NEO_C_sum | 0.0004800 | 0.9940978 |
| dist_study | NEO_C_sum | -0.1070064 | 0.0981616 |
| NMPQ_sum | NEO_C_sum | -0.0029408 | 0.9638511 |
| SAD_sum | NEO_C_sum | -0.0335509 | 0.6050159 |
| SAD_dep_sum | NEO_C_sum | -0.0909739 | 0.1600454 |
| SAD_ea_sum | NEO_C_sum | -0.0161619 | 0.8032928 |
| SAD_access_sum | NEO_C_sum | 0.0498851 | 0.4417387 |
| SAD_dist_sum | NEO_C_sum | -0.0023599 | 0.9709883 |
| BIS_sum | NEO_C_sum | -0.6874698 | 0.0000000 |
| NEO_O_sum | NEO_C_sum | 0.0455627 | 0.4823461 |
| age | NEO_E_sum | -0.0951073 | 0.1418231 |
| age_first_phone | NEO_E_sum | -0.1162831 | 0.0721532 |
| com_gen | NEO_E_sum | 0.2432736 | 0.0001410 |
| com_unattended | NEO_E_sum | 0.1490207 | 0.0209172 |
| com_leave_with_others | NEO_E_sum | 0.1914719 | 0.0028977 |
| com_locked | NEO_E_sum | 0.1226303 | 0.0578243 |
| com_room_task | NEO_E_sum | 0.1369061 | 0.0340179 |
| dist_daily | NEO_E_sum | 0.2050855 | 0.0014002 |
| dist_study | NEO_E_sum | 0.0382056 | 0.5558583 |
| NMPQ_sum | NEO_E_sum | 0.0323498 | 0.6180083 |
| SAD_sum | NEO_E_sum | 0.0368995 | 0.5694550 |
| SAD_dep_sum | NEO_E_sum | -0.0446987 | 0.4906988 |
| SAD_ea_sum | NEO_E_sum | 0.0845724 | 0.1916580 |
| SAD_access_sum | NEO_E_sum | 0.0442860 | 0.4947153 |
| SAD_dist_sum | NEO_E_sum | 0.1106772 | 0.0870972 |
| BIS_sum | NEO_E_sum | -0.2004918 | 0.0017990 |
| NEO_O_sum | NEO_E_sum | 0.3003354 | 0.0000022 |
| NEO_C_sum | NEO_E_sum | 0.4089694 | 0.0000000 |
| age | NEO_A_sum | 0.1463623 | 0.0233400 |
| age_first_phone | NEO_A_sum | 0.1243601 | 0.0543551 |
| com_gen | NEO_A_sum | 0.2239333 | 0.0004731 |
| com_unattended | NEO_A_sum | 0.1199072 | 0.0636545 |
| com_leave_with_others | NEO_A_sum | 0.1781255 | 0.0056529 |
| com_locked | NEO_A_sum | 0.0924528 | 0.1533337 |
| com_room_task | NEO_A_sum | 0.1717988 | 0.0076427 |
| dist_daily | NEO_A_sum | 0.1637237 | 0.0110752 |
| dist_study | NEO_A_sum | -0.1541330 | 0.0168645 |
| NMPQ_sum | NEO_A_sum | 0.0376060 | 0.5620811 |
| SAD_sum | NEO_A_sum | -0.0137490 | 0.8321877 |
| SAD_dep_sum | NEO_A_sum | -0.0537918 | 0.4067714 |
| SAD_ea_sum | NEO_A_sum | -0.0764009 | 0.2383416 |
| SAD_access_sum | NEO_A_sum | 0.0771512 | 0.2337488 |
| SAD_dist_sum | NEO_A_sum | 0.0362321 | 0.5764635 |
| BIS_sum | NEO_A_sum | -0.2752500 | 0.0000152 |
| NEO_O_sum | NEO_A_sum | 0.3069601 | 0.0000012 |
| NEO_C_sum | NEO_A_sum | 0.3607763 | 0.0000000 |
| NEO_E_sum | NEO_A_sum | 0.2104222 | 0.0010395 |
| age | NEO_N_sum | 0.1411576 | 0.0287903 |
| age_first_phone | NEO_N_sum | 0.0823756 | 0.2034948 |
| com_gen | NEO_N_sum | -0.0805619 | 0.2136596 |
| com_unattended | NEO_N_sum | 0.0035464 | 0.9564141 |
| com_leave_with_others | NEO_N_sum | -0.0520289 | 0.4223424 |
| com_locked | NEO_N_sum | -0.0024103 | 0.9703695 |
| com_room_task | NEO_N_sum | -0.0258418 | 0.6903971 |
| dist_daily | NEO_N_sum | 0.0651281 | 0.3150124 |
| dist_study | NEO_N_sum | 0.0897709 | 0.1656661 |
| NMPQ_sum | NEO_N_sum | 0.2438789 | 0.0001355 |
| SAD_sum | NEO_N_sum | 0.2365534 | 0.0002172 |
| SAD_dep_sum | NEO_N_sum | 0.1818527 | 0.0047112 |
| SAD_ea_sum | NEO_N_sum | 0.1627066 | 0.0115921 |
| SAD_access_sum | NEO_N_sum | 0.2412295 | 0.0001610 |
| SAD_dist_sum | NEO_N_sum | 0.1598343 | 0.0131687 |
| BIS_sum | NEO_N_sum | 0.3391693 | 0.0000001 |
| NEO_O_sum | NEO_N_sum | 0.0322652 | 0.6189275 |
| NEO_C_sum | NEO_N_sum | -0.3168889 | 0.0000005 |
| NEO_E_sum | NEO_N_sum | -0.3320323 | 0.0000001 |
| NEO_A_sum | NEO_N_sum | 0.0474295 | 0.4645651 |
| age | RSES_sum | -0.0259072 | 0.6896541 |
| age_first_phone | RSES_sum | 0.0102024 | 0.8750602 |
| com_gen | RSES_sum | -0.1096555 | 0.0900712 |
| com_unattended | RSES_sum | -0.0541899 | 0.4033028 |
| com_leave_with_others | RSES_sum | -0.0253261 | 0.6962669 |
| com_locked | RSES_sum | -0.1349066 | 0.0367428 |
| com_room_task | RSES_sum | -0.0594664 | 0.3590108 |
| dist_daily | RSES_sum | -0.0417275 | 0.5199998 |
| dist_study | RSES_sum | -0.0321157 | 0.6205549 |
| NMPQ_sum | RSES_sum | 0.0967666 | 0.1349705 |
| SAD_sum | RSES_sum | 0.0919870 | 0.1554243 |
| SAD_dep_sum | RSES_sum | 0.0964267 | 0.1363531 |
| SAD_ea_sum | RSES_sum | 0.0589719 | 0.3630277 |
| SAD_access_sum | RSES_sum | 0.1140016 | 0.0779619 |
| SAD_dist_sum | RSES_sum | -0.0088131 | 0.8919623 |
| BIS_sum | RSES_sum | 0.1212277 | 0.0607696 |
| NEO_O_sum | RSES_sum | -0.0508590 | 0.4328646 |
| NEO_C_sum | RSES_sum | -0.2283918 | 0.0003611 |
| NEO_E_sum | RSES_sum | -0.4019877 | 0.0000000 |
| NEO_A_sum | RSES_sum | 0.0254546 | 0.6948027 |
| NEO_N_sum | RSES_sum | 0.4484059 | 0.0000000 |
| age | SRS_sum | -0.1278829 | 0.0478225 |
| age_first_phone | SRS_sum | 0.0046487 | 0.9428873 |
| com_gen | SRS_sum | 0.2514077 | 0.0000822 |
| com_unattended | SRS_sum | 0.1244597 | 0.0541608 |
| com_leave_with_others | SRS_sum | 0.1954156 | 0.0023585 |
| com_locked | SRS_sum | -0.0078757 | 0.9033941 |
| com_room_task | SRS_sum | 0.1607153 | 0.0126662 |
| dist_daily | SRS_sum | -0.0949802 | 0.1423588 |
| dist_study | SRS_sum | -0.1962918 | 0.0022518 |
| NMPQ_sum | SRS_sum | -0.1750881 | 0.0065414 |
| SAD_sum | SRS_sum | -0.1568388 | 0.0150107 |
| SAD_dep_sum | SRS_sum | -0.1623732 | 0.0117661 |
| SAD_ea_sum | SRS_sum | -0.0705365 | 0.2764192 |
| SAD_access_sum | SRS_sum | -0.1001034 | 0.1219633 |
| SAD_dist_sum | SRS_sum | -0.1725915 | 0.0073631 |
| BIS_sum | SRS_sum | -0.4909803 | 0.0000000 |
| NEO_O_sum | SRS_sum | -0.0287322 | 0.6578487 |
| NEO_C_sum | SRS_sum | 0.3799657 | 0.0000000 |
| NEO_E_sum | SRS_sum | 0.2641515 | 0.0000340 |
| NEO_A_sum | SRS_sum | 0.0555717 | 0.3914002 |
| NEO_N_sum | SRS_sum | -0.3925108 | 0.0000000 |
| RSES_sum | SRS_sum | -0.2805139 | 0.0000102 |
| age | SSEIT_sum | 0.0712568 | 0.2715323 |
| age_first_phone | SSEIT_sum | 0.0327748 | 0.6133972 |
| com_gen | SSEIT_sum | 0.2080908 | 0.0011850 |
| com_unattended | SSEIT_sum | 0.1662271 | 0.0098884 |
| com_leave_with_others | SSEIT_sum | 0.1410947 | 0.0288623 |
| com_locked | SSEIT_sum | 0.1978097 | 0.0020774 |
| com_room_task | SSEIT_sum | 0.1253856 | 0.0523814 |
| dist_daily | SSEIT_sum | 0.2859003 | 0.0000068 |
| dist_study | SSEIT_sum | 0.0088705 | 0.8912624 |
| NMPQ_sum | SSEIT_sum | 0.1239614 | 0.0551391 |
| SAD_sum | SSEIT_sum | 0.1921045 | 0.0028043 |
| SAD_dep_sum | SSEIT_sum | 0.0704950 | 0.2767029 |
| SAD_ea_sum | SSEIT_sum | 0.1551991 | 0.0161115 |
| SAD_access_sum | SSEIT_sum | 0.2023550 | 0.0016262 |
| SAD_dist_sum | SSEIT_sum | 0.2417069 | 0.0001561 |
| BIS_sum | SSEIT_sum | -0.2918925 | 0.0000042 |
| NEO_O_sum | SSEIT_sum | 0.3667766 | 0.0000000 |
| NEO_C_sum | SSEIT_sum | 0.4292255 | 0.0000000 |
| NEO_E_sum | SSEIT_sum | 0.6081244 | 0.0000000 |
| NEO_A_sum | SSEIT_sum | 0.4076831 | 0.0000000 |
| NEO_N_sum | SSEIT_sum | -0.1474491 | 0.0223217 |
| RSES_sum | SSEIT_sum | -0.3134390 | 0.0000007 |
| SRS_sum | SSEIT_sum | 0.2389500 | 0.0001864 |
| age | DASS_D_sum | 0.0148984 | 0.8183923 |
| age_first_phone | DASS_D_sum | 0.0024662 | 0.9696826 |
| com_gen | DASS_D_sum | -0.1131201 | 0.0803051 |
| com_unattended | DASS_D_sum | -0.0037829 | 0.9535104 |
| com_leave_with_others | DASS_D_sum | -0.0885041 | 0.1717428 |
| com_locked | DASS_D_sum | -0.0699043 | 0.2807577 |
| com_room_task | DASS_D_sum | -0.1007284 | 0.1196388 |
| dist_daily | DASS_D_sum | -0.0413880 | 0.5234046 |
| dist_study | DASS_D_sum | 0.0384320 | 0.5535183 |
| NMPQ_sum | DASS_D_sum | 0.0691010 | 0.2863358 |
| SAD_sum | DASS_D_sum | 0.1322150 | 0.0407008 |
| SAD_dep_sum | DASS_D_sum | 0.0484072 | 0.4553999 |
| SAD_ea_sum | DASS_D_sum | 0.1782271 | 0.0056251 |
| SAD_access_sum | DASS_D_sum | 0.1432873 | 0.0264409 |
| SAD_dist_sum | DASS_D_sum | 0.0452892 | 0.4849813 |
| BIS_sum | DASS_D_sum | 0.2956870 | 0.0000031 |
| NEO_O_sum | DASS_D_sum | 0.0189184 | 0.7706088 |
| NEO_C_sum | DASS_D_sum | -0.2775303 | 0.0000128 |
| NEO_E_sum | DASS_D_sum | -0.2851010 | 0.0000072 |
| NEO_A_sum | DASS_D_sum | -0.1094061 | 0.0908093 |
| NEO_N_sum | DASS_D_sum | 0.4298114 | 0.0000000 |
| RSES_sum | DASS_D_sum | 0.4907444 | 0.0000000 |
| SRS_sum | DASS_D_sum | -0.3507570 | 0.0000000 |
| SSEIT_sum | DASS_D_sum | -0.2993032 | 0.0000023 |
| age | DASS_A_sum | 0.0395386 | 0.5421467 |
| age_first_phone | DASS_A_sum | -0.0290693 | 0.6540926 |
| com_gen | DASS_A_sum | -0.1922405 | 0.0027846 |
| com_unattended | DASS_A_sum | -0.0180951 | 0.7803317 |
| com_leave_with_others | DASS_A_sum | -0.1132487 | 0.0799600 |
| com_locked | DASS_A_sum | -0.1348823 | 0.0367770 |
| com_room_task | DASS_A_sum | -0.1503658 | 0.0197765 |
| dist_daily | DASS_A_sum | -0.1305603 | 0.0433079 |
| dist_study | DASS_A_sum | 0.1868945 | 0.0036621 |
| NMPQ_sum | DASS_A_sum | 0.0907227 | 0.1612071 |
| SAD_sum | DASS_A_sum | 0.0939981 | 0.1465501 |
| SAD_dep_sum | DASS_A_sum | 0.0480624 | 0.4586206 |
| SAD_ea_sum | DASS_A_sum | 0.1326045 | 0.0401067 |
| SAD_access_sum | DASS_A_sum | 0.0679631 | 0.2943643 |
| SAD_dist_sum | DASS_A_sum | 0.0614436 | 0.3432281 |
| BIS_sum | DASS_A_sum | 0.3240650 | 0.0000003 |
| NEO_O_sum | DASS_A_sum | -0.0630913 | 0.3304175 |
| NEO_C_sum | DASS_A_sum | -0.2363803 | 0.0002196 |
| NEO_E_sum | DASS_A_sum | -0.2519562 | 0.0000792 |
| NEO_A_sum | DASS_A_sum | -0.2622369 | 0.0000390 |
| NEO_N_sum | DASS_A_sum | 0.3372589 | 0.0000001 |
| RSES_sum | DASS_A_sum | 0.3130846 | 0.0000007 |
| SRS_sum | DASS_A_sum | -0.3002108 | 0.0000022 |
| SSEIT_sum | DASS_A_sum | -0.3449853 | 0.0000000 |
| DASS_D_sum | DASS_A_sum | 0.6219328 | 0.0000000 |
| age | DASS_S_sum | 0.1625733 | 0.0116614 |
| age_first_phone | DASS_S_sum | 0.0489886 | 0.4499977 |
| com_gen | DASS_S_sum | -0.0664499 | 0.3052704 |
| com_unattended | DASS_S_sum | 0.0469953 | 0.4686686 |
| com_leave_with_others | DASS_S_sum | -0.0174053 | 0.7885037 |
| com_locked | DASS_S_sum | -0.0667873 | 0.3028163 |
| com_room_task | DASS_S_sum | -0.0575602 | 0.3746487 |
| dist_daily | DASS_S_sum | -0.0354474 | 0.5847543 |
| dist_study | DASS_S_sum | 0.0951173 | 0.1417809 |
| NMPQ_sum | DASS_S_sum | 0.1495273 | 0.0204810 |
| SAD_sum | DASS_S_sum | 0.2020555 | 0.0016529 |
| SAD_dep_sum | DASS_S_sum | 0.0853075 | 0.1878117 |
| SAD_ea_sum | DASS_S_sum | 0.2536518 | 0.0000706 |
| SAD_access_sum | DASS_S_sum | 0.1705261 | 0.0081112 |
| SAD_dist_sum | DASS_S_sum | 0.1525665 | 0.0180265 |
| BIS_sum | DASS_S_sum | 0.3265384 | 0.0000002 |
| NEO_O_sum | DASS_S_sum | 0.0880843 | 0.1737929 |
| NEO_C_sum | DASS_S_sum | -0.2433805 | 0.0001400 |
| NEO_E_sum | DASS_S_sum | -0.1820056 | 0.0046758 |
| NEO_A_sum | DASS_S_sum | -0.0700556 | 0.2797154 |
| NEO_N_sum | DASS_S_sum | 0.5105424 | 0.0000000 |
| RSES_sum | DASS_S_sum | 0.3492234 | 0.0000000 |
| SRS_sum | DASS_S_sum | -0.3511598 | 0.0000000 |
| SSEIT_sum | DASS_S_sum | -0.1804562 | 0.0050461 |
| DASS_D_sum | DASS_S_sum | 0.7315613 | 0.0000000 |
| DASS_A_sum | DASS_S_sum | 0.7262390 | 0.0000000 |
# print tables using kable
kable(as.data.frame(format(sona_corr2$r, scientific = FALSE)), caption = "SONA Study - Correlation: r values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.43 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | -0.0599 | 0.0215 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0.0184 | 0.0074 | 0.4018 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 0.0118 | 0.0191 | 0.5683 | 0.6172 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 0.0069 | -0.0634 | -0.1785 | -0.1536 | -0.1951 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | -0.024 | 9e-04 | 0.3294 | 0.4199 | 0.3999 | 0.0257 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | -0.1596 | -0.1395 | 0.073 | 0.1212 | 0.0403 | 0.2036 | 0.0788 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | -0.0305 | -0.0535 | -0.1114 | -0.1456 | -0.107 | -0.0809 | -0.1515 | 0.2723 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 0.0094 | -0.0412 | -0.142 | -0.1827 | -0.1949 | 0.1766 | -0.1548 | 0.2414 | 0.2221 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 0.0275 | -0.0584 | -0.1258 | -0.1367 | -0.1913 | 0.2474 | -0.161 | 0.3377 | 0.1792 | 0.7625 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 0.0567 | -0.0351 | -0.1621 | -0.1589 | -0.2325 | 0.2084 | -0.2362 | 0.2599 | 0.1383 | 0.6813 | 0.8511 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 0.0483 | -0.0304 | -0.0853 | -0.1453 | -0.1111 | 0.1897 | -0.0802 | 0.2087 | 0.1269 | 0.5727 | 0.8218 | 0.5384 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | -0.004 | -0.1122 | -0.0956 | -0.0731 | -0.154 | 0.1997 | -0.1059 | 0.2788 | 0.137 | 0.6631 | 0.8084 | 0.606 | 0.5515 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | -0.0369 | 0.0173 | -0.0447 | -0.0162 | -0.0677 | 0.1928 | -0.0765 | 0.3818 | 0.1757 | 0.4194 | 0.6586 | 0.4259 | 0.5277 | 0.3681 | 1 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0.024 | -0.0619 | -0.083 | -0.0297 | -0.0177 | -0.0941 | -0.0157 | 0.0473 | 0.2363 | 0.0346 | 0.0826 | 0.0915 | 0.0456 | 0.0198 | 0.1079 | 1 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.0106 | 0.0464 | 0.065 | 0.0756 | 0.0448 | 0.1023 | 0.1219 | 0.2223 | -0.0361 | -0.0605 | -0.0187 | -0.1477 | -0.0269 | 0.07 | 0.1324 | 0.0254 | 1 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.0386 | 0.0379 | 0.048 | 0.0754 | 0.0341 | 0.119 | 0.0273 | 5e-04 | -0.107 | -0.0029 | -0.0336 | -0.091 | -0.0162 | 0.0499 | -0.0024 | -0.6875 | 0.0456 | 1 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | -0.0951 | -0.1163 | 0.2433 | 0.149 | 0.1915 | 0.1226 | 0.1369 | 0.2051 | 0.0382 | 0.0323 | 0.0369 | -0.0447 | 0.0846 | 0.0443 | 0.1107 | -0.2005 | 0.3003 | 0.409 | 1 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.1464 | 0.1244 | 0.2239 | 0.1199 | 0.1781 | 0.0925 | 0.1718 | 0.1637 | -0.1541 | 0.0376 | -0.0137 | -0.0538 | -0.0764 | 0.0772 | 0.0362 | -0.2753 | 0.307 | 0.3608 | 0.2104 | 1 | - | - | - | - | - | - | - |
| NEO_N_sum | 0.1412 | 0.0824 | -0.0806 | 0.0035 | -0.052 | -0.0024 | -0.0258 | 0.0651 | 0.0898 | 0.2439 | 0.2366 | 0.1819 | 0.1627 | 0.2412 | 0.1598 | 0.3392 | 0.0323 | -0.3169 | -0.332 | 0.0474 | 1 | - | - | - | - | - | - |
| RSES_sum | -0.0259 | 0.0102 | -0.1097 | -0.0542 | -0.0253 | -0.1349 | -0.0595 | -0.0417 | -0.0321 | 0.0968 | 0.092 | 0.0964 | 0.059 | 0.114 | -0.0088 | 0.1212 | -0.0509 | -0.2284 | -0.402 | 0.0255 | 0.4484 | 1 | - | - | - | - | - |
| SRS_sum | -0.1279 | 0.0046 | 0.2514 | 0.1245 | 0.1954 | -0.0079 | 0.1607 | -0.095 | -0.1963 | -0.1751 | -0.1568 | -0.1624 | -0.0705 | -0.1001 | -0.1726 | -0.491 | -0.0287 | 0.38 | 0.2642 | 0.0556 | -0.3925 | -0.2805 | 1 | - | - | - | - |
| SSEIT_sum | 0.0713 | 0.0328 | 0.2081 | 0.1662 | 0.1411 | 0.1978 | 0.1254 | 0.2859 | 0.0089 | 0.124 | 0.1921 | 0.0705 | 0.1552 | 0.2024 | 0.2417 | -0.2919 | 0.3668 | 0.4292 | 0.6081 | 0.4077 | -0.1474 | -0.3134 | 0.2389 | 1 | - | - | - |
| DASS_D_sum | 0.0149 | 0.0025 | -0.1131 | -0.0038 | -0.0885 | -0.0699 | -0.1007 | -0.0414 | 0.0384 | 0.0691 | 0.1322 | 0.0484 | 0.1782 | 0.1433 | 0.0453 | 0.2957 | 0.0189 | -0.2775 | -0.2851 | -0.1094 | 0.4298 | 0.4907 | -0.3508 | -0.2993 | 1 | - | - |
| DASS_A_sum | 0.0395 | -0.0291 | -0.1922 | -0.0181 | -0.1132 | -0.1349 | -0.1504 | -0.1306 | 0.1869 | 0.0907 | 0.094 | 0.0481 | 0.1326 | 0.068 | 0.0614 | 0.3241 | -0.0631 | -0.2364 | -0.252 | -0.2622 | 0.3373 | 0.3131 | -0.3002 | -0.345 | 0.6219 | 1 | - |
| DASS_S_sum | 0.1626 | 0.049 | -0.0664 | 0.047 | -0.0174 | -0.0668 | -0.0576 | -0.0354 | 0.0951 | 0.1495 | 0.2021 | 0.0853 | 0.2537 | 0.1705 | 0.1526 | 0.3265 | 0.0881 | -0.2434 | -0.182 | -0.0701 | 0.5105 | 0.3492 | -0.3512 | -0.1805 | 0.7316 | 0.7262 | 1 |
kable(as.data.frame(format(sona_corr2$P, scientific = FALSE)), caption = "SONA Study - Correlation: p values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 0.3554 | 0.74 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0.7772 | 0.909 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 0.856 | 0.7682 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 0.9152 | 0.3282 | 0.0056 | 0.0172 | 0.0024 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 0.7111 | 0.9895 | 0 | 0 | 0 | 0.6916 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 0.0133 | 0.0308 | 0.2597 | 0.0609 | 0.5347 | 0.0015 | 0.2239 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 0.6383 | 0.409 | 0.0851 | 0.0241 | 0.098 | 0.2119 | 0.0189 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 0.8849 | 0.5258 | 0.0278 | 0.0045 | 0.0024 | 0.0061 | 0.0164 | 2e-04 | 5e-04 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 0.6719 | 0.368 | 0.0515 | 0.0342 | 0.0029 | 1e-04 | 0.0125 | 0 | 0.0054 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 0.3815 | 0.5885 | 0.0119 | 0.0137 | 3e-04 | 0.0012 | 2e-04 | 0 | 0.0322 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 0.4565 | 0.6396 | 0.1877 | 0.0244 | 0.086 | 0.0032 | 0.2156 | 0.0011 | 0.0495 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 0.9505 | 0.0828 | 0.1397 | 0.2595 | 0.017 | 0.0019 | 0.1016 | 0 | 0.0339 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 0.5691 | 0.7896 | 0.4904 | 0.803 | 0.296 | 0.0027 | 0.2378 | 0 | 0.0064 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0.7112 | 0.3396 | 0.1999 | 0.6473 | 0.7845 | 0.146 | 0.809 | 0.4659 | 2e-04 | 0.5933 | 0.2023 | 0.1576 | 0.4823 | 0.7608 | 0.0952 | NA | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.8701 | 0.4741 | 0.3159 | 0.2433 | 0.4894 | 0.1139 | 0.0593 | 5e-04 | 0.5778 | 0.3504 | 0.7735 | 0.0221 | 0.6785 | 0.2803 | 0.0404 | 0.6952 | NA | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.552 | 0.5594 | 0.4597 | 0.2445 | 0.5995 | 0.0658 | 0.6734 | 0.9941 | 0.0982 | 0.9639 | 0.605 | 0.16 | 0.8033 | 0.4417 | 0.971 | 0 | 0.4823 | NA | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 0.1418 | 0.0722 | 1e-04 | 0.0209 | 0.0029 | 0.0578 | 0.034 | 0.0014 | 0.5559 | 0.618 | 0.5695 | 0.4907 | 0.1917 | 0.4947 | 0.0871 | 0.0018 | 0 | 0 | NA | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.0233 | 0.0544 | 5e-04 | 0.0637 | 0.0057 | 0.1533 | 0.0076 | 0.0111 | 0.0169 | 0.5621 | 0.8322 | 0.4068 | 0.2383 | 0.2337 | 0.5765 | 0 | 0 | 0 | 0.001 | NA | - | - | - | - | - | - | - |
| NEO_N_sum | 0.0288 | 0.2035 | 0.2137 | 0.9564 | 0.4223 | 0.9704 | 0.6904 | 0.315 | 0.1657 | 1e-04 | 2e-04 | 0.0047 | 0.0116 | 2e-04 | 0.0132 | 0 | 0.6189 | 0 | 0 | 0.4646 | NA | - | - | - | - | - | - |
| RSES_sum | 0.6897 | 0.8751 | 0.0901 | 0.4033 | 0.6963 | 0.0367 | 0.359 | 0.52 | 0.6206 | 0.135 | 0.1554 | 0.1364 | 0.363 | 0.078 | 0.892 | 0.0608 | 0.4329 | 4e-04 | 0 | 0.6948 | 0 | NA | - | - | - | - | - |
| SRS_sum | 0.0478 | 0.9429 | 1e-04 | 0.0542 | 0.0024 | 0.9034 | 0.0127 | 0.1424 | 0.0023 | 0.0065 | 0.015 | 0.0118 | 0.2764 | 0.122 | 0.0074 | 0 | 0.6578 | 0 | 0 | 0.3914 | 0 | 0 | NA | - | - | - | - |
| SSEIT_sum | 0.2715 | 0.6134 | 0.0012 | 0.0099 | 0.0289 | 0.0021 | 0.0524 | 0 | 0.8913 | 0.0551 | 0.0028 | 0.2767 | 0.0161 | 0.0016 | 2e-04 | 0 | 0 | 0 | 0 | 0 | 0.0223 | 0 | 2e-04 | NA | - | - | - |
| DASS_D_sum | 0.8184 | 0.9697 | 0.0803 | 0.9535 | 0.1717 | 0.2808 | 0.1196 | 0.5234 | 0.5535 | 0.2863 | 0.0407 | 0.4554 | 0.0056 | 0.0264 | 0.485 | 0 | 0.7706 | 0 | 0 | 0.0908 | 0 | 0 | 0 | 0 | NA | - | - |
| DASS_A_sum | 0.5421 | 0.6541 | 0.0028 | 0.7803 | 0.08 | 0.0368 | 0.0198 | 0.0433 | 0.0037 | 0.1612 | 0.1466 | 0.4586 | 0.0401 | 0.2944 | 0.3432 | 0 | 0.3304 | 2e-04 | 1e-04 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - |
| DASS_S_sum | 0.0117 | 0.45 | 0.3053 | 0.4687 | 0.7885 | 0.3028 | 0.3746 | 0.5848 | 0.1418 | 0.0205 | 0.0017 | 0.1878 | 1e-04 | 0.0081 | 0.018 | 0 | 0.1738 | 1e-04 | 0.0047 | 0.2797 | 0 | 0 | 0 | 0.005 | 0 | 0 | NA |
kable(as.data.frame(format(sona_corr2$n, scientific = FALSE)), caption = "SONA Study - Correlation: n values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - | - |
| NEO_N_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - | - |
| RSES_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - | - |
| SRS_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - | - |
| SSEIT_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - | - |
| DASS_D_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - | - |
| DASS_A_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | - |
| DASS_S_sum | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 | 240 |
# create new matrix for r & p vals with different col names
# get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
sona_corr_r <- sona_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
sona_corr_p <- sona_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")
# all vars included
corr_plot_sona_all <- {
corrplot(sona_corr$r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_corr$P, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .5, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# with all SAD subscales
corr_plot_sona_ques_all <- {
corrplot(sona_corr_r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_corr_p, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# final -- with SAD overall only
corr_plot_sona_ques_final <- {
corrplot(sona_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_all(oct28)", plot = replayPlot(corr_plot_sona_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ques_all(oct28)", plot = replayPlot(corr_plot_sona_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ques_final(oct28)", plot = replayPlot(corr_plot_sona_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
# export csv files of R values & p values
write.csv(sona_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_corr_rvals(oct28).csv", row.names = T)
write.csv(sona_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_corr_pvals(oct28).csv", row.names = T)Study 2: Mturk
mturk_corr <-
Mturk_data %>%
# select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study,
NMPQ_sum,
SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>%
as.matrix() %>%
Hmisc::rcorr(type = c("pearson"))
# create new pilot_corr to shown only lower triangle...
mturk_corr2 <- mturk_corr
# round to 4 decimals...
mturk_corr2$r <- round(mturk_corr2$r, 4)
mturk_corr2$P <- round(mturk_corr2$P, 4)
mturk_corr2$n <- round(mturk_corr2$n, 4)
# remove upper triangle form r, p, and n
mturk_corr2$r[upper.tri(mturk_corr2$r)] <- "-"
mturk_corr2$P[upper.tri(mturk_corr2$P)] <- "-"
mturk_corr2$n[upper.tri(mturk_corr2$n)] <- "-"
# show corr table with flattenCorr
kable(flattenCorrMatrix(mturk_corr$r, mturk_corr$P), caption = "MTURK Study - Correlation: Flat") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| row | column | cor | p |
|---|---|---|---|
| age | age_first_phone | 0.1152701 | 0.0095253 |
| age | com_gen | -0.1992789 | 0.0000064 |
| age_first_phone | com_gen | -0.0207049 | 0.6425176 |
| age | com_unattended | -0.2682833 | 0.0000000 |
| age_first_phone | com_unattended | -0.0277585 | 0.5336988 |
| com_gen | com_unattended | 0.6317465 | 0.0000000 |
| age | com_leave_with_others | -0.2622209 | 0.0000000 |
| age_first_phone | com_leave_with_others | -0.0245583 | 0.5819123 |
| com_gen | com_leave_with_others | 0.6179141 | 0.0000000 |
| com_unattended | com_leave_with_others | 0.7350103 | 0.0000000 |
| age | com_locked | -0.1409302 | 0.0014982 |
| age_first_phone | com_locked | 0.0210407 | 0.6371326 |
| com_gen | com_locked | -0.0955808 | 0.0317528 |
| com_unattended | com_locked | -0.1958468 | 0.0000093 |
| com_leave_with_others | com_locked | -0.1590704 | 0.0003324 |
| age | com_room_task | -0.1115064 | 0.0121620 |
| age_first_phone | com_room_task | -0.0392236 | 0.3790793 |
| com_gen | com_room_task | 0.5067979 | 0.0000000 |
| com_unattended | com_room_task | 0.5964662 | 0.0000000 |
| com_leave_with_others | com_room_task | 0.5570108 | 0.0000000 |
| com_locked | com_room_task | -0.1422099 | 0.0013546 |
| age | dist_daily | -0.3131918 | 0.0000000 |
| age_first_phone | dist_daily | 0.0041442 | 0.9259842 |
| com_gen | dist_daily | 0.3412001 | 0.0000000 |
| com_unattended | dist_daily | 0.3323936 | 0.0000000 |
| com_leave_with_others | dist_daily | 0.3478189 | 0.0000000 |
| com_locked | dist_daily | 0.1118473 | 0.0118991 |
| com_room_task | dist_daily | 0.2881640 | 0.0000000 |
| age | dist_study | -0.3420285 | 0.0000000 |
| age_first_phone | dist_study | 0.0239547 | 0.5912273 |
| com_gen | dist_study | 0.4514006 | 0.0000000 |
| com_unattended | dist_study | 0.5416987 | 0.0000000 |
| com_leave_with_others | dist_study | 0.5861214 | 0.0000000 |
| com_locked | dist_study | 0.0706124 | 0.1129975 |
| com_room_task | dist_study | 0.3830527 | 0.0000000 |
| dist_daily | dist_study | 0.4700815 | 0.0000000 |
| age | NMPQ_sum | -0.2567735 | 0.0000000 |
| age_first_phone | NMPQ_sum | 0.0065218 | 0.8837656 |
| com_gen | NMPQ_sum | 0.2623418 | 0.0000000 |
| com_unattended | NMPQ_sum | 0.2545792 | 0.0000000 |
| com_leave_with_others | NMPQ_sum | 0.3254167 | 0.0000000 |
| com_locked | NMPQ_sum | 0.2452234 | 0.0000000 |
| com_room_task | NMPQ_sum | 0.1689246 | 0.0001367 |
| dist_daily | NMPQ_sum | 0.5175468 | 0.0000000 |
| dist_study | NMPQ_sum | 0.5143775 | 0.0000000 |
| age | SAD_sum | -0.2976407 | 0.0000000 |
| age_first_phone | SAD_sum | -0.0016625 | 0.9702710 |
| com_gen | SAD_sum | 0.3296294 | 0.0000000 |
| com_unattended | SAD_sum | 0.2689651 | 0.0000000 |
| com_leave_with_others | SAD_sum | 0.3434096 | 0.0000000 |
| com_locked | SAD_sum | 0.2139692 | 0.0000012 |
| com_room_task | SAD_sum | 0.1795750 | 0.0000494 |
| dist_daily | SAD_sum | 0.5376784 | 0.0000000 |
| dist_study | SAD_sum | 0.5286169 | 0.0000000 |
| NMPQ_sum | SAD_sum | 0.8691721 | 0.0000000 |
| age | SAD_dep_sum | -0.2448318 | 0.0000000 |
| age_first_phone | SAD_dep_sum | 0.0082084 | 0.8540087 |
| com_gen | SAD_dep_sum | 0.2948498 | 0.0000000 |
| com_unattended | SAD_dep_sum | 0.2252527 | 0.0000003 |
| com_leave_with_others | SAD_dep_sum | 0.2787308 | 0.0000000 |
| com_locked | SAD_dep_sum | 0.1631873 | 0.0002307 |
| com_room_task | SAD_dep_sum | 0.1262124 | 0.0045027 |
| dist_daily | SAD_dep_sum | 0.4716996 | 0.0000000 |
| dist_study | SAD_dep_sum | 0.4556352 | 0.0000000 |
| NMPQ_sum | SAD_dep_sum | 0.8122230 | 0.0000000 |
| SAD_sum | SAD_dep_sum | 0.9161922 | 0.0000000 |
| age | SAD_ea_sum | -0.3223664 | 0.0000000 |
| age_first_phone | SAD_ea_sum | 0.0016167 | 0.9710907 |
| com_gen | SAD_ea_sum | 0.3694184 | 0.0000000 |
| com_unattended | SAD_ea_sum | 0.3186166 | 0.0000000 |
| com_leave_with_others | SAD_ea_sum | 0.3681343 | 0.0000000 |
| com_locked | SAD_ea_sum | 0.1828880 | 0.0000356 |
| com_room_task | SAD_ea_sum | 0.2065290 | 0.0000029 |
| dist_daily | SAD_ea_sum | 0.4707792 | 0.0000000 |
| dist_study | SAD_ea_sum | 0.5285391 | 0.0000000 |
| NMPQ_sum | SAD_ea_sum | 0.7623385 | 0.0000000 |
| SAD_sum | SAD_ea_sum | 0.9032741 | 0.0000000 |
| SAD_dep_sum | SAD_ea_sum | 0.7437688 | 0.0000000 |
| age | SAD_access_sum | -0.1625391 | 0.0002445 |
| age_first_phone | SAD_access_sum | -0.0081724 | 0.8546415 |
| com_gen | SAD_access_sum | 0.1547900 | 0.0004814 |
| com_unattended | SAD_access_sum | 0.1006440 | 0.0237078 |
| com_leave_with_others | SAD_access_sum | 0.1668916 | 0.0001648 |
| com_locked | SAD_access_sum | 0.2472758 | 0.0000000 |
| com_room_task | SAD_access_sum | 0.0370379 | 0.4062309 |
| dist_daily | SAD_access_sum | 0.4188661 | 0.0000000 |
| dist_study | SAD_access_sum | 0.3205921 | 0.0000000 |
| NMPQ_sum | SAD_access_sum | 0.7600923 | 0.0000000 |
| SAD_sum | SAD_access_sum | 0.8529878 | 0.0000000 |
| SAD_dep_sum | SAD_access_sum | 0.7722094 | 0.0000000 |
| SAD_ea_sum | SAD_access_sum | 0.6466162 | 0.0000000 |
| age | SAD_dist_sum | -0.3470620 | 0.0000000 |
| age_first_phone | SAD_dist_sum | -0.0202373 | 0.6500480 |
| com_gen | SAD_dist_sum | 0.3863118 | 0.0000000 |
| com_unattended | SAD_dist_sum | 0.3517954 | 0.0000000 |
| com_leave_with_others | SAD_dist_sum | 0.4506256 | 0.0000000 |
| com_locked | SAD_dist_sum | 0.1031158 | 0.0204668 |
| com_room_task | SAD_dist_sum | 0.3177774 | 0.0000000 |
| dist_daily | SAD_dist_sum | 0.5269001 | 0.0000000 |
| dist_study | SAD_dist_sum | 0.6153073 | 0.0000000 |
| NMPQ_sum | SAD_dist_sum | 0.6443373 | 0.0000000 |
| SAD_sum | SAD_dist_sum | 0.7888812 | 0.0000000 |
| SAD_dep_sum | SAD_dist_sum | 0.6389310 | 0.0000000 |
| SAD_ea_sum | SAD_dist_sum | 0.7346070 | 0.0000000 |
| SAD_access_sum | SAD_dist_sum | 0.5137395 | 0.0000000 |
| age | BIS_sum | -0.2431553 | 0.0000000 |
| age_first_phone | BIS_sum | -0.0082234 | 0.8537438 |
| com_gen | BIS_sum | 0.2232944 | 0.0000004 |
| com_unattended | BIS_sum | 0.4291903 | 0.0000000 |
| com_leave_with_others | BIS_sum | 0.4516855 | 0.0000000 |
| com_locked | BIS_sum | -0.1325832 | 0.0028335 |
| com_room_task | BIS_sum | 0.2888952 | 0.0000000 |
| dist_daily | BIS_sum | 0.3219655 | 0.0000000 |
| dist_study | BIS_sum | 0.5275567 | 0.0000000 |
| NMPQ_sum | BIS_sum | 0.2789264 | 0.0000000 |
| SAD_sum | BIS_sum | 0.3288891 | 0.0000000 |
| SAD_dep_sum | BIS_sum | 0.2784614 | 0.0000000 |
| SAD_ea_sum | BIS_sum | 0.2982723 | 0.0000000 |
| SAD_access_sum | BIS_sum | 0.1768891 | 0.0000642 |
| SAD_dist_sum | BIS_sum | 0.4885262 | 0.0000000 |
| age | NEO_O_sum | 0.1331245 | 0.0027217 |
| age_first_phone | NEO_O_sum | 0.0291156 | 0.5138796 |
| com_gen | NEO_O_sum | -0.2770840 | 0.0000000 |
| com_unattended | NEO_O_sum | -0.3688361 | 0.0000000 |
| com_leave_with_others | NEO_O_sum | -0.3544968 | 0.0000000 |
| com_locked | NEO_O_sum | 0.0850217 | 0.0562172 |
| com_room_task | NEO_O_sum | -0.2486216 | 0.0000000 |
| dist_daily | NEO_O_sum | -0.1401570 | 0.0015917 |
| dist_study | NEO_O_sum | -0.4660624 | 0.0000000 |
| NMPQ_sum | NEO_O_sum | -0.2138432 | 0.0000012 |
| SAD_sum | NEO_O_sum | -0.2624147 | 0.0000000 |
| SAD_dep_sum | NEO_O_sum | -0.2390357 | 0.0000001 |
| SAD_ea_sum | NEO_O_sum | -0.2699038 | 0.0000000 |
| SAD_access_sum | NEO_O_sum | -0.1280221 | 0.0039557 |
| SAD_dist_sum | NEO_O_sum | -0.3525210 | 0.0000000 |
| BIS_sum | NEO_O_sum | -0.3689202 | 0.0000000 |
| age | NEO_C_sum | 0.3149069 | 0.0000000 |
| age_first_phone | NEO_C_sum | 0.0209876 | 0.6379831 |
| com_gen | NEO_C_sum | -0.1810588 | 0.0000427 |
| com_unattended | NEO_C_sum | -0.3043721 | 0.0000000 |
| com_leave_with_others | NEO_C_sum | -0.3324210 | 0.0000000 |
| com_locked | NEO_C_sum | 0.1444843 | 0.0011302 |
| com_room_task | NEO_C_sum | -0.2254174 | 0.0000003 |
| dist_daily | NEO_C_sum | -0.2576367 | 0.0000000 |
| dist_study | NEO_C_sum | -0.4369764 | 0.0000000 |
| NMPQ_sum | NEO_C_sum | -0.0920886 | 0.0385740 |
| SAD_sum | NEO_C_sum | -0.1613790 | 0.0002711 |
| SAD_dep_sum | NEO_C_sum | -0.1229692 | 0.0056566 |
| SAD_ea_sum | NEO_C_sum | -0.1683429 | 0.0001442 |
| SAD_access_sum | NEO_C_sum | 0.0084537 | 0.8496958 |
| SAD_dist_sum | NEO_C_sum | -0.3949571 | 0.0000000 |
| BIS_sum | NEO_C_sum | -0.6698270 | 0.0000000 |
| NEO_O_sum | NEO_C_sum | 0.3036323 | 0.0000000 |
| age | NEO_E_sum | -0.0974948 | 0.0284725 |
| age_first_phone | NEO_E_sum | -0.0277797 | 0.5333873 |
| com_gen | NEO_E_sum | 0.3068363 | 0.0000000 |
| com_unattended | NEO_E_sum | 0.1376061 | 0.0019391 |
| com_leave_with_others | NEO_E_sum | 0.1578014 | 0.0003713 |
| com_locked | NEO_E_sum | 0.1298914 | 0.0034544 |
| com_room_task | NEO_E_sum | 0.1027269 | 0.0209496 |
| dist_daily | NEO_E_sum | 0.0756853 | 0.0893118 |
| dist_study | NEO_E_sum | 0.2664280 | 0.0000000 |
| NMPQ_sum | NEO_E_sum | 0.3023222 | 0.0000000 |
| SAD_sum | NEO_E_sum | 0.2957975 | 0.0000000 |
| SAD_dep_sum | NEO_E_sum | 0.2482318 | 0.0000000 |
| SAD_ea_sum | NEO_E_sum | 0.3412425 | 0.0000000 |
| SAD_access_sum | NEO_E_sum | 0.2270591 | 0.0000003 |
| SAD_dist_sum | NEO_E_sum | 0.2186135 | 0.0000007 |
| BIS_sum | NEO_E_sum | -0.0533212 | 0.2316474 |
| NEO_O_sum | NEO_E_sum | -0.0714501 | 0.1087775 |
| NEO_C_sum | NEO_E_sum | 0.2783141 | 0.0000000 |
| age | NEO_A_sum | 0.3499926 | 0.0000000 |
| age_first_phone | NEO_A_sum | 0.0095476 | 0.8305254 |
| com_gen | NEO_A_sum | -0.2731724 | 0.0000000 |
| com_unattended | NEO_A_sum | -0.3896453 | 0.0000000 |
| com_leave_with_others | NEO_A_sum | -0.4210423 | 0.0000000 |
| com_locked | NEO_A_sum | 0.0578119 | 0.1946223 |
| com_room_task | NEO_A_sum | -0.1984662 | 0.0000070 |
| dist_daily | NEO_A_sum | -0.2112281 | 0.0000017 |
| dist_study | NEO_A_sum | -0.5851432 | 0.0000000 |
| NMPQ_sum | NEO_A_sum | -0.2647259 | 0.0000000 |
| SAD_sum | NEO_A_sum | -0.3044065 | 0.0000000 |
| SAD_dep_sum | NEO_A_sum | -0.2514306 | 0.0000000 |
| SAD_ea_sum | NEO_A_sum | -0.3340294 | 0.0000000 |
| SAD_access_sum | NEO_A_sum | -0.1347004 | 0.0024186 |
| SAD_dist_sum | NEO_A_sum | -0.4440743 | 0.0000000 |
| BIS_sum | NEO_A_sum | -0.5432258 | 0.0000000 |
| NEO_O_sum | NEO_A_sum | 0.5151080 | 0.0000000 |
| NEO_C_sum | NEO_A_sum | 0.5829223 | 0.0000000 |
| NEO_E_sum | NEO_A_sum | -0.0809224 | 0.0692202 |
| age | NEO_N_sum | -0.2397601 | 0.0000000 |
| age_first_phone | NEO_N_sum | 0.0286193 | 0.5210836 |
| com_gen | NEO_N_sum | 0.1276778 | 0.0040549 |
| com_unattended | NEO_N_sum | 0.3120163 | 0.0000000 |
| com_leave_with_others | NEO_N_sum | 0.3427017 | 0.0000000 |
| com_locked | NEO_N_sum | -0.0033936 | 0.9393610 |
| com_room_task | NEO_N_sum | 0.2590695 | 0.0000000 |
| dist_daily | NEO_N_sum | 0.3727406 | 0.0000000 |
| dist_study | NEO_N_sum | 0.4382150 | 0.0000000 |
| NMPQ_sum | NEO_N_sum | 0.3401242 | 0.0000000 |
| SAD_sum | NEO_N_sum | 0.3845639 | 0.0000000 |
| SAD_dep_sum | NEO_N_sum | 0.3377203 | 0.0000000 |
| SAD_ea_sum | NEO_N_sum | 0.3191194 | 0.0000000 |
| SAD_access_sum | NEO_N_sum | 0.2560302 | 0.0000000 |
| SAD_dist_sum | NEO_N_sum | 0.4762090 | 0.0000000 |
| BIS_sum | NEO_N_sum | 0.5765723 | 0.0000000 |
| NEO_O_sum | NEO_N_sum | -0.2903720 | 0.0000000 |
| NEO_C_sum | NEO_N_sum | -0.6089177 | 0.0000000 |
| NEO_E_sum | NEO_N_sum | -0.3294030 | 0.0000000 |
| NEO_A_sum | NEO_N_sum | -0.4461611 | 0.0000000 |
| age | RSES_sum | -0.2166900 | 0.0000009 |
| age_first_phone | RSES_sum | -0.0463808 | 0.2982211 |
| com_gen | RSES_sum | 0.1271870 | 0.0042002 |
| com_unattended | RSES_sum | 0.1462151 | 0.0009830 |
| com_leave_with_others | RSES_sum | 0.1439597 | 0.0011787 |
| com_locked | RSES_sum | -0.0141026 | 0.7518907 |
| com_room_task | RSES_sum | 0.1664290 | 0.0001720 |
| dist_daily | RSES_sum | 0.0844968 | 0.0577594 |
| dist_study | RSES_sum | 0.2059450 | 0.0000031 |
| NMPQ_sum | RSES_sum | 0.1023403 | 0.0214395 |
| SAD_sum | RSES_sum | 0.1460603 | 0.0009954 |
| SAD_dep_sum | RSES_sum | 0.1173804 | 0.0082810 |
| SAD_ea_sum | RSES_sum | 0.1591752 | 0.0003293 |
| SAD_access_sum | RSES_sum | 0.0573266 | 0.1984010 |
| SAD_dist_sum | RSES_sum | 0.2166912 | 0.0000009 |
| BIS_sum | RSES_sum | 0.2625560 | 0.0000000 |
| NEO_O_sum | RSES_sum | -0.1005793 | 0.0237982 |
| NEO_C_sum | RSES_sum | -0.3249502 | 0.0000000 |
| NEO_E_sum | RSES_sum | -0.1528406 | 0.0005681 |
| NEO_A_sum | RSES_sum | -0.2379877 | 0.0000001 |
| NEO_N_sum | RSES_sum | 0.3857729 | 0.0000000 |
| age | SRS_sum | 0.2904433 | 0.0000000 |
| age_first_phone | SRS_sum | 0.0130523 | 0.7698290 |
| com_gen | SRS_sum | -0.1186133 | 0.0076231 |
| com_unattended | SRS_sum | -0.1922309 | 0.0000136 |
| com_leave_with_others | SRS_sum | -0.2423433 | 0.0000000 |
| com_locked | SRS_sum | 0.0858012 | 0.0539897 |
| com_room_task | SRS_sum | -0.1568199 | 0.0004043 |
| dist_daily | SRS_sum | -0.3238002 | 0.0000000 |
| dist_study | SRS_sum | -0.3904458 | 0.0000000 |
| NMPQ_sum | SRS_sum | -0.2522255 | 0.0000000 |
| SAD_sum | SRS_sum | -0.3485457 | 0.0000000 |
| SAD_dep_sum | SRS_sum | -0.2844839 | 0.0000000 |
| SAD_ea_sum | SRS_sum | -0.3252558 | 0.0000000 |
| SAD_access_sum | SRS_sum | -0.1598778 | 0.0003096 |
| SAD_dist_sum | SRS_sum | -0.5386157 | 0.0000000 |
| BIS_sum | SRS_sum | -0.6122693 | 0.0000000 |
| NEO_O_sum | SRS_sum | 0.2777230 | 0.0000000 |
| NEO_C_sum | SRS_sum | 0.6469792 | 0.0000000 |
| NEO_E_sum | SRS_sum | 0.2538790 | 0.0000000 |
| NEO_A_sum | SRS_sum | 0.4803300 | 0.0000000 |
| NEO_N_sum | SRS_sum | -0.6544501 | 0.0000000 |
| RSES_sum | SRS_sum | -0.3492303 | 0.0000000 |
| age | SSEIT_sum | 0.0684685 | 0.1243844 |
| age_first_phone | SSEIT_sum | 0.0249982 | 0.5751655 |
| com_gen | SSEIT_sum | 0.1281130 | 0.0039298 |
| com_unattended | SSEIT_sum | 0.0019913 | 0.9643960 |
| com_leave_with_others | SSEIT_sum | 0.0069607 | 0.8760050 |
| com_locked | SSEIT_sum | 0.1903230 | 0.0000166 |
| com_room_task | SSEIT_sum | 0.0137440 | 0.7580003 |
| dist_daily | SSEIT_sum | 0.0577066 | 0.1954373 |
| dist_study | SSEIT_sum | -0.0480549 | 0.2811021 |
| NMPQ_sum | SSEIT_sum | 0.2396931 | 0.0000000 |
| SAD_sum | SSEIT_sum | 0.2175114 | 0.0000008 |
| SAD_dep_sum | SSEIT_sum | 0.1685359 | 0.0001417 |
| SAD_ea_sum | SSEIT_sum | 0.2447649 | 0.0000000 |
| SAD_access_sum | SSEIT_sum | 0.2261382 | 0.0000003 |
| SAD_dist_sum | SSEIT_sum | 0.0322182 | 0.4700435 |
| BIS_sum | SSEIT_sum | -0.3259352 | 0.0000000 |
| NEO_O_sum | SSEIT_sum | 0.2500836 | 0.0000000 |
| NEO_C_sum | SSEIT_sum | 0.5797669 | 0.0000000 |
| NEO_E_sum | SSEIT_sum | 0.5447708 | 0.0000000 |
| NEO_A_sum | SSEIT_sum | 0.3882565 | 0.0000000 |
| NEO_N_sum | SSEIT_sum | -0.4071724 | 0.0000000 |
| RSES_sum | SSEIT_sum | -0.2340695 | 0.0000001 |
| SRS_sum | SSEIT_sum | 0.5027334 | 0.0000000 |
| age | DASS_D_sum | -0.3413734 | 0.0000000 |
| age_first_phone | DASS_D_sum | -0.0457253 | 0.3051095 |
| com_gen | DASS_D_sum | 0.3765500 | 0.0000000 |
| com_unattended | DASS_D_sum | 0.4610583 | 0.0000000 |
| com_leave_with_others | DASS_D_sum | 0.4943770 | 0.0000000 |
| com_locked | DASS_D_sum | 0.0940061 | 0.0346891 |
| com_room_task | DASS_D_sum | 0.3467990 | 0.0000000 |
| dist_daily | DASS_D_sum | 0.4320280 | 0.0000000 |
| dist_study | DASS_D_sum | 0.6557449 | 0.0000000 |
| NMPQ_sum | DASS_D_sum | 0.4653782 | 0.0000000 |
| SAD_sum | DASS_D_sum | 0.5168254 | 0.0000000 |
| SAD_dep_sum | DASS_D_sum | 0.4348713 | 0.0000000 |
| SAD_ea_sum | DASS_D_sum | 0.4974917 | 0.0000000 |
| SAD_access_sum | DASS_D_sum | 0.3269694 | 0.0000000 |
| SAD_dist_sum | DASS_D_sum | 0.6182126 | 0.0000000 |
| BIS_sum | DASS_D_sum | 0.5720959 | 0.0000000 |
| NEO_O_sum | DASS_D_sum | -0.4136964 | 0.0000000 |
| NEO_C_sum | DASS_D_sum | -0.5794489 | 0.0000000 |
| NEO_E_sum | DASS_D_sum | 0.0063608 | 0.8866152 |
| NEO_A_sum | DASS_D_sum | -0.5979259 | 0.0000000 |
| NEO_N_sum | DASS_D_sum | 0.6680898 | 0.0000000 |
| RSES_sum | DASS_D_sum | 0.4263575 | 0.0000000 |
| SRS_sum | DASS_D_sum | -0.5427926 | 0.0000000 |
| SSEIT_sum | DASS_D_sum | -0.2217423 | 0.0000005 |
| age | DASS_A_sum | -0.3652333 | 0.0000000 |
| age_first_phone | DASS_A_sum | -0.0362944 | 0.4157241 |
| com_gen | DASS_A_sum | 0.4411488 | 0.0000000 |
| com_unattended | DASS_A_sum | 0.4956217 | 0.0000000 |
| com_leave_with_others | DASS_A_sum | 0.5642206 | 0.0000000 |
| com_locked | DASS_A_sum | 0.0807031 | 0.0699803 |
| com_room_task | DASS_A_sum | 0.3529379 | 0.0000000 |
| dist_daily | DASS_A_sum | 0.4236448 | 0.0000000 |
| dist_study | DASS_A_sum | 0.7421144 | 0.0000000 |
| NMPQ_sum | DASS_A_sum | 0.5294467 | 0.0000000 |
| SAD_sum | DASS_A_sum | 0.5928547 | 0.0000000 |
| SAD_dep_sum | DASS_A_sum | 0.5098626 | 0.0000000 |
| SAD_ea_sum | DASS_A_sum | 0.5827145 | 0.0000000 |
| SAD_access_sum | DASS_A_sum | 0.3653758 | 0.0000000 |
| SAD_dist_sum | DASS_A_sum | 0.6880797 | 0.0000000 |
| BIS_sum | DASS_A_sum | 0.5969497 | 0.0000000 |
| NEO_O_sum | DASS_A_sum | -0.4473584 | 0.0000000 |
| NEO_C_sum | DASS_A_sum | -0.5352169 | 0.0000000 |
| NEO_E_sum | DASS_A_sum | 0.1785400 | 0.0000547 |
| NEO_A_sum | DASS_A_sum | -0.6225484 | 0.0000000 |
| NEO_N_sum | DASS_A_sum | 0.5805995 | 0.0000000 |
| RSES_sum | DASS_A_sum | 0.3354409 | 0.0000000 |
| SRS_sum | DASS_A_sum | -0.5127237 | 0.0000000 |
| SSEIT_sum | DASS_A_sum | -0.1088303 | 0.0144103 |
| DASS_D_sum | DASS_A_sum | 0.8593110 | 0.0000000 |
| age | DASS_S_sum | -0.3075764 | 0.0000000 |
| age_first_phone | DASS_S_sum | 0.0541507 | 0.2244568 |
| com_gen | DASS_S_sum | 0.3494967 | 0.0000000 |
| com_unattended | DASS_S_sum | 0.4532564 | 0.0000000 |
| com_leave_with_others | DASS_S_sum | 0.4877037 | 0.0000000 |
| com_locked | DASS_S_sum | 0.0902008 | 0.0427525 |
| com_room_task | DASS_S_sum | 0.3357213 | 0.0000000 |
| dist_daily | DASS_S_sum | 0.4656930 | 0.0000000 |
| dist_study | DASS_S_sum | 0.6788551 | 0.0000000 |
| NMPQ_sum | DASS_S_sum | 0.5241917 | 0.0000000 |
| SAD_sum | DASS_S_sum | 0.5692090 | 0.0000000 |
| SAD_dep_sum | DASS_S_sum | 0.4992664 | 0.0000000 |
| SAD_ea_sum | DASS_S_sum | 0.5245261 | 0.0000000 |
| SAD_access_sum | DASS_S_sum | 0.3788605 | 0.0000000 |
| SAD_dist_sum | DASS_S_sum | 0.6523476 | 0.0000000 |
| BIS_sum | DASS_S_sum | 0.5845331 | 0.0000000 |
| NEO_O_sum | DASS_S_sum | -0.3782456 | 0.0000000 |
| NEO_C_sum | DASS_S_sum | -0.5273599 | 0.0000000 |
| NEO_E_sum | DASS_S_sum | 0.0892276 | 0.0450517 |
| NEO_A_sum | DASS_S_sum | -0.5840085 | 0.0000000 |
| NEO_N_sum | DASS_S_sum | 0.6803168 | 0.0000000 |
| RSES_sum | DASS_S_sum | 0.3741466 | 0.0000000 |
| SRS_sum | DASS_S_sum | -0.5363289 | 0.0000000 |
| SSEIT_sum | DASS_S_sum | -0.1390122 | 0.0017398 |
| DASS_D_sum | DASS_S_sum | 0.8718889 | 0.0000000 |
| DASS_A_sum | DASS_S_sum | 0.8920808 | 0.0000000 |
# print tables using kable
kable(as.data.frame(format(mturk_corr2$r, scientific = FALSE)), caption = "MTURK Study - Correlation: r values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.1153 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | -0.1993 | -0.0207 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | -0.2683 | -0.0278 | 0.6317 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | -0.2622 | -0.0246 | 0.6179 | 0.735 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | -0.1409 | 0.021 | -0.0956 | -0.1958 | -0.1591 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | -0.1115 | -0.0392 | 0.5068 | 0.5965 | 0.557 | -0.1422 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | -0.3132 | 0.0041 | 0.3412 | 0.3324 | 0.3478 | 0.1118 | 0.2882 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | -0.342 | 0.024 | 0.4514 | 0.5417 | 0.5861 | 0.0706 | 0.3831 | 0.4701 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | -0.2568 | 0.0065 | 0.2623 | 0.2546 | 0.3254 | 0.2452 | 0.1689 | 0.5175 | 0.5144 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | -0.2976 | -0.0017 | 0.3296 | 0.269 | 0.3434 | 0.214 | 0.1796 | 0.5377 | 0.5286 | 0.8692 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | -0.2448 | 0.0082 | 0.2948 | 0.2253 | 0.2787 | 0.1632 | 0.1262 | 0.4717 | 0.4556 | 0.8122 | 0.9162 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | -0.3224 | 0.0016 | 0.3694 | 0.3186 | 0.3681 | 0.1829 | 0.2065 | 0.4708 | 0.5285 | 0.7623 | 0.9033 | 0.7438 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | -0.1625 | -0.0082 | 0.1548 | 0.1006 | 0.1669 | 0.2473 | 0.037 | 0.4189 | 0.3206 | 0.7601 | 0.853 | 0.7722 | 0.6466 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | -0.3471 | -0.0202 | 0.3863 | 0.3518 | 0.4506 | 0.1031 | 0.3178 | 0.5269 | 0.6153 | 0.6443 | 0.7889 | 0.6389 | 0.7346 | 0.5137 | 1 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | -0.2432 | -0.0082 | 0.2233 | 0.4292 | 0.4517 | -0.1326 | 0.2889 | 0.322 | 0.5276 | 0.2789 | 0.3289 | 0.2785 | 0.2983 | 0.1769 | 0.4885 | 1 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.1331 | 0.0291 | -0.2771 | -0.3688 | -0.3545 | 0.085 | -0.2486 | -0.1402 | -0.4661 | -0.2138 | -0.2624 | -0.239 | -0.2699 | -0.128 | -0.3525 | -0.3689 | 1 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.3149 | 0.021 | -0.1811 | -0.3044 | -0.3324 | 0.1445 | -0.2254 | -0.2576 | -0.437 | -0.0921 | -0.1614 | -0.123 | -0.1683 | 0.0085 | -0.395 | -0.6698 | 0.3036 | 1 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | -0.0975 | -0.0278 | 0.3068 | 0.1376 | 0.1578 | 0.1299 | 0.1027 | 0.0757 | 0.2664 | 0.3023 | 0.2958 | 0.2482 | 0.3412 | 0.2271 | 0.2186 | -0.0533 | -0.0715 | 0.2783 | 1 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.35 | 0.0095 | -0.2732 | -0.3896 | -0.421 | 0.0578 | -0.1985 | -0.2112 | -0.5851 | -0.2647 | -0.3044 | -0.2514 | -0.334 | -0.1347 | -0.4441 | -0.5432 | 0.5151 | 0.5829 | -0.0809 | 1 | - | - | - | - | - | - | - |
| NEO_N_sum | -0.2398 | 0.0286 | 0.1277 | 0.312 | 0.3427 | -0.0034 | 0.2591 | 0.3727 | 0.4382 | 0.3401 | 0.3846 | 0.3377 | 0.3191 | 0.256 | 0.4762 | 0.5766 | -0.2904 | -0.6089 | -0.3294 | -0.4462 | 1 | - | - | - | - | - | - |
| RSES_sum | -0.2167 | -0.0464 | 0.1272 | 0.1462 | 0.144 | -0.0141 | 0.1664 | 0.0845 | 0.2059 | 0.1023 | 0.1461 | 0.1174 | 0.1592 | 0.0573 | 0.2167 | 0.2626 | -0.1006 | -0.325 | -0.1528 | -0.238 | 0.3858 | 1 | - | - | - | - | - |
| SRS_sum | 0.2904 | 0.0131 | -0.1186 | -0.1922 | -0.2423 | 0.0858 | -0.1568 | -0.3238 | -0.3904 | -0.2522 | -0.3485 | -0.2845 | -0.3253 | -0.1599 | -0.5386 | -0.6123 | 0.2777 | 0.647 | 0.2539 | 0.4803 | -0.6545 | -0.3492 | 1 | - | - | - | - |
| SSEIT_sum | 0.0685 | 0.025 | 0.1281 | 0.002 | 0.007 | 0.1903 | 0.0137 | 0.0577 | -0.0481 | 0.2397 | 0.2175 | 0.1685 | 0.2448 | 0.2261 | 0.0322 | -0.3259 | 0.2501 | 0.5798 | 0.5448 | 0.3883 | -0.4072 | -0.2341 | 0.5027 | 1 | - | - | - |
| DASS_D_sum | -0.3414 | -0.0457 | 0.3766 | 0.4611 | 0.4944 | 0.094 | 0.3468 | 0.432 | 0.6557 | 0.4654 | 0.5168 | 0.4349 | 0.4975 | 0.327 | 0.6182 | 0.5721 | -0.4137 | -0.5794 | 0.0064 | -0.5979 | 0.6681 | 0.4264 | -0.5428 | -0.2217 | 1 | - | - |
| DASS_A_sum | -0.3652 | -0.0363 | 0.4411 | 0.4956 | 0.5642 | 0.0807 | 0.3529 | 0.4236 | 0.7421 | 0.5294 | 0.5929 | 0.5099 | 0.5827 | 0.3654 | 0.6881 | 0.5969 | -0.4474 | -0.5352 | 0.1785 | -0.6225 | 0.5806 | 0.3354 | -0.5127 | -0.1088 | 0.8593 | 1 | - |
| DASS_S_sum | -0.3076 | 0.0542 | 0.3495 | 0.4533 | 0.4877 | 0.0902 | 0.3357 | 0.4657 | 0.6789 | 0.5242 | 0.5692 | 0.4993 | 0.5245 | 0.3789 | 0.6523 | 0.5845 | -0.3782 | -0.5274 | 0.0892 | -0.584 | 0.6803 | 0.3741 | -0.5363 | -0.139 | 0.8719 | 0.8921 | 1 |
kable(as.data.frame(format(mturk_corr2$P, scientific = FALSE)), caption = "MTURK Study - Correlation: p values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.0095 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 0 | 0.6425 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0 | 0.5337 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 0 | 0.5819 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 0.0015 | 0.6371 | 0.0318 | 0 | 3e-04 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 0.0122 | 0.3791 | 0 | 0 | 0 | 0.0014 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 0 | 0.926 | 0 | 0 | 0 | 0.0119 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 0 | 0.5912 | 0 | 0 | 0 | 0.113 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 0 | 0.8838 | 0 | 0 | 0 | 0 | 1e-04 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 0 | 0.9703 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 0 | 0.854 | 0 | 0 | 0 | 2e-04 | 0.0045 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 0 | 0.9711 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 2e-04 | 0.8546 | 5e-04 | 0.0237 | 2e-04 | 0 | 0.4062 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 0 | 0.65 | 0 | 0 | 0 | 0.0205 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0 | 0.8537 | 0 | 0 | 0 | 0.0028 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1e-04 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.0027 | 0.5139 | 0 | 0 | 0 | 0.0562 | 0 | 0.0016 | 0 | 0 | 0 | 0 | 0 | 0.004 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0 | 0.638 | 0 | 0 | 0 | 0.0011 | 0 | 0 | 0 | 0.0386 | 3e-04 | 0.0057 | 1e-04 | 0.8497 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 0.0285 | 0.5334 | 0 | 0.0019 | 4e-04 | 0.0035 | 0.0209 | 0.0893 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2316 | 0.1088 | 0 | NA | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0 | 0.8305 | 0 | 0 | 0 | 0.1946 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0024 | 0 | 0 | 0 | 0 | 0.0692 | NA | - | - | - | - | - | - | - |
| NEO_N_sum | 0 | 0.5211 | 0.0041 | 0 | 0 | 0.9394 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - |
| RSES_sum | 0 | 0.2982 | 0.0042 | 0.001 | 0.0012 | 0.7519 | 2e-04 | 0.0578 | 0 | 0.0214 | 0.001 | 0.0083 | 3e-04 | 0.1984 | 0 | 0 | 0.0238 | 0 | 6e-04 | 0 | 0 | NA | - | - | - | - | - |
| SRS_sum | 0 | 0.7698 | 0.0076 | 0 | 0 | 0.054 | 4e-04 | 0 | 0 | 0 | 0 | 0 | 0 | 3e-04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - |
| SSEIT_sum | 0.1244 | 0.5752 | 0.0039 | 0.9644 | 0.876 | 0 | 0.758 | 0.1954 | 0.2811 | 0 | 0 | 1e-04 | 0 | 0 | 0.47 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - |
| DASS_D_sum | 0 | 0.3051 | 0 | 0 | 0 | 0.0347 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.8866 | 0 | 0 | 0 | 0 | 0 | NA | - | - |
| DASS_A_sum | 0 | 0.4157 | 0 | 0 | 0 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1e-04 | 0 | 0 | 0 | 0 | 0.0144 | 0 | NA | - |
| DASS_S_sum | 0 | 0.2245 | 0 | 0 | 0 | 0.0428 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0451 | 0 | 0 | 0 | 0 | 0.0017 | 0 | 0 | NA |
kable(as.data.frame(format(mturk_corr2$n, scientific = FALSE)), caption = "MTURK Study - Correlation: n values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - | - |
| NEO_N_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - | - |
| RSES_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - | - |
| SRS_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - | - |
| SSEIT_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - | - |
| DASS_D_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - | - |
| DASS_A_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | - |
| DASS_S_sum | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 | 505 |
# create new matrix for r & p vals with different col names
# get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
mturk_corr_r <- mturk_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
mturk_corr_p <- mturk_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")
# all vars included
corr_plot_mturk_all <- {
corrplot(mturk_corr$r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_corr$P, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .5, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# with all SAD subscales
corr_plot_mturk_ques_all <- {
corrplot(mturk_corr_r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_corr_p, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# final -- with SAD overall only
corr_plot_mturk_ques_final <- {
corrplot(mturk_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_all(oct28)", plot = replayPlot(corr_plot_mturk_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ques_all(oct28)", plot = replayPlot(corr_plot_mturk_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ques_final(oct28)", plot = replayPlot(corr_plot_mturk_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
# export csv files of R values & p values
write.csv(mturk_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_corr_rvals(oct28).csv", row.names = T)
write.csv(mturk_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_corr_pvals(oct28).csv", row.names = T)Regressions
A regression analysis was completed to investigate which measure was a significant predictor of nomophobia (NMPQ) and smartphone dependency (SAD: Dep.).
The Linear models were as follows:
- \(NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
- \(SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
- \(SAD: Dep. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
- \(SAD: Access. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
- \(SAD: EA = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
- \(SAD: Dist. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
All Data
Study 1: SONA
NMPQ
This regression used the following formula: \(NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 2.8478559, p = .001, R2 = 0.14
# run lm
sona_reg_NMPQ <- lm(data = SONA_data, formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_NMPQ)
Call:
lm(formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-61.225 -13.624 -0.002 14.614 55.358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 83.73122 32.30362 2.592 0.01016 *
age -0.64849 0.67399 -0.962 0.33700
BIS_sum -0.47233 0.57083 -0.827 0.40886
NEO_O_sum -0.59817 0.24687 -2.423 0.01618 *
NEO_C_sum -0.03698 0.36866 -0.100 0.92018
NEO_E_sum 0.21302 0.26232 0.812 0.41763
NEO_A_sum -0.07458 0.24882 -0.300 0.76466
NEO_N_sum 0.70429 0.25400 2.773 0.00602 **
RSES_sum 0.19876 0.36088 0.551 0.58234
SRS_sum -0.92889 0.38813 -2.393 0.01752 *
SSEIT_sum 0.31572 0.12283 2.570 0.01080 *
DASS_D_sum -0.49156 0.48685 -1.010 0.31373
DASS_A_sum 0.26087 0.58044 0.449 0.65354
DASS_S_sum 0.53740 0.64578 0.832 0.40619
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.41 on 226 degrees of freedom
Multiple R-squared: 0.1408, Adjusted R-squared: 0.09133
F-statistic: 2.848 on 13 and 226 DF, p-value: 0.000816# use this to plot assumption plots (e.g., q-q)
# plot(sona_reg_NMPQ)
# summary(lm(data = SONA_data, formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
# create table depicting main stats to export later
sona_reg_NMPQ_t <-
rbind(tidy(sona_reg_NMPQ), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_NMPQ)$fstatistic[2], summary(sona_reg_NMPQ)$r.squared, nrow(sona_reg_NMPQ$model)),
"std.error" = c(summary(sona_reg_NMPQ)$fstatistic[3], summary(sona_reg_NMPQ)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_NMPQ)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_NMPQ)$fstatistic[1], summary(sona_reg_NMPQ)$fstatistic[2], summary(sona_reg_NMPQ)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_NMPQ_t, caption = "Multiple Regression Model Predicting NMPQ Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 83.731 | 32.304 | 2.592 | 0.010 |
| age | -0.648 | 0.674 | -0.962 | 0.337 |
| BIS_sum | -0.472 | 0.571 | -0.827 | 0.409 |
| NEO_O_sum | -0.598 | 0.247 | -2.423 | 0.016 |
| NEO_C_sum | -0.037 | 0.369 | -0.100 | 0.920 |
| NEO_E_sum | 0.213 | 0.262 | 0.812 | 0.418 |
| NEO_A_sum | -0.075 | 0.249 | -0.300 | 0.765 |
| NEO_N_sum | 0.704 | 0.254 | 2.773 | 0.006 |
| RSES_sum | 0.199 | 0.361 | 0.551 | 0.582 |
| SRS_sum | -0.929 | 0.388 | -2.393 | 0.018 |
| SSEIT_sum | 0.316 | 0.123 | 2.570 | 0.011 |
| DASS_D_sum | -0.492 | 0.487 | -1.010 | 0.314 |
| DASS_A_sum | 0.261 | 0.580 | 0.449 | 0.654 |
| DASS_S_sum | 0.537 | 0.646 | 0.832 | 0.406 |
| f_stat | 13.000 | 226.000 | 2.848 | 0.001 |
| R2/Adj.R2 | 0.141 | 0.091 | ||
| N | 240.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
# reg_colours <- c("#7E1044", "#170FA7", "black", "red", "purple", "grey", "yellow")
# reg_colours <- c("#98410B", "#F0803C", "#DDA448", "#858585", "#3F826D", "#08605F", "#414535") # orange >green
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_NMPQ_plot <-
sjPlot::plot_model(model = sona_reg_NMPQ, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
#axis.lim = c(-.25,.25)
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_NMPQ_plot
# this plots the non-standardized betas
# sjPlot::plot_model(sona_reg_NMPQ, type = "est") #ither otpions: c("est", "eff", "pred", "int", "std", "std2", "slope", "resid", "diag")
# plot the table using sjPlot
# sjPlot::tab_model(sona_reg_NMPQ, show.stat = T, show.std = T)
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_NMPQ_plot(oct28)", plot = sona_reg_NMPQ_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_NMPQ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_NMPQ_t(oct28).csv", row.names = T)SAD - Overall
This regression used the following formula: \(SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 3.5779788, p < .001, R2 = 0.17
# run lm
sona_reg_SAD <- lm(data = SONA_data, formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_SAD)
Call:
lm(formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-37.297 -6.629 0.473 8.260 35.114
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42.691221 19.481636 2.191 0.0294 *
age -0.279862 0.406469 -0.689 0.4918
BIS_sum -0.004475 0.344256 -0.013 0.9896
NEO_O_sum -0.313319 0.148883 -2.104 0.0364 *
NEO_C_sum -0.012606 0.222330 -0.057 0.9548
NEO_E_sum -0.007554 0.158202 -0.048 0.9620
NEO_A_sum -0.240820 0.150056 -1.605 0.1099
NEO_N_sum 0.311012 0.153184 2.030 0.0435 *
RSES_sum 0.141384 0.217641 0.650 0.5166
SRS_sum -0.392246 0.234072 -1.676 0.0952 .
SSEIT_sum 0.324132 0.074075 4.376 1.85e-05 ***
DASS_D_sum 0.006256 0.293607 0.021 0.9830
DASS_A_sum -0.162021 0.350049 -0.463 0.6439
DASS_S_sum 0.566880 0.389455 1.456 0.1469
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.91 on 226 degrees of freedom
Multiple R-squared: 0.1707, Adjusted R-squared: 0.123
F-statistic: 3.578 on 13 and 226 DF, p-value: 3.999e-05# create table depicting main stats to export later
sona_reg_SAD_t <-
rbind(tidy(sona_reg_SAD), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_SAD)$fstatistic[2], summary(sona_reg_SAD)$r.squared, nrow(sona_reg_SAD$model)),
"std.error" = c(summary(sona_reg_SAD)$fstatistic[3], summary(sona_reg_SAD)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_SAD)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_SAD)$fstatistic[1], summary(sona_reg_SAD)$fstatistic[2], summary(sona_reg_SAD)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_SAD_t, caption = "Multiple Regression Model Predicting SAD Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 42.691 | 19.482 | 2.191 | 0.029 |
| age | -0.280 | 0.406 | -0.689 | 0.492 |
| BIS_sum | -0.004 | 0.344 | -0.013 | 0.990 |
| NEO_O_sum | -0.313 | 0.149 | -2.104 | 0.036 |
| NEO_C_sum | -0.013 | 0.222 | -0.057 | 0.955 |
| NEO_E_sum | -0.008 | 0.158 | -0.048 | 0.962 |
| NEO_A_sum | -0.241 | 0.150 | -1.605 | 0.110 |
| NEO_N_sum | 0.311 | 0.153 | 2.030 | 0.043 |
| RSES_sum | 0.141 | 0.218 | 0.650 | 0.517 |
| SRS_sum | -0.392 | 0.234 | -1.676 | 0.095 |
| SSEIT_sum | 0.324 | 0.074 | 4.376 | 0.000 |
| DASS_D_sum | 0.006 | 0.294 | 0.021 | 0.983 |
| DASS_A_sum | -0.162 | 0.350 | -0.463 | 0.644 |
| DASS_S_sum | 0.567 | 0.389 | 1.456 | 0.147 |
| f_stat | 13.000 | 226.000 | 3.578 | 0.000 |
| R2/Adj.R2 | 0.171 | 0.123 | ||
| N | 240.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_SAD_plot <-
sjPlot::plot_model(model = sona_reg_SAD, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_SAD_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_plot(oct28)", plot = sona_reg_SAD_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_SAD_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_t(oct28).csv", row.names = T)SAD - Dependency
This regression used the following formula: \(SAD: Dep = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 2.3475007, p = .01, R2 = 0.12
# run lm
sona_reg_SAD_dep <- lm(data = SONA_data, formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_SAD_dep)
Call:
lm(formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-12.9026 -2.4888 -0.0599 3.1104 10.0122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.302299 6.648998 2.151 0.03253 *
age 0.032077 0.138726 0.231 0.81735
BIS_sum -0.015189 0.117493 -0.129 0.89725
NEO_O_sum -0.162854 0.050813 -3.205 0.00155 **
NEO_C_sum -0.059042 0.075880 -0.778 0.43733
NEO_E_sum 0.001822 0.053994 0.034 0.97312
NEO_A_sum -0.054659 0.051213 -1.067 0.28698
NEO_N_sum 0.081183 0.052281 1.553 0.12186
RSES_sum 0.072668 0.074280 0.978 0.32897
SRS_sum -0.148618 0.079888 -1.860 0.06414 .
SSEIT_sum 0.077291 0.025281 3.057 0.00250 **
DASS_D_sum -0.054670 0.100207 -0.546 0.58590
DASS_A_sum -0.010934 0.119470 -0.092 0.92716
DASS_S_sum 0.041328 0.132919 0.311 0.75615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.406 on 226 degrees of freedom
Multiple R-squared: 0.119, Adjusted R-squared: 0.06829
F-statistic: 2.348 on 13 and 226 DF, p-value: 0.005912# create table depicting main stats to export later
sona_reg_SAD_dep_t <-
rbind(tidy(sona_reg_SAD_dep), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_SAD_dep)$fstatistic[2], summary(sona_reg_SAD_dep)$r.squared, nrow(sona_reg_SAD_dep$model)),
"std.error" = c(summary(sona_reg_SAD_dep)$fstatistic[3], summary(sona_reg_SAD_dep)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_SAD_dep)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_SAD_dep)$fstatistic[1], summary(sona_reg_SAD_dep)$fstatistic[2], summary(sona_reg_SAD_dep)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_SAD_dep_t, caption = "Multiple Regression Model Predicting SAD_dep Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 14.302 | 6.649 | 2.151 | 0.033 |
| age | 0.032 | 0.139 | 0.231 | 0.817 |
| BIS_sum | -0.015 | 0.117 | -0.129 | 0.897 |
| NEO_O_sum | -0.163 | 0.051 | -3.205 | 0.002 |
| NEO_C_sum | -0.059 | 0.076 | -0.778 | 0.437 |
| NEO_E_sum | 0.002 | 0.054 | 0.034 | 0.973 |
| NEO_A_sum | -0.055 | 0.051 | -1.067 | 0.287 |
| NEO_N_sum | 0.081 | 0.052 | 1.553 | 0.122 |
| RSES_sum | 0.073 | 0.074 | 0.978 | 0.329 |
| SRS_sum | -0.149 | 0.080 | -1.860 | 0.064 |
| SSEIT_sum | 0.077 | 0.025 | 3.057 | 0.003 |
| DASS_D_sum | -0.055 | 0.100 | -0.546 | 0.586 |
| DASS_A_sum | -0.011 | 0.119 | -0.092 | 0.927 |
| DASS_S_sum | 0.041 | 0.133 | 0.311 | 0.756 |
| f_stat | 13.000 | 226.000 | 2.348 | 0.006 |
| R2/Adj.R2 | 0.119 | 0.068 | ||
| N | 240.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_SAD_dep_plot <-
sjPlot::plot_model(model = sona_reg_SAD_dep, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_SAD_dep_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_dep_plot(oct28)", plot = sona_reg_SAD_dep_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_SAD_dep_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_dep_t(oct28).csv", row.names = T)SAD - Accessibility
This regression used the following formula: \(SAD: Access. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 2.9346619, p = .001, R2 = 0.14
# run lm
sona_reg_SAD_access <- lm(data = SONA_data, formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_SAD_access)
Call:
lm(formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-13.4838 -2.3200 0.0367 2.6640 8.0595
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.55612 5.62970 0.632 0.528240
age -0.13052 0.11746 -1.111 0.267677
BIS_sum 0.01550 0.09948 0.156 0.876335
NEO_O_sum -0.02323 0.04302 -0.540 0.589826
NEO_C_sum 0.06474 0.06425 1.008 0.314660
NEO_E_sum -0.01722 0.04572 -0.377 0.706720
NEO_A_sum -0.03191 0.04336 -0.736 0.462526
NEO_N_sum 0.11242 0.04427 2.540 0.011772 *
RSES_sum 0.04233 0.06289 0.673 0.501570
SRS_sum -0.05747 0.06764 -0.850 0.396412
SSEIT_sum 0.07465 0.02141 3.487 0.000586 ***
DASS_D_sum 0.06362 0.08484 0.750 0.454139
DASS_A_sum -0.03573 0.10116 -0.353 0.724228
DASS_S_sum 0.07077 0.11254 0.629 0.530125
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.731 on 226 degrees of freedom
Multiple R-squared: 0.1444, Adjusted R-squared: 0.09521
F-statistic: 2.935 on 13 and 226 DF, p-value: 0.0005736# create table depicting main stats to export later
sona_reg_SAD_access_t <-
rbind(tidy(sona_reg_SAD_access), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_SAD_access)$fstatistic[2], summary(sona_reg_SAD_access)$r.squared, nrow(sona_reg_SAD_access$model)),
"std.error" = c(summary(sona_reg_SAD_access)$fstatistic[3], summary(sona_reg_SAD_access)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_SAD_access)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_SAD_access)$fstatistic[1], summary(sona_reg_SAD_access)$fstatistic[2], summary(sona_reg_SAD_access)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_SAD_access_t, caption = "Multiple Regression Model Predicting SAD_access Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.556 | 5.630 | 0.632 | 0.528 |
| age | -0.131 | 0.117 | -1.111 | 0.268 |
| BIS_sum | 0.015 | 0.099 | 0.156 | 0.876 |
| NEO_O_sum | -0.023 | 0.043 | -0.540 | 0.590 |
| NEO_C_sum | 0.065 | 0.064 | 1.008 | 0.315 |
| NEO_E_sum | -0.017 | 0.046 | -0.377 | 0.707 |
| NEO_A_sum | -0.032 | 0.043 | -0.736 | 0.463 |
| NEO_N_sum | 0.112 | 0.044 | 2.540 | 0.012 |
| RSES_sum | 0.042 | 0.063 | 0.673 | 0.502 |
| SRS_sum | -0.057 | 0.068 | -0.850 | 0.396 |
| SSEIT_sum | 0.075 | 0.021 | 3.487 | 0.001 |
| DASS_D_sum | 0.064 | 0.085 | 0.750 | 0.454 |
| DASS_A_sum | -0.036 | 0.101 | -0.353 | 0.724 |
| DASS_S_sum | 0.071 | 0.113 | 0.629 | 0.530 |
| f_stat | 13.000 | 226.000 | 2.935 | 0.001 |
| R2/Adj.R2 | 0.144 | 0.095 | ||
| N | 240.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_SAD_access_plot <-
sjPlot::plot_model(model = sona_reg_SAD_access, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_SAD_access_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_access_plot(oct28)", plot = sona_reg_SAD_access_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_SAD_access_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_access_t(oct28).csv", row.names = T)SAD - Emotional Attachement
This regression used the following formula: \(SAD: EA = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 3.2315141, p < .001, R2 = 0.16
# run lm
sona_reg_SAD_ea <- lm(data = SONA_data, formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_SAD_ea)
Call:
lm(formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-15.5397 -2.4464 -0.0422 3.0712 10.6453
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.52232 6.59371 1.899 0.05882 .
age 0.01487 0.13757 0.108 0.91399
BIS_sum -0.05729 0.11652 -0.492 0.62342
NEO_O_sum -0.09854 0.05039 -1.956 0.05175 .
NEO_C_sum -0.02254 0.07525 -0.300 0.76482
NEO_E_sum 0.05133 0.05354 0.959 0.33876
NEO_A_sum -0.11645 0.05079 -2.293 0.02277 *
NEO_N_sum 0.05385 0.05185 1.039 0.30006
RSES_sum 0.02985 0.07366 0.405 0.68564
SRS_sum -0.03245 0.07922 -0.410 0.68250
SSEIT_sum 0.08298 0.02507 3.310 0.00109 **
DASS_D_sum 0.06592 0.09937 0.663 0.50779
DASS_A_sum -0.09150 0.11848 -0.772 0.44075
DASS_S_sum 0.30741 0.13181 2.332 0.02057 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.369 on 226 degrees of freedom
Multiple R-squared: 0.1567, Adjusted R-squared: 0.1082
F-statistic: 3.232 on 13 and 226 DF, p-value: 0.0001694# create table depicting main stats to export later
sona_reg_SAD_ea_t <-
rbind(tidy(sona_reg_SAD_ea), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_SAD_ea)$fstatistic[2], summary(sona_reg_SAD_ea)$r.squared, nrow(sona_reg_SAD_ea$model)),
"std.error" = c(summary(sona_reg_SAD_ea)$fstatistic[3], summary(sona_reg_SAD_ea)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_SAD_ea)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_SAD_ea)$fstatistic[1], summary(sona_reg_SAD_ea)$fstatistic[2], summary(sona_reg_SAD_ea)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_SAD_ea_t, caption = "Multiple Regression Model Predicting SAD_ea Score", align = rep('lcccc'), digits = 4) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 12.5223 | 6.5937 | 1.8991 | 0.0588 |
| age | 0.0149 | 0.1376 | 0.1081 | 0.9140 |
| BIS_sum | -0.0573 | 0.1165 | -0.4917 | 0.6234 |
| NEO_O_sum | -0.0985 | 0.0504 | -1.9556 | 0.0517 |
| NEO_C_sum | -0.0225 | 0.0752 | -0.2995 | 0.7648 |
| NEO_E_sum | 0.0513 | 0.0535 | 0.9586 | 0.3388 |
| NEO_A_sum | -0.1165 | 0.0508 | -2.2929 | 0.0228 |
| NEO_N_sum | 0.0539 | 0.0518 | 1.0387 | 0.3001 |
| RSES_sum | 0.0299 | 0.0737 | 0.4053 | 0.6856 |
| SRS_sum | -0.0324 | 0.0792 | -0.4096 | 0.6825 |
| SSEIT_sum | 0.0830 | 0.0251 | 3.3097 | 0.0011 |
| DASS_D_sum | 0.0659 | 0.0994 | 0.6633 | 0.5078 |
| DASS_A_sum | -0.0915 | 0.1185 | -0.7723 | 0.4407 |
| DASS_S_sum | 0.3074 | 0.1318 | 2.3321 | 0.0206 |
| f_stat | 13.0000 | 226.0000 | 3.2315 | 0.0002 |
| R2/Adj.R2 | 0.1567 | 0.1082 | ||
| N | 240.0000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_SAD_ea_plot <-
sjPlot::plot_model(model = sona_reg_SAD_ea, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_SAD_ea_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_ea_plot(oct28)", plot = sona_reg_SAD_ea_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_SAD_ea_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_ea_t(oct28).csv", row.names = T)SAD - Distractibility
This regression used the following formula: \(SAD: Dist = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 226) = 3.3724433, p < .001, R2 = 0.16
# run lm
sona_reg_SAD_dist <- lm(data = SONA_data, formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(sona_reg_SAD_dist)
Call:
lm(formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-6.5548 -1.2844 0.1908 1.6459 5.2641
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.699303 3.655199 1.833 0.068146 .
age -0.143765 0.076263 -1.885 0.060696 .
BIS_sum 0.049786 0.064590 0.771 0.441630
NEO_O_sum 0.004503 0.027934 0.161 0.872065
NEO_C_sum 0.024427 0.041714 0.586 0.558747
NEO_E_sum -0.009767 0.029682 -0.329 0.742414
NEO_A_sum -0.029430 0.028154 -1.045 0.296997
NEO_N_sum 0.037296 0.028741 1.298 0.195724
RSES_sum -0.009578 0.040834 -0.235 0.814771
SRS_sum -0.106371 0.043917 -2.422 0.016220 *
SSEIT_sum 0.054260 0.013898 3.904 0.000125 ***
DASS_D_sum -0.055450 0.055087 -1.007 0.315211
DASS_A_sum -0.001039 0.065677 -0.016 0.987388
DASS_S_sum 0.111389 0.073071 1.524 0.128806
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.422 on 226 degrees of freedom
Multiple R-squared: 0.1625, Adjusted R-squared: 0.1143
F-statistic: 3.372 on 13 and 226 DF, p-value: 9.438e-05# create table depicting main stats to export later
sona_reg_SAD_dist_t <-
rbind(tidy(sona_reg_SAD_dist), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(sona_reg_SAD_dist)$fstatistic[2], summary(sona_reg_SAD_dist)$r.squared, nrow(sona_reg_SAD_dist$model)),
"std.error" = c(summary(sona_reg_SAD_dist)$fstatistic[3], summary(sona_reg_SAD_dist)$adj.r.squared, NA),
"statistic" = c(summary(sona_reg_SAD_dist)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(sona_reg_SAD_dist)$fstatistic[1], summary(sona_reg_SAD_dist)$fstatistic[2], summary(sona_reg_SAD_dist)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(sona_reg_SAD_dist_t, caption = "Multiple Regression Model Predicting SAD_dist Score", align = rep('lcccc'), digits = 4) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 6.6993 | 3.6552 | 1.8328 | 0.0681 |
| age | -0.1438 | 0.0763 | -1.8851 | 0.0607 |
| BIS_sum | 0.0498 | 0.0646 | 0.7708 | 0.4416 |
| NEO_O_sum | 0.0045 | 0.0279 | 0.1612 | 0.8721 |
| NEO_C_sum | 0.0244 | 0.0417 | 0.5856 | 0.5587 |
| NEO_E_sum | -0.0098 | 0.0297 | -0.3291 | 0.7424 |
| NEO_A_sum | -0.0294 | 0.0282 | -1.0453 | 0.2970 |
| NEO_N_sum | 0.0373 | 0.0287 | 1.2977 | 0.1957 |
| RSES_sum | -0.0096 | 0.0408 | -0.2345 | 0.8148 |
| SRS_sum | -0.1064 | 0.0439 | -2.4221 | 0.0162 |
| SSEIT_sum | 0.0543 | 0.0139 | 3.9041 | 0.0001 |
| DASS_D_sum | -0.0555 | 0.0551 | -1.0066 | 0.3152 |
| DASS_A_sum | -0.0010 | 0.0657 | -0.0158 | 0.9874 |
| DASS_S_sum | 0.1114 | 0.0731 | 1.5244 | 0.1288 |
| f_stat | 13.0000 | 226.0000 | 3.3724 | 0.0001 |
| R2/Adj.R2 | 0.1625 | 0.1143 | ||
| N | 240.0000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
sona_reg_SAD_dist_plot <-
sjPlot::plot_model(model = sona_reg_SAD_dist, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
sona_reg_SAD_dist_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_dist_plot(oct28)", plot = sona_reg_SAD_dist_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(sona_reg_SAD_dist_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_dist_t(oct28).csv", row.names = T)Study 2: Mturk
NMPQ
This regression used the following formula: \(NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 32.109364, p < .001, R2 = 0.46
# run lm
mturk_reg_NMPQ <- lm(data = Mturk_data, formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_NMPQ)
Call:
lm(formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-70.609 -9.827 0.464 11.456 59.484
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.64067 17.32345 -0.441 0.659364
age -0.23127 0.10179 -2.272 0.023522 *
BIS_sum 0.10695 0.31844 0.336 0.737115
NEO_O_sum -0.14219 0.16051 -0.886 0.376129
NEO_C_sum 0.85822 0.23524 3.648 0.000292 ***
NEO_E_sum 0.42582 0.14199 2.999 0.002847 **
NEO_A_sum -0.04721 0.18202 -0.259 0.795471
NEO_N_sum 0.70104 0.16466 4.257 2.48e-05 ***
RSES_sum -0.28655 0.14833 -1.932 0.053958 .
SRS_sum -0.87975 0.26569 -3.311 0.000997 ***
SSEIT_sum 0.44977 0.08105 5.549 4.71e-08 ***
DASS_D_sum 0.40794 0.32461 1.257 0.209461
DASS_A_sum 0.73845 0.37551 1.967 0.049799 *
DASS_S_sum 0.57946 0.39398 1.471 0.141993
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 20.24 on 491 degrees of freedom
Multiple R-squared: 0.4595, Adjusted R-squared: 0.4452
F-statistic: 32.11 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_NMPQ_t <-
rbind(tidy(mturk_reg_NMPQ), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_NMPQ)$fstatistic[2], summary(mturk_reg_NMPQ)$r.squared, nrow(mturk_reg_NMPQ$model)),
"std.error" = c(summary(mturk_reg_NMPQ)$fstatistic[3], summary(mturk_reg_NMPQ)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_NMPQ)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_NMPQ)$fstatistic[1], summary(mturk_reg_NMPQ)$fstatistic[2], summary(mturk_reg_NMPQ)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_NMPQ_t, caption = "Multiple Regression Model Predicting NMPQ Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -7.641 | 17.323 | -0.441 | 0.659 |
| age | -0.231 | 0.102 | -2.272 | 0.024 |
| BIS_sum | 0.107 | 0.318 | 0.336 | 0.737 |
| NEO_O_sum | -0.142 | 0.161 | -0.886 | 0.376 |
| NEO_C_sum | 0.858 | 0.235 | 3.648 | 0.000 |
| NEO_E_sum | 0.426 | 0.142 | 2.999 | 0.003 |
| NEO_A_sum | -0.047 | 0.182 | -0.259 | 0.795 |
| NEO_N_sum | 0.701 | 0.165 | 4.257 | 0.000 |
| RSES_sum | -0.287 | 0.148 | -1.932 | 0.054 |
| SRS_sum | -0.880 | 0.266 | -3.311 | 0.001 |
| SSEIT_sum | 0.450 | 0.081 | 5.549 | 0.000 |
| DASS_D_sum | 0.408 | 0.325 | 1.257 | 0.209 |
| DASS_A_sum | 0.738 | 0.376 | 1.967 | 0.050 |
| DASS_S_sum | 0.579 | 0.394 | 1.471 | 0.142 |
| f_stat | 13.000 | 491.000 | 32.109 | 0.000 |
| R2/Adj.R2 | 0.460 | 0.445 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_NMPQ_plot <-
sjPlot::plot_model(model = mturk_reg_NMPQ, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_NMPQ_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_NMPQ_plot(oct28)", plot = mturk_reg_NMPQ_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_NMPQ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_NMPQ_t(oct28).csv", row.names = T)SAD - Overall
This regression used the following formula: \(SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 43.6071508, p < .001, R2 = 0.54
# run lm
mturk_reg_SAD <- lm(data = Mturk_data, formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_SAD)
Call:
lm(formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-48.848 -6.418 0.259 7.606 30.793
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.23237 10.23239 1.293 0.19656
age -0.15343 0.06013 -2.552 0.01102 *
BIS_sum -0.07438 0.18809 -0.395 0.69269
NEO_O_sum -0.19260 0.09481 -2.031 0.04275 *
NEO_C_sum 0.39212 0.13895 2.822 0.00496 **
NEO_E_sum 0.26211 0.08387 3.125 0.00188 **
NEO_A_sum 0.02920 0.10751 0.272 0.78605
NEO_N_sum 0.39806 0.09726 4.093 4.98e-05 ***
RSES_sum -0.12812 0.08762 -1.462 0.14430
SRS_sum -0.95980 0.15693 -6.116 1.96e-09 ***
SSEIT_sum 0.33966 0.04788 7.094 4.56e-12 ***
DASS_D_sum 0.20670 0.19174 1.078 0.28154
DASS_A_sum 0.68778 0.22180 3.101 0.00204 **
DASS_S_sum 0.17965 0.23271 0.772 0.44049
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.95 on 491 degrees of freedom
Multiple R-squared: 0.5359, Adjusted R-squared: 0.5236
F-statistic: 43.61 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_SAD_t <-
rbind(tidy(mturk_reg_SAD), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_SAD)$fstatistic[2], summary(mturk_reg_SAD)$r.squared, nrow(mturk_reg_SAD$model)),
"std.error" = c(summary(mturk_reg_SAD)$fstatistic[3], summary(mturk_reg_SAD)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_SAD)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_SAD)$fstatistic[1], summary(mturk_reg_SAD)$fstatistic[2], summary(mturk_reg_SAD)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_SAD_t, caption = "Multiple Regression Model Predicting SAD Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 13.232 | 10.232 | 1.293 | 0.197 |
| age | -0.153 | 0.060 | -2.552 | 0.011 |
| BIS_sum | -0.074 | 0.188 | -0.395 | 0.693 |
| NEO_O_sum | -0.193 | 0.095 | -2.031 | 0.043 |
| NEO_C_sum | 0.392 | 0.139 | 2.822 | 0.005 |
| NEO_E_sum | 0.262 | 0.084 | 3.125 | 0.002 |
| NEO_A_sum | 0.029 | 0.108 | 0.272 | 0.786 |
| NEO_N_sum | 0.398 | 0.097 | 4.093 | 0.000 |
| RSES_sum | -0.128 | 0.088 | -1.462 | 0.144 |
| SRS_sum | -0.960 | 0.157 | -6.116 | 0.000 |
| SSEIT_sum | 0.340 | 0.048 | 7.094 | 0.000 |
| DASS_D_sum | 0.207 | 0.192 | 1.078 | 0.282 |
| DASS_A_sum | 0.688 | 0.222 | 3.101 | 0.002 |
| DASS_S_sum | 0.180 | 0.233 | 0.772 | 0.440 |
| f_stat | 13.000 | 491.000 | 43.607 | 0.000 |
| R2/Adj.R2 | 0.536 | 0.524 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_SAD_plot <-
sjPlot::plot_model(model = mturk_reg_SAD, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_SAD_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_plot(oct28)", plot = mturk_reg_SAD_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_SAD_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_t(oct28).csv", row.names = T)SAD - Dependency
This regression used the following formula: \(SAD_dep = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 24.0268599, p < .001, R2 = 0.39
# run lm
mturk_reg_SAD_dep <- lm(data = Mturk_data, formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_SAD_dep)
Call:
lm(formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-12.6332 -2.2595 0.2762 2.3108 10.9460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.955791 3.336644 0.286 0.774652
age -0.040241 0.019606 -2.052 0.040652 *
BIS_sum -0.006676 0.061335 -0.109 0.913370
NEO_O_sum -0.060116 0.030916 -1.945 0.052402 .
NEO_C_sum 0.127500 0.045308 2.814 0.005088 **
NEO_E_sum 0.066252 0.027349 2.422 0.015776 *
NEO_A_sum 0.028183 0.035058 0.804 0.421851
NEO_N_sum 0.106815 0.031715 3.368 0.000817 ***
RSES_sum -0.036435 0.028570 -1.275 0.202813
SRS_sum -0.193096 0.051174 -3.773 0.000181 ***
SSEIT_sum 0.063808 0.015612 4.087 5.1e-05 ***
DASS_D_sum -0.004418 0.062523 -0.071 0.943701
DASS_A_sum 0.179083 0.072326 2.476 0.013620 *
DASS_S_sum 0.117970 0.075884 1.555 0.120685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.898 on 491 degrees of freedom
Multiple R-squared: 0.3888, Adjusted R-squared: 0.3726
F-statistic: 24.03 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_SAD_dep_t <-
rbind(tidy(mturk_reg_SAD_dep), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_SAD_dep)$fstatistic[2], summary(mturk_reg_SAD_dep)$r.squared, nrow(mturk_reg_SAD_dep$model)),
"std.error" = c(summary(mturk_reg_SAD_dep)$fstatistic[3], summary(mturk_reg_SAD_dep)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_SAD_dep)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_SAD_dep)$fstatistic[1], summary(mturk_reg_SAD_dep)$fstatistic[2], summary(mturk_reg_SAD_dep)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_SAD_dep_t, caption = "Multiple Regression Model Predicting SAD_dep Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.956 | 3.337 | 0.286 | 0.775 |
| age | -0.040 | 0.020 | -2.052 | 0.041 |
| BIS_sum | -0.007 | 0.061 | -0.109 | 0.913 |
| NEO_O_sum | -0.060 | 0.031 | -1.945 | 0.052 |
| NEO_C_sum | 0.128 | 0.045 | 2.814 | 0.005 |
| NEO_E_sum | 0.066 | 0.027 | 2.422 | 0.016 |
| NEO_A_sum | 0.028 | 0.035 | 0.804 | 0.422 |
| NEO_N_sum | 0.107 | 0.032 | 3.368 | 0.001 |
| RSES_sum | -0.036 | 0.029 | -1.275 | 0.203 |
| SRS_sum | -0.193 | 0.051 | -3.773 | 0.000 |
| SSEIT_sum | 0.064 | 0.016 | 4.087 | 0.000 |
| DASS_D_sum | -0.004 | 0.063 | -0.071 | 0.944 |
| DASS_A_sum | 0.179 | 0.072 | 2.476 | 0.014 |
| DASS_S_sum | 0.118 | 0.076 | 1.555 | 0.121 |
| f_stat | 13.000 | 491.000 | 24.027 | 0.000 |
| R2/Adj.R2 | 0.389 | 0.373 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_SAD_dep_plot <-
sjPlot::plot_model(model = mturk_reg_SAD_dep, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_SAD_dep_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_dep_plot(oct28)", plot = mturk_reg_SAD_dep_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_SAD_dep_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_dep_t(oct28).csv", row.names = T)SAD - Accessibility
This regression used the following formula: \(SAD_access = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 15.3007298, p < .001, R2 = 0.29
# run lm
mturk_reg_SAD_access <- lm(data = Mturk_data, formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_SAD_access)
Call:
lm(formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-13.235 -2.023 0.375 2.393 9.456
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.26598 3.22644 -1.012 0.311914
age -0.02738 0.01896 -1.444 0.149389
BIS_sum 0.03868 0.05931 0.652 0.514642
NEO_O_sum -0.01248 0.02989 -0.418 0.676449
NEO_C_sum 0.17894 0.04381 4.084 5.16e-05 ***
NEO_E_sum 0.05025 0.02645 1.900 0.057980 .
NEO_A_sum 0.01773 0.03390 0.523 0.601136
NEO_N_sum 0.11401 0.03067 3.718 0.000224 ***
RSES_sum -0.04355 0.02763 -1.576 0.115607
SRS_sum -0.11141 0.04948 -2.251 0.024804 *
SSEIT_sum 0.05552 0.01510 3.678 0.000261 ***
DASS_D_sum 0.06866 0.06046 1.136 0.256623
DASS_A_sum 0.06017 0.06994 0.860 0.389996
DASS_S_sum 0.09398 0.07338 1.281 0.200891
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.77 on 491 degrees of freedom
Multiple R-squared: 0.2883, Adjusted R-squared: 0.2695
F-statistic: 15.3 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_SAD_access_t <-
rbind(tidy(mturk_reg_SAD_access), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_SAD_access)$fstatistic[2], summary(mturk_reg_SAD_access)$r.squared, nrow(mturk_reg_SAD_access$model)),
"std.error" = c(summary(mturk_reg_SAD_access)$fstatistic[3], summary(mturk_reg_SAD_access)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_SAD_access)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_SAD_access)$fstatistic[1], summary(mturk_reg_SAD_access)$fstatistic[2], summary(mturk_reg_SAD_access)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_SAD_access_t, caption = "Multiple Regression Model Predicting SAD_access Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -3.266 | 3.226 | -1.012 | 0.312 |
| age | -0.027 | 0.019 | -1.444 | 0.149 |
| BIS_sum | 0.039 | 0.059 | 0.652 | 0.515 |
| NEO_O_sum | -0.012 | 0.030 | -0.418 | 0.676 |
| NEO_C_sum | 0.179 | 0.044 | 4.084 | 0.000 |
| NEO_E_sum | 0.050 | 0.026 | 1.900 | 0.058 |
| NEO_A_sum | 0.018 | 0.034 | 0.523 | 0.601 |
| NEO_N_sum | 0.114 | 0.031 | 3.718 | 0.000 |
| RSES_sum | -0.044 | 0.028 | -1.576 | 0.116 |
| SRS_sum | -0.111 | 0.049 | -2.251 | 0.025 |
| SSEIT_sum | 0.056 | 0.015 | 3.678 | 0.000 |
| DASS_D_sum | 0.069 | 0.060 | 1.136 | 0.257 |
| DASS_A_sum | 0.060 | 0.070 | 0.860 | 0.390 |
| DASS_S_sum | 0.094 | 0.073 | 1.281 | 0.201 |
| f_stat | 13.000 | 491.000 | 15.301 | 0.000 |
| R2/Adj.R2 | 0.288 | 0.269 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_SAD_access_plot <-
sjPlot::plot_model(model = mturk_reg_SAD_access, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_SAD_access_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_access_plot(oct28)", plot = mturk_reg_SAD_access_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_SAD_access_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_access_t(oct28).csv", row.names = T)SAD - Emotional Attachment
This regression used the following formula: \(SAD_ea = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 42.7396262, p < .001, R2 = 0.53
# run lm
mturk_reg_SAD_ea <- lm(data = Mturk_data, formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_SAD_ea)
Call:
lm(formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-15.406 -2.307 0.234 2.349 12.112
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.790959 3.354981 2.620 0.009058 **
age -0.052280 0.019714 -2.652 0.008261 **
BIS_sum -0.098030 0.061672 -1.590 0.112582
NEO_O_sum -0.069942 0.031086 -2.250 0.024893 *
NEO_C_sum 0.052487 0.045557 1.152 0.249839
NEO_E_sum 0.096066 0.027499 3.493 0.000520 ***
NEO_A_sum -0.045495 0.035251 -1.291 0.197453
NEO_N_sum 0.090811 0.031889 2.848 0.004588 **
RSES_sum -0.006332 0.028727 -0.220 0.825630
SRS_sum -0.333303 0.051455 -6.478 2.27e-10 ***
SSEIT_sum 0.132213 0.015698 8.422 4.09e-16 ***
DASS_D_sum 0.093128 0.062867 1.481 0.139154
DASS_A_sum 0.274020 0.072723 3.768 0.000185 ***
DASS_S_sum -0.085472 0.076301 -1.120 0.263178
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.92 on 491 degrees of freedom
Multiple R-squared: 0.5309, Adjusted R-squared: 0.5184
F-statistic: 42.74 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_SAD_ea_t <-
rbind(tidy(mturk_reg_SAD_ea), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_SAD_ea)$fstatistic[2], summary(mturk_reg_SAD_ea)$r.squared, nrow(mturk_reg_SAD_ea$model)),
"std.error" = c(summary(mturk_reg_SAD_ea)$fstatistic[3], summary(mturk_reg_SAD_ea)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_SAD_ea)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_SAD_ea)$fstatistic[1], summary(mturk_reg_SAD_ea)$fstatistic[2], summary(mturk_reg_SAD_ea)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_SAD_ea_t, caption = "Multiple Regression Model Predicting SAD_ea Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 8.791 | 3.355 | 2.620 | 0.009 |
| age | -0.052 | 0.020 | -2.652 | 0.008 |
| BIS_sum | -0.098 | 0.062 | -1.590 | 0.113 |
| NEO_O_sum | -0.070 | 0.031 | -2.250 | 0.025 |
| NEO_C_sum | 0.052 | 0.046 | 1.152 | 0.250 |
| NEO_E_sum | 0.096 | 0.027 | 3.493 | 0.001 |
| NEO_A_sum | -0.045 | 0.035 | -1.291 | 0.197 |
| NEO_N_sum | 0.091 | 0.032 | 2.848 | 0.005 |
| RSES_sum | -0.006 | 0.029 | -0.220 | 0.826 |
| SRS_sum | -0.333 | 0.051 | -6.478 | 0.000 |
| SSEIT_sum | 0.132 | 0.016 | 8.422 | 0.000 |
| DASS_D_sum | 0.093 | 0.063 | 1.481 | 0.139 |
| DASS_A_sum | 0.274 | 0.073 | 3.768 | 0.000 |
| DASS_S_sum | -0.085 | 0.076 | -1.120 | 0.263 |
| f_stat | 13.000 | 491.000 | 42.740 | 0.000 |
| R2/Adj.R2 | 0.531 | 0.518 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_SAD_ea_plot <-
sjPlot::plot_model(model = mturk_reg_SAD_ea, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_SAD_ea_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_ea_plot(oct28)", plot = mturk_reg_SAD_ea_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_SAD_ea_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_ea_t(oct28).csv", row.names = T)SAD - Distractibility
This regression used the following formula: \(SAD_dist = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S\)
Results indicate that:
- over all model was significant, F(13, 491) = 60.8601971, p < .001, R2 = 0.62
# run lm
mturk_reg_SAD_dist <- lm(data = Mturk_data, formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)
# show summary of lm
summary(mturk_reg_SAD_dist)
Call:
lm(formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum +
SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-7.848 -1.325 0.077 1.411 7.510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.816919 1.790808 4.923 1.16e-06 ***
age -0.021940 0.010523 -2.085 0.037582 *
BIS_sum -0.001808 0.032919 -0.055 0.956233
NEO_O_sum -0.049463 0.016593 -2.981 0.003015 **
NEO_C_sum -0.025952 0.024317 -1.067 0.286391
NEO_E_sum 0.050035 0.014678 3.409 0.000706 ***
NEO_A_sum 0.006152 0.018816 0.327 0.743842
NEO_N_sum 0.031955 0.017022 1.877 0.061069 .
RSES_sum -0.020690 0.015334 -1.349 0.177872
SRS_sum -0.256086 0.027466 -9.324 < 2e-16 ***
SSEIT_sum 0.055437 0.008379 6.616 9.66e-11 ***
DASS_D_sum 0.034926 0.033557 1.041 0.298479
DASS_A_sum 0.133196 0.038818 3.431 0.000651 ***
DASS_S_sum 0.027792 0.040728 0.682 0.495324
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.092 on 491 degrees of freedom
Multiple R-squared: 0.6171, Adjusted R-squared: 0.6069
F-statistic: 60.86 on 13 and 491 DF, p-value: < 2.2e-16# create table depicting main stats to export later
mturk_reg_SAD_dist_t <-
rbind(tidy(mturk_reg_SAD_dist), # use tidy() in broom package
tibble("term" = c("f_stat", "R2/Adj.R2", "N"),
"estimate" = c(summary(mturk_reg_SAD_dist)$fstatistic[2], summary(mturk_reg_SAD_dist)$r.squared, nrow(mturk_reg_SAD_dist$model)),
"std.error" = c(summary(mturk_reg_SAD_dist)$fstatistic[3], summary(mturk_reg_SAD_dist)$adj.r.squared, NA),
"statistic" = c(summary(mturk_reg_SAD_dist)$fstatistic[1], rep(NA, 2)),
"p.value" = c(pf(summary(mturk_reg_SAD_dist)$fstatistic[1], summary(mturk_reg_SAD_dist)$fstatistic[2], summary(mturk_reg_SAD_dist)$fstatistic[3], lower.tail = F), rep(NA, 2))
)
)
# remove NA from kable tables
options(knitr.kable.NA = '')
# show kable table
kable(mturk_reg_SAD_dist_t, caption = "Multiple Regression Model Predicting SAD_dist Score", align = rep('lcccc'), digits =3) %>%
kable_paper(full_width = F) %>%
# add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
# pack_rows("Criterion(s)", 1, 6) %>%
# # column_spec(1, width = "10em") %>%
# pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 8.817 | 1.791 | 4.923 | 0.000 |
| age | -0.022 | 0.011 | -2.085 | 0.038 |
| BIS_sum | -0.002 | 0.033 | -0.055 | 0.956 |
| NEO_O_sum | -0.049 | 0.017 | -2.981 | 0.003 |
| NEO_C_sum | -0.026 | 0.024 | -1.067 | 0.286 |
| NEO_E_sum | 0.050 | 0.015 | 3.409 | 0.001 |
| NEO_A_sum | 0.006 | 0.019 | 0.327 | 0.744 |
| NEO_N_sum | 0.032 | 0.017 | 1.877 | 0.061 |
| RSES_sum | -0.021 | 0.015 | -1.349 | 0.178 |
| SRS_sum | -0.256 | 0.027 | -9.324 | 0.000 |
| SSEIT_sum | 0.055 | 0.008 | 6.616 | 0.000 |
| DASS_D_sum | 0.035 | 0.034 | 1.041 | 0.298 |
| DASS_A_sum | 0.133 | 0.039 | 3.431 | 0.001 |
| DASS_S_sum | 0.028 | 0.041 | 0.682 | 0.495 |
| f_stat | 13.000 | 491.000 | 60.860 | 0.000 |
| R2/Adj.R2 | 0.617 | 0.607 | ||
| N | 505.000 | |||
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). |
# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA)
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange
# plot lm
# plot standardized betas to compare cooefficients
mturk_reg_SAD_dist_plot <-
sjPlot::plot_model(model = mturk_reg_SAD_dist, # lm output
# title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
title = "", # to remove title
type = "std", # type "std" = standardized betas
#sort.est = T, # sort based on betas (highest on top)
wrap.title = 55, # char length of each title line
# axis.labels = c("INSERT PRED NAMES")
grid.breaks = .25, # x-axis breaks
#se = T # use se instead of CI
colors = reg_colours,
show.values = T, # show betas
show.p = T, # show * for sig p
value.offset = 0.40, # offset to beta labels
value.size = 3, # size of beta labels
digits = 2, # num decimals in beta labels
dot.size = 2.5, line.size = 1.5,
vline.color = "black",
group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"),
axis.title = "Standardized Beta Coefficient",
width = .5
) +
font_size(offset.x = 10) +
font_size(base.theme = theme_classic())
# show plot
mturk_reg_SAD_dist_plot
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_dist_plot(oct28)", plot = mturk_reg_SAD_dist_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
# export csv files of lm table
write.csv(mturk_reg_SAD_dist_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_dist_t(oct28).csv", row.names = T)Exploratory Analyses: Assessing Screen Time
This data only included ps who reported both having an iPhone (SONA = 209; Mturk = 248) and had ST activated prior to the study (SONA = 175 ; Mturk = 162) rather than the total sample (SONA = 240; Mturk = 505).
Assumption Tests
All assumptions were met for the correlation analysis. Click here to see the assumption tests.
The assumptions for a correlation analysis were tested. There were two assumptions: (1) Linearity and (2) Normality.
Linearity was visually inspected from the scatter plots (see plots in the “Figures: Scatter Plots” section).
Normality was tested using (a) the Shapiro-Wilk normality test and
- visually inspected using a normality plot.
The Shapiro-Wilk normality test will show two values (i.e., W and p) and each test uses the following hypotheses: HN: the data are normally distributed and HA: the data are not normally distributed.
The normality plots used were Q-Q plots (quantile-quantile plots), which draw the correlation between a given sample and the theoretical normal distribution.
(a) Shapiro-Wilk Normality Test
The table depicts the results of the Shapiro-Wilks normality test for each measure. (click to see code)
# create a table to show results of all normality tests
corrs_reg_norm_test_sona_ST <-
as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D",
"DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"),
rbind(c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_sum)$p.value), # W & p-value for SAD_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$age)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$age)$p.value), # W & p-value for age
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SRS_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SRS_sum)$p.value), # W & p-value for SRS_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$BIS_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$BIS_sum)$p.value), # W & p-value for BIS_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$RSES_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$RSES_sum)$p.value), # W & p-value for RSES_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
)))
colnames(corrs_reg_norm_test_sona_ST) <- c("Measure", "W", "p")
# make cols numeric & round to 3 decimals
corrs_reg_norm_test_sona_ST$W <- as.numeric(corrs_reg_norm_test_sona_ST$W) %>% round(.,3)
corrs_reg_norm_test_sona_ST$p <- as.numeric(corrs_reg_norm_test_sona_ST$p) %>% round(.,3)
corrs_reg_norm_test_mturk_ST <-
as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D",
"DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"),
rbind(c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_sum)$p.value), # W & p-value for SAD_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$age)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$age)$p.value), # W & p-value for age
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SRS_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SRS_sum)$p.value), # W & p-value for SRS_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$BIS_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$BIS_sum)$p.value), # W & p-value for BIS_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$RSES_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$RSES_sum)$p.value), # W & p-value for RSES_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
)))
colnames(corrs_reg_norm_test_mturk_ST) <- c("Measure", "W", "p")
# make cols numeric & round to 3 decimals
corrs_reg_norm_test_mturk_ST$W <- as.numeric(corrs_reg_norm_test_mturk_ST$W) %>% round(.,3)
corrs_reg_norm_test_mturk_ST$p <- as.numeric(corrs_reg_norm_test_mturk_ST$p) %>% round(.,3)
# join into 1 large table
corrs_reg_norm_tests_ST_t <- cbind(corrs_reg_norm_test_sona_ST, corrs_reg_norm_test_mturk_ST[,2:3])As seen in the table, most measures showed a significant result, indicating non-normality. The visual inspection of the Q-Q plots was completed next.
# show table using kable
kable(corrs_reg_norm_tests_ST_t, caption = "Shapiro-Wilks Normality Test for All Measures -- ST Data only", align = rep('lcccc')) %>%
kable_paper(full_width = F) %>%
add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
pack_rows("Criterion(s)", 1, 6) %>%
# column_spec(1, width = "10em") %>%
pack_rows("Predictor(s)", 7, 19) %>%
footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>%
column_spec(c(4), border_left = T) %>%
kable_classic()SONA |
Mturk |
|||
|---|---|---|---|---|
| Measure | W | p | W | p |
| Criterion(s) | ||||
| NMPQ | 0.986 | 0.084 | 0.914 | 0.000 |
| SAD | 0.967 | 0.000 | 0.930 | 0.000 |
| SAD_dep | 0.958 | 0.000 | 0.917 | 0.000 |
| SAD_access | 0.959 | 0.000 | 0.893 | 0.000 |
| SAD_ea | 0.970 | 0.001 | 0.917 | 0.000 |
| SAD_dist | 0.942 | 0.000 | 0.903 | 0.000 |
| Predictor(s) | ||||
| age | 0.743 | 0.000 | 0.782 | 0.000 |
| SRS | 0.973 | 0.002 | 0.947 | 0.000 |
| DASS_D | 0.939 | 0.000 | 0.903 | 0.000 |
| DASS_A | 0.954 | 0.000 | 0.903 | 0.000 |
| DASS_S | 0.977 | 0.005 | 0.919 | 0.000 |
| BIS | 0.983 | 0.036 | 0.965 | 0.000 |
| SSEIT | 0.961 | 0.000 | 0.981 | 0.028 |
| RSES | 0.973 | 0.002 | 0.901 | 0.000 |
| NEO_N | 0.990 | 0.261 | 0.897 | 0.000 |
| NEO_E | 0.986 | 0.083 | 0.955 | 0.000 |
| NEO_O | 0.988 | 0.130 | 0.943 | 0.000 |
| NEO_A | 0.974 | 0.002 | 0.915 | 0.000 |
| NEO_C | 0.993 | 0.552 | 0.960 | 0.000 |
| Note: | ||||
| Normality is met for non-significant p-values (i.e., greater than 0.05). | ||||
(b) Q-Q Plots
Overall, the Q-Q plots show the data for all measures except age and age of first smartphone did fall along or close to the line. The age measures were not expected to be normally distributed; therefore, the analysis was completed for each measure. (click to see code)
# SONA - All
grid.arrange(ggqqplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, ylab = "NMPQ"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_sum, ylab = "SAD"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum, ylab = "SAD_dep"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum, ylab = "SAD_access"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum, ylab = "SAD_ea"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum, ylab = "SAD_dist"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$age, ylab = "age"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SRS_sum, ylab = "SRS"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum, ylab = "DASS_D"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum, ylab = "DASS_A"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum, ylab = "DASS_S"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$BIS_sum, ylab = "BIS"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum, ylab = "SSEIT"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$RSES_sum, ylab = "RSES"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum, ylab = "NEO_N"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum, ylab = "NEO_E"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum, ylab = "NEO_O"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum, ylab = "NEO_A"),
ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum, ylab = "NEO_C"),
nrow = 4,
top = text_grob("Q-Q Plots For SONA - ST",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to the line.",
face = "italic",
x = 0.05,
hjust = 0)
)
# for MTURK - ALL
grid.arrange(ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, ylab = "NMPQ"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_sum, ylab = "SAD"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum, ylab = "SAD_dep"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum, ylab = "SAD_access"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum, ylab = "SAD_ea"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum, ylab = "SAD_dist"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$age, ylab = "age"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SRS_sum, ylab = "SRS"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum, ylab = "DASS_D"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum, ylab = "DASS_A"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum, ylab = "DASS_S"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$BIS_sum, ylab = "BIS"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum, ylab = "SSEIT"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$RSES_sum, ylab = "RSES"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum, ylab = "NEO_N"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum, ylab = "NEO_E"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum, ylab = "NEO_O"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum, ylab = "NEO_A"),
ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum, ylab = "NEO_C"),
nrow = 4,
top = text_grob("Q-Q Plots For Mturk - ST",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to the line.",
face = "italic",
x = 0.05,
hjust = 0)
)
(c) Histogram Plots
Overall, the Q-Q plots show the data for all measures except age and age of first smartphone did fall along or close to the line. The age measures were not expected to be normally distributed; therefore, the analysis was completed for each measure. (click to see code)
# SONA - ST
grid.arrange(qplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
# qplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, geom = "density"),
qplot((SONA_data %>% filter(ST == "had ST"))$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
nrow = 4,
top = text_grob("Histogram For SONA Data - ST",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.",
face = "italic",
x = 0.05,
hjust = 0)
)
# for MTURK - ST
grid.arrange(qplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
# qplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, geom = "density"),
qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
nrow = 4,
top = text_grob("Histogram For Mturk Data - ST",
face = "bold"),
bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.",
face = "italic",
x = 0.05,
hjust = 0)
)
Correlations
This data only included ps who reported both having an iPhone (SONA = 209; Mturk = 248) and had ST activated prior to the study (SONA = 175 ; Mturk = 162) rather than the total sample (SONA = 240; Mturk = 505).
Study 1: SONA
sona_ST_corr <-
SONA_data %>%
filter(ST == "had ST") %>%
# select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study,
NMPQ_sum,
SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>%
as.matrix() %>%
Hmisc::rcorr(type = c("pearson"))
# create new pilot_corr to shown only lower triangle...
sona_ST_corr2 <- sona_ST_corr
# round to 4 decimals...
sona_ST_corr2$r <- round(sona_ST_corr2$r, 4)
sona_ST_corr2$P <- round(sona_ST_corr2$P, 4)
sona_ST_corr2$n <- round(sona_ST_corr2$n, 4)
# remove upper triangle form r, p, and n
sona_ST_corr2$r[upper.tri(sona_ST_corr2$r)] <- "-"
sona_ST_corr2$P[upper.tri(sona_ST_corr2$P)] <- "-"
sona_ST_corr2$n[upper.tri(sona_ST_corr2$n)] <- "-"
# show corr table with flattenCorr
kable(flattenCorrMatrix(sona_ST_corr$r, sona_ST_corr$P), caption = "SONA Study - ST - Correlation: Flat") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| row | column | cor | p |
|---|---|---|---|
| age | age_first_phone | 0.1688723 | 0.0254811 |
| age | com_gen | 0.0972295 | 0.2005302 |
| age_first_phone | com_gen | 0.1648607 | 0.0292416 |
| age | com_unattended | 0.1962586 | 0.0092408 |
| age_first_phone | com_unattended | 0.0996381 | 0.1895507 |
| com_gen | com_unattended | 0.3953597 | 0.0000001 |
| age | com_leave_with_others | 0.1942871 | 0.0099848 |
| age_first_phone | com_leave_with_others | 0.1313528 | 0.0831496 |
| com_gen | com_leave_with_others | 0.5685787 | 0.0000000 |
| com_unattended | com_leave_with_others | 0.6045518 | 0.0000000 |
| age | com_locked | -0.1294428 | 0.0877689 |
| age_first_phone | com_locked | -0.1620136 | 0.0321894 |
| com_gen | com_locked | -0.1791602 | 0.0176773 |
| com_unattended | com_locked | -0.1014291 | 0.1816762 |
| com_leave_with_others | com_locked | -0.2107763 | 0.0051126 |
| age | com_room_task | 0.0853554 | 0.2613931 |
| age_first_phone | com_room_task | 0.0625501 | 0.4108859 |
| com_gen | com_room_task | 0.3753834 | 0.0000003 |
| com_unattended | com_room_task | 0.4602589 | 0.0000000 |
| com_leave_with_others | com_room_task | 0.4362201 | 0.0000000 |
| com_locked | com_room_task | 0.0285460 | 0.7076613 |
| age | dist_daily | -0.0703934 | 0.3546055 |
| age_first_phone | dist_daily | -0.1634567 | 0.0306652 |
| com_gen | dist_daily | 0.0969812 | 0.2016877 |
| com_unattended | dist_daily | 0.0908530 | 0.2318005 |
| com_leave_with_others | dist_daily | 0.0176549 | 0.8166183 |
| com_locked | dist_daily | 0.2120712 | 0.0048406 |
| com_room_task | dist_daily | 0.0327543 | 0.6669711 |
| age | dist_study | -0.0236268 | 0.7562893 |
| age_first_phone | dist_study | -0.0402072 | 0.5972956 |
| com_gen | dist_study | -0.1208710 | 0.1110791 |
| com_unattended | dist_study | -0.1910365 | 0.0113275 |
| com_leave_with_others | dist_study | -0.1691085 | 0.0252732 |
| com_locked | dist_study | -0.0846563 | 0.2653339 |
| com_room_task | dist_study | -0.2249017 | 0.0027690 |
| dist_daily | dist_study | 0.3409685 | 0.0000039 |
| age | NMPQ_sum | 0.0472854 | 0.5343418 |
| age_first_phone | NMPQ_sum | -0.0795809 | 0.2951583 |
| com_gen | NMPQ_sum | -0.0955740 | 0.2083388 |
| com_unattended | NMPQ_sum | -0.2068664 | 0.0060189 |
| com_leave_with_others | NMPQ_sum | -0.1879610 | 0.0127415 |
| com_locked | NMPQ_sum | 0.1846129 | 0.0144548 |
| com_room_task | NMPQ_sum | -0.1976222 | 0.0087553 |
| dist_daily | NMPQ_sum | 0.2577841 | 0.0005729 |
| dist_study | NMPQ_sum | 0.2776287 | 0.0001994 |
| age | SAD_sum | -0.0576671 | 0.4484340 |
| age_first_phone | SAD_sum | -0.1399986 | 0.0646246 |
| com_gen | SAD_sum | -0.0834805 | 0.2720527 |
| com_unattended | SAD_sum | -0.1350661 | 0.0747285 |
| com_leave_with_others | SAD_sum | -0.1831861 | 0.0152441 |
| com_locked | SAD_sum | 0.2493271 | 0.0008767 |
| com_room_task | SAD_sum | -0.1761044 | 0.0197430 |
| dist_daily | SAD_sum | 0.3406136 | 0.0000040 |
| dist_study | SAD_sum | 0.2576632 | 0.0005765 |
| NMPQ_sum | SAD_sum | 0.7570728 | 0.0000000 |
| age | SAD_dep_sum | -0.0315556 | 0.6784690 |
| age_first_phone | SAD_dep_sum | -0.0890978 | 0.2409803 |
| com_gen | SAD_dep_sum | -0.1372909 | 0.0700230 |
| com_unattended | SAD_dep_sum | -0.1705544 | 0.0240322 |
| com_leave_with_others | SAD_dep_sum | -0.2377604 | 0.0015334 |
| com_locked | SAD_dep_sum | 0.1913094 | 0.0112089 |
| com_room_task | SAD_dep_sum | -0.2763838 | 0.0002136 |
| dist_daily | SAD_dep_sum | 0.2472923 | 0.0009692 |
| dist_study | SAD_dep_sum | 0.2105099 | 0.0051702 |
| NMPQ_sum | SAD_dep_sum | 0.6707269 | 0.0000000 |
| SAD_sum | SAD_dep_sum | 0.8425951 | 0.0000000 |
| age | SAD_ea_sum | -0.0871586 | 0.2514145 |
| age_first_phone | SAD_ea_sum | -0.0979790 | 0.1970651 |
| com_gen | SAD_ea_sum | -0.0653270 | 0.3903852 |
| com_unattended | SAD_ea_sum | -0.1541136 | 0.0417200 |
| com_leave_with_others | SAD_ea_sum | -0.1032765 | 0.1738084 |
| com_locked | SAD_ea_sum | 0.1956892 | 0.0094504 |
| com_room_task | SAD_ea_sum | -0.0718206 | 0.3449100 |
| dist_daily | SAD_ea_sum | 0.2348180 | 0.0017603 |
| dist_study | SAD_ea_sum | 0.2019989 | 0.0073460 |
| NMPQ_sum | SAD_ea_sum | 0.5849794 | 0.0000000 |
| SAD_sum | SAD_ea_sum | 0.8175759 | 0.0000000 |
| SAD_dep_sum | SAD_ea_sum | 0.5009174 | 0.0000000 |
| age | SAD_access_sum | 0.0299549 | 0.6939391 |
| age_first_phone | SAD_access_sum | -0.1660057 | 0.0281229 |
| com_gen | SAD_access_sum | -0.0888762 | 0.2421573 |
| com_unattended | SAD_access_sum | -0.0651298 | 0.3918202 |
| com_leave_with_others | SAD_access_sum | -0.1573667 | 0.0375417 |
| com_locked | SAD_access_sum | 0.2253985 | 0.0027081 |
| com_room_task | SAD_access_sum | -0.1247957 | 0.0998668 |
| dist_daily | SAD_access_sum | 0.3008648 | 0.0000522 |
| dist_study | SAD_access_sum | 0.1841305 | 0.0147176 |
| NMPQ_sum | SAD_access_sum | 0.6641953 | 0.0000000 |
| SAD_sum | SAD_access_sum | 0.8123703 | 0.0000000 |
| SAD_dep_sum | SAD_access_sum | 0.6148846 | 0.0000000 |
| SAD_ea_sum | SAD_access_sum | 0.5492330 | 0.0000000 |
| age | SAD_dist_sum | -0.1110425 | 0.1434796 |
| age_first_phone | SAD_dist_sum | -0.0676824 | 0.3734881 |
| com_gen | SAD_dist_sum | 0.0495967 | 0.5145287 |
| com_unattended | SAD_dist_sum | 0.0078958 | 0.9174038 |
| com_leave_with_others | SAD_dist_sum | -0.0252533 | 0.7400942 |
| com_locked | SAD_dist_sum | 0.1931676 | 0.0104304 |
| com_room_task | SAD_dist_sum | -0.0534328 | 0.4825013 |
| dist_daily | SAD_dist_sum | 0.3618681 | 0.0000009 |
| dist_study | SAD_dist_sum | 0.2122725 | 0.0047995 |
| NMPQ_sum | SAD_dist_sum | 0.3570847 | 0.0000012 |
| SAD_sum | SAD_dist_sum | 0.6418540 | 0.0000000 |
| SAD_dep_sum | SAD_dist_sum | 0.4022527 | 0.0000000 |
| SAD_ea_sum | SAD_dist_sum | 0.5371919 | 0.0000000 |
| SAD_access_sum | SAD_dist_sum | 0.3453312 | 0.0000029 |
| age | BIS_sum | 0.0618765 | 0.4159527 |
| age_first_phone | BIS_sum | -0.0862467 | 0.2564274 |
| com_gen | BIS_sum | -0.0022480 | 0.9764460 |
| com_unattended | BIS_sum | -0.0122131 | 0.8725565 |
| com_leave_with_others | BIS_sum | -0.0285823 | 0.7073068 |
| com_locked | BIS_sum | -0.1393615 | 0.0658632 |
| com_room_task | BIS_sum | -0.0044090 | 0.9538217 |
| dist_daily | BIS_sum | 0.0558306 | 0.4630409 |
| dist_study | BIS_sum | 0.2369060 | 0.0015964 |
| NMPQ_sum | BIS_sum | 0.0661642 | 0.3843270 |
| SAD_sum | BIS_sum | 0.1333432 | 0.0785456 |
| SAD_dep_sum | BIS_sum | 0.1658938 | 0.0282306 |
| SAD_ea_sum | BIS_sum | 0.0775315 | 0.3078088 |
| SAD_access_sum | BIS_sum | 0.0399414 | 0.5997238 |
| SAD_dist_sum | BIS_sum | 0.1206317 | 0.1117933 |
| age | NEO_O_sum | 0.0729699 | 0.3372245 |
| age_first_phone | NEO_O_sum | -0.0219482 | 0.7731163 |
| com_gen | NEO_O_sum | 0.0383700 | 0.6141665 |
| com_unattended | NEO_O_sum | 0.1256026 | 0.0976765 |
| com_leave_with_others | NEO_O_sum | 0.0658477 | 0.3866101 |
| com_locked | NEO_O_sum | 0.1044306 | 0.1690228 |
| com_room_task | NEO_O_sum | 0.1158949 | 0.1266841 |
| dist_daily | NEO_O_sum | 0.2042253 | 0.0067097 |
| dist_study | NEO_O_sum | -0.0437646 | 0.5652390 |
| NMPQ_sum | NEO_O_sum | -0.0854043 | 0.2611191 |
| SAD_sum | NEO_O_sum | -0.0518662 | 0.4954490 |
| SAD_dep_sum | NEO_O_sum | -0.1587019 | 0.0359319 |
| SAD_ea_sum | NEO_O_sum | -0.0330323 | 0.6643157 |
| SAD_access_sum | NEO_O_sum | 0.0000346 | 0.9996374 |
| SAD_dist_sum | NEO_O_sum | 0.1140937 | 0.1327321 |
| BIS_sum | NEO_O_sum | 0.0068489 | 0.9283239 |
| age | NEO_C_sum | 0.0075447 | 0.9210645 |
| age_first_phone | NEO_C_sum | 0.0467311 | 0.5391493 |
| com_gen | NEO_C_sum | -0.0528792 | 0.4870560 |
| com_unattended | NEO_C_sum | 0.0356694 | 0.6393345 |
| com_leave_with_others | NEO_C_sum | -0.0051054 | 0.9465379 |
| com_locked | NEO_C_sum | 0.1805264 | 0.0168162 |
| com_room_task | NEO_C_sum | 0.0363486 | 0.6329642 |
| dist_daily | NEO_C_sum | -0.0270101 | 0.7227290 |
| dist_study | NEO_C_sum | -0.1276374 | 0.0923216 |
| NMPQ_sum | NEO_C_sum | -0.0504330 | 0.5074547 |
| SAD_sum | NEO_C_sum | -0.1309741 | 0.0840495 |
| SAD_dep_sum | NEO_C_sum | -0.2111299 | 0.0050370 |
| SAD_ea_sum | NEO_C_sum | -0.0836134 | 0.2712875 |
| SAD_access_sum | NEO_C_sum | -0.0176174 | 0.8170004 |
| SAD_dist_sum | NEO_C_sum | -0.0317457 | 0.6766403 |
| BIS_sum | NEO_C_sum | -0.6902587 | 0.0000000 |
| NEO_O_sum | NEO_C_sum | 0.0823753 | 0.2784728 |
| age | NEO_E_sum | 0.0507162 | 0.5050703 |
| age_first_phone | NEO_E_sum | -0.0601106 | 0.4294080 |
| com_gen | NEO_E_sum | 0.1739198 | 0.0213450 |
| com_unattended | NEO_E_sum | 0.1012908 | 0.1822756 |
| com_leave_with_others | NEO_E_sum | 0.1266477 | 0.0948961 |
| com_locked | NEO_E_sum | 0.2079020 | 0.0057659 |
| com_room_task | NEO_E_sum | 0.1762302 | 0.0196540 |
| dist_daily | NEO_E_sum | 0.2447902 | 0.0010951 |
| dist_study | NEO_E_sum | 0.0245084 | 0.7474974 |
| NMPQ_sum | NEO_E_sum | 0.0838447 | 0.2699592 |
| SAD_sum | NEO_E_sum | 0.0710299 | 0.3502605 |
| SAD_dep_sum | NEO_E_sum | -0.0383420 | 0.6144253 |
| SAD_ea_sum | NEO_E_sum | 0.1172171 | 0.1223815 |
| SAD_access_sum | NEO_E_sum | 0.0690751 | 0.3637121 |
| SAD_dist_sum | NEO_E_sum | 0.1484915 | 0.0498595 |
| BIS_sum | NEO_E_sum | -0.1619068 | 0.0323046 |
| NEO_O_sum | NEO_E_sum | 0.3887823 | 0.0000001 |
| NEO_C_sum | NEO_E_sum | 0.3619939 | 0.0000009 |
| age | NEO_A_sum | 0.0153008 | 0.8407210 |
| age_first_phone | NEO_A_sum | 0.0334214 | 0.6606047 |
| com_gen | NEO_A_sum | 0.2735258 | 0.0002497 |
| com_unattended | NEO_A_sum | 0.1949777 | 0.0097184 |
| com_leave_with_others | NEO_A_sum | 0.2501818 | 0.0008404 |
| com_locked | NEO_A_sum | 0.0884671 | 0.2443402 |
| com_room_task | NEO_A_sum | 0.1999953 | 0.0079640 |
| dist_daily | NEO_A_sum | 0.1560412 | 0.0391995 |
| dist_study | NEO_A_sum | -0.1535253 | 0.0425158 |
| NMPQ_sum | NEO_A_sum | -0.0142654 | 0.8513703 |
| SAD_sum | NEO_A_sum | -0.1156601 | 0.1274601 |
| SAD_dep_sum | NEO_A_sum | -0.1289943 | 0.0888828 |
| SAD_ea_sum | NEO_A_sum | -0.1474464 | 0.0515095 |
| SAD_access_sum | NEO_A_sum | -0.0172594 | 0.8206562 |
| SAD_dist_sum | NEO_A_sum | -0.0439335 | 0.5637371 |
| BIS_sum | NEO_A_sum | -0.2728845 | 0.0002585 |
| NEO_O_sum | NEO_A_sum | 0.2340391 | 0.0018253 |
| NEO_C_sum | NEO_A_sum | 0.3789149 | 0.0000002 |
| NEO_E_sum | NEO_A_sum | 0.2879350 | 0.0001116 |
| age | NEO_N_sum | 0.0584415 | 0.4423535 |
| age_first_phone | NEO_N_sum | 0.0179703 | 0.8134012 |
| com_gen | NEO_N_sum | -0.0274404 | 0.7184965 |
| com_unattended | NEO_N_sum | 0.0346750 | 0.6487089 |
| com_leave_with_others | NEO_N_sum | 0.0188598 | 0.8043471 |
| com_locked | NEO_N_sum | -0.0456978 | 0.5481684 |
| com_room_task | NEO_N_sum | -0.0258273 | 0.7344055 |
| dist_daily | NEO_N_sum | 0.1884676 | 0.0124984 |
| dist_study | NEO_N_sum | 0.1506377 | 0.0466089 |
| NMPQ_sum | NEO_N_sum | 0.2009244 | 0.0076719 |
| SAD_sum | NEO_N_sum | 0.2196188 | 0.0034981 |
| SAD_dep_sum | NEO_N_sum | 0.2156473 | 0.0041556 |
| SAD_ea_sum | NEO_N_sum | 0.1382264 | 0.0681179 |
| SAD_access_sum | NEO_N_sum | 0.1802215 | 0.0170052 |
| SAD_dist_sum | NEO_N_sum | 0.1654218 | 0.0286888 |
| BIS_sum | NEO_N_sum | 0.3827954 | 0.0000002 |
| NEO_O_sum | NEO_N_sum | -0.0029449 | 0.9691465 |
| NEO_C_sum | NEO_N_sum | -0.3445181 | 0.0000030 |
| NEO_E_sum | NEO_N_sum | -0.2358217 | 0.0016797 |
| NEO_A_sum | NEO_N_sum | -0.0283753 | 0.7093309 |
| age | RSES_sum | -0.0860646 | 0.2574366 |
| age_first_phone | RSES_sum | -0.0193382 | 0.7994873 |
| com_gen | RSES_sum | -0.0800603 | 0.2922492 |
| com_unattended | RSES_sum | -0.0342580 | 0.6526565 |
| com_leave_with_others | RSES_sum | 0.0428018 | 0.5738332 |
| com_locked | RSES_sum | -0.1370273 | 0.0705677 |
| com_room_task | RSES_sum | -0.0490359 | 0.5193015 |
| dist_daily | RSES_sum | -0.0539158 | 0.4785461 |
| dist_study | RSES_sum | -0.0427553 | 0.5742503 |
| NMPQ_sum | RSES_sum | 0.0193572 | 0.7992948 |
| SAD_sum | RSES_sum | 0.0132049 | 0.8623052 |
| SAD_dep_sum | RSES_sum | 0.0627251 | 0.4095756 |
| SAD_ea_sum | RSES_sum | -0.0109395 | 0.8857503 |
| SAD_access_sum | RSES_sum | 0.0393978 | 0.6047023 |
| SAD_dist_sum | RSES_sum | -0.0938683 | 0.2166102 |
| BIS_sum | RSES_sum | 0.0796187 | 0.2949279 |
| NEO_O_sum | RSES_sum | -0.1264470 | 0.0954251 |
| NEO_C_sum | RSES_sum | -0.1746016 | 0.0208334 |
| NEO_E_sum | RSES_sum | -0.3750939 | 0.0000003 |
| NEO_A_sum | RSES_sum | 0.0132418 | 0.8619248 |
| NEO_N_sum | RSES_sum | 0.3909726 | 0.0000001 |
| age | SRS_sum | 0.0075466 | 0.9210443 |
| age_first_phone | SRS_sum | 0.1490404 | 0.0490108 |
| com_gen | SRS_sum | 0.2207372 | 0.0033307 |
| com_unattended | SRS_sum | 0.1074086 | 0.1571282 |
| com_leave_with_others | SRS_sum | 0.1798413 | 0.0172434 |
| com_locked | SRS_sum | 0.0624479 | 0.4116525 |
| com_room_task | SRS_sum | 0.2341012 | 0.0018201 |
| dist_daily | SRS_sum | -0.0927670 | 0.2220738 |
| dist_study | SRS_sum | -0.2087932 | 0.0055557 |
| NMPQ_sum | SRS_sum | -0.1374102 | 0.0697778 |
| SAD_sum | SRS_sum | -0.1044563 | 0.1689176 |
| SAD_dep_sum | SRS_sum | -0.1526780 | 0.0436843 |
| SAD_ea_sum | SRS_sum | 0.0030769 | 0.9677644 |
| SAD_access_sum | SRS_sum | -0.0783656 | 0.3026176 |
| SAD_dist_sum | SRS_sum | -0.1022358 | 0.1782088 |
| BIS_sum | SRS_sum | -0.5099163 | 0.0000000 |
| NEO_O_sum | SRS_sum | -0.0028289 | 0.9703619 |
| NEO_C_sum | SRS_sum | 0.4028258 | 0.0000000 |
| NEO_E_sum | SRS_sum | 0.2636880 | 0.0004221 |
| NEO_A_sum | SRS_sum | 0.1433851 | 0.0583570 |
| NEO_N_sum | SRS_sum | -0.3624753 | 0.0000008 |
| RSES_sum | SRS_sum | -0.2578226 | 0.0005718 |
| age | SSEIT_sum | 0.0956789 | 0.2078375 |
| age_first_phone | SSEIT_sum | -0.0066587 | 0.9303093 |
| com_gen | SSEIT_sum | 0.1988311 | 0.0083439 |
| com_unattended | SSEIT_sum | 0.2297235 | 0.0022268 |
| com_leave_with_others | SSEIT_sum | 0.1495985 | 0.0481601 |
| com_locked | SSEIT_sum | 0.2074733 | 0.0058694 |
| com_room_task | SSEIT_sum | 0.1809906 | 0.0165321 |
| dist_daily | SSEIT_sum | 0.2922581 | 0.0000869 |
| dist_study | SSEIT_sum | -0.0059561 | 0.9376463 |
| NMPQ_sum | SSEIT_sum | 0.1490683 | 0.0489679 |
| SAD_sum | SSEIT_sum | 0.1795944 | 0.0173996 |
| SAD_dep_sum | SSEIT_sum | 0.0761272 | 0.3166793 |
| SAD_ea_sum | SSEIT_sum | 0.1537579 | 0.0421996 |
| SAD_access_sum | SSEIT_sum | 0.1706472 | 0.0239544 |
| SAD_dist_sum | SSEIT_sum | 0.2126047 | 0.0047324 |
| BIS_sum | SSEIT_sum | -0.3249205 | 0.0000115 |
| NEO_O_sum | SSEIT_sum | 0.3231261 | 0.0000129 |
| NEO_C_sum | SSEIT_sum | 0.4476251 | 0.0000000 |
| NEO_E_sum | SSEIT_sum | 0.6627682 | 0.0000000 |
| NEO_A_sum | SSEIT_sum | 0.4246705 | 0.0000000 |
| NEO_N_sum | SSEIT_sum | -0.1552224 | 0.0402541 |
| RSES_sum | SSEIT_sum | -0.3723920 | 0.0000004 |
| SRS_sum | SSEIT_sum | 0.2743378 | 0.0002389 |
| age | DASS_D_sum | -0.0903063 | 0.2346328 |
| age_first_phone | DASS_D_sum | -0.0432292 | 0.5700109 |
| com_gen | DASS_D_sum | -0.1213869 | 0.1095515 |
| com_unattended | DASS_D_sum | -0.0061240 | 0.9358925 |
| com_leave_with_others | DASS_D_sum | -0.1054170 | 0.1650111 |
| com_locked | DASS_D_sum | -0.0697907 | 0.3587508 |
| com_room_task | DASS_D_sum | -0.1338476 | 0.0774122 |
| dist_daily | DASS_D_sum | 0.0832252 | 0.2735268 |
| dist_study | DASS_D_sum | 0.0755236 | 0.3205428 |
| NMPQ_sum | DASS_D_sum | 0.1466795 | 0.0527488 |
| SAD_sum | DASS_D_sum | 0.2113490 | 0.0049907 |
| SAD_dep_sum | DASS_D_sum | 0.1110309 | 0.1435217 |
| SAD_ea_sum | DASS_D_sum | 0.2452921 | 0.0010687 |
| SAD_access_sum | DASS_D_sum | 0.1719059 | 0.0229200 |
| SAD_dist_sum | DASS_D_sum | 0.1575485 | 0.0373190 |
| BIS_sum | DASS_D_sum | 0.2842977 | 0.0001373 |
| NEO_O_sum | DASS_D_sum | 0.0138485 | 0.8556656 |
| NEO_C_sum | DASS_D_sum | -0.2341519 | 0.0018158 |
| NEO_E_sum | DASS_D_sum | -0.1703873 | 0.0241729 |
| NEO_A_sum | DASS_D_sum | -0.1300026 | 0.0863942 |
| NEO_N_sum | DASS_D_sum | 0.3664640 | 0.0000006 |
| RSES_sum | DASS_D_sum | 0.4671225 | 0.0000000 |
| SRS_sum | DASS_D_sum | -0.4385616 | 0.0000000 |
| SSEIT_sum | DASS_D_sum | -0.2628663 | 0.0004406 |
| age | DASS_A_sum | -0.0024082 | 0.9747676 |
| age_first_phone | DASS_A_sum | -0.0677533 | 0.3729863 |
| com_gen | DASS_A_sum | -0.2261453 | 0.0026188 |
| com_unattended | DASS_A_sum | 0.0005977 | 0.9937363 |
| com_leave_with_others | DASS_A_sum | -0.1253224 | 0.0984327 |
| com_locked | DASS_A_sum | -0.1179529 | 0.1200366 |
| com_room_task | DASS_A_sum | -0.1405900 | 0.0634920 |
| dist_daily | DASS_A_sum | -0.0110142 | 0.8849760 |
| dist_study | DASS_A_sum | 0.2158005 | 0.0041283 |
| NMPQ_sum | DASS_A_sum | 0.1243944 | 0.1009703 |
| SAD_sum | DASS_A_sum | 0.1644917 | 0.0296102 |
| SAD_dep_sum | DASS_A_sum | 0.1032357 | 0.1739791 |
| SAD_ea_sum | DASS_A_sum | 0.1845370 | 0.0144959 |
| SAD_access_sum | DASS_A_sum | 0.1038354 | 0.1714785 |
| SAD_dist_sum | DASS_A_sum | 0.1718918 | 0.0229314 |
| BIS_sum | DASS_A_sum | 0.3639482 | 0.0000007 |
| NEO_O_sum | DASS_A_sum | 0.0097789 | 0.8978022 |
| NEO_C_sum | DASS_A_sum | -0.2408140 | 0.0013264 |
| NEO_E_sum | DASS_A_sum | -0.2013718 | 0.0075347 |
| NEO_A_sum | DASS_A_sum | -0.2485367 | 0.0009116 |
| NEO_N_sum | DASS_A_sum | 0.3338642 | 0.0000063 |
| RSES_sum | DASS_A_sum | 0.2918566 | 0.0000890 |
| SRS_sum | DASS_A_sum | -0.3468354 | 0.0000026 |
| SSEIT_sum | DASS_A_sum | -0.2543605 | 0.0006818 |
| DASS_D_sum | DASS_A_sum | 0.6037017 | 0.0000000 |
| age | DASS_S_sum | 0.1138081 | 0.1337110 |
| age_first_phone | DASS_S_sum | -0.0226106 | 0.7664626 |
| com_gen | DASS_S_sum | -0.0653934 | 0.3899025 |
| com_unattended | DASS_S_sum | 0.0666367 | 0.3809333 |
| com_leave_with_others | DASS_S_sum | -0.0325064 | 0.6693422 |
| com_locked | DASS_S_sum | -0.0428174 | 0.5736936 |
| com_room_task | DASS_S_sum | -0.0061985 | 0.9351151 |
| dist_daily | DASS_S_sum | 0.1117847 | 0.1408064 |
| dist_study | DASS_S_sum | 0.1298221 | 0.0868355 |
| NMPQ_sum | DASS_S_sum | 0.1964029 | 0.0091883 |
| SAD_sum | DASS_S_sum | 0.2717917 | 0.0002743 |
| SAD_dep_sum | DASS_S_sum | 0.1633149 | 0.0308121 |
| SAD_ea_sum | DASS_S_sum | 0.2959500 | 0.0000700 |
| SAD_access_sum | DASS_S_sum | 0.2026123 | 0.0071656 |
| SAD_dist_sum | DASS_S_sum | 0.2313847 | 0.0020636 |
| BIS_sum | DASS_S_sum | 0.3627733 | 0.0000008 |
| NEO_O_sum | DASS_S_sum | 0.1012432 | 0.1824820 |
| NEO_C_sum | DASS_S_sum | -0.2507247 | 0.0008180 |
| NEO_E_sum | DASS_S_sum | -0.1007336 | 0.1847048 |
| NEO_A_sum | DASS_S_sum | -0.1013780 | 0.1818974 |
| NEO_N_sum | DASS_S_sum | 0.4868650 | 0.0000000 |
| RSES_sum | DASS_S_sum | 0.2896199 | 0.0001013 |
| SRS_sum | DASS_S_sum | -0.3798852 | 0.0000002 |
| SSEIT_sum | DASS_S_sum | -0.1588421 | 0.0357664 |
| DASS_D_sum | DASS_S_sum | 0.7248841 | 0.0000000 |
| DASS_A_sum | DASS_S_sum | 0.7222178 | 0.0000000 |
# print tables using kable
kable(as.data.frame(format(sona_ST_corr2$r, scientific = FALSE)), caption = "SONA Study - ST - Correlation: r values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.1689 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 0.0972 | 0.1649 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0.1963 | 0.0996 | 0.3954 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 0.1943 | 0.1314 | 0.5686 | 0.6046 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | -0.1294 | -0.162 | -0.1792 | -0.1014 | -0.2108 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 0.0854 | 0.0626 | 0.3754 | 0.4603 | 0.4362 | 0.0285 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | -0.0704 | -0.1635 | 0.097 | 0.0909 | 0.0177 | 0.2121 | 0.0328 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | -0.0236 | -0.0402 | -0.1209 | -0.191 | -0.1691 | -0.0847 | -0.2249 | 0.341 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 0.0473 | -0.0796 | -0.0956 | -0.2069 | -0.188 | 0.1846 | -0.1976 | 0.2578 | 0.2776 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | -0.0577 | -0.14 | -0.0835 | -0.1351 | -0.1832 | 0.2493 | -0.1761 | 0.3406 | 0.2577 | 0.7571 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | -0.0316 | -0.0891 | -0.1373 | -0.1706 | -0.2378 | 0.1913 | -0.2764 | 0.2473 | 0.2105 | 0.6707 | 0.8426 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | -0.0872 | -0.098 | -0.0653 | -0.1541 | -0.1033 | 0.1957 | -0.0718 | 0.2348 | 0.202 | 0.585 | 0.8176 | 0.5009 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 0.03 | -0.166 | -0.0889 | -0.0651 | -0.1574 | 0.2254 | -0.1248 | 0.3009 | 0.1841 | 0.6642 | 0.8124 | 0.6149 | 0.5492 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | -0.111 | -0.0677 | 0.0496 | 0.0079 | -0.0253 | 0.1932 | -0.0534 | 0.3619 | 0.2123 | 0.3571 | 0.6419 | 0.4023 | 0.5372 | 0.3453 | 1 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0.0619 | -0.0862 | -0.0022 | -0.0122 | -0.0286 | -0.1394 | -0.0044 | 0.0558 | 0.2369 | 0.0662 | 0.1333 | 0.1659 | 0.0775 | 0.0399 | 0.1206 | 1 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.073 | -0.0219 | 0.0384 | 0.1256 | 0.0658 | 0.1044 | 0.1159 | 0.2042 | -0.0438 | -0.0854 | -0.0519 | -0.1587 | -0.033 | 0 | 0.1141 | 0.0068 | 1 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.0075 | 0.0467 | -0.0529 | 0.0357 | -0.0051 | 0.1805 | 0.0363 | -0.027 | -0.1276 | -0.0504 | -0.131 | -0.2111 | -0.0836 | -0.0176 | -0.0317 | -0.6903 | 0.0824 | 1 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 0.0507 | -0.0601 | 0.1739 | 0.1013 | 0.1266 | 0.2079 | 0.1762 | 0.2448 | 0.0245 | 0.0838 | 0.071 | -0.0383 | 0.1172 | 0.0691 | 0.1485 | -0.1619 | 0.3888 | 0.362 | 1 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.0153 | 0.0334 | 0.2735 | 0.195 | 0.2502 | 0.0885 | 0.2 | 0.156 | -0.1535 | -0.0143 | -0.1157 | -0.129 | -0.1474 | -0.0173 | -0.0439 | -0.2729 | 0.234 | 0.3789 | 0.2879 | 1 | - | - | - | - | - | - | - |
| NEO_N_sum | 0.0584 | 0.018 | -0.0274 | 0.0347 | 0.0189 | -0.0457 | -0.0258 | 0.1885 | 0.1506 | 0.2009 | 0.2196 | 0.2156 | 0.1382 | 0.1802 | 0.1654 | 0.3828 | -0.0029 | -0.3445 | -0.2358 | -0.0284 | 1 | - | - | - | - | - | - |
| RSES_sum | -0.0861 | -0.0193 | -0.0801 | -0.0343 | 0.0428 | -0.137 | -0.049 | -0.0539 | -0.0428 | 0.0194 | 0.0132 | 0.0627 | -0.0109 | 0.0394 | -0.0939 | 0.0796 | -0.1264 | -0.1746 | -0.3751 | 0.0132 | 0.391 | 1 | - | - | - | - | - |
| SRS_sum | 0.0075 | 0.149 | 0.2207 | 0.1074 | 0.1798 | 0.0624 | 0.2341 | -0.0928 | -0.2088 | -0.1374 | -0.1045 | -0.1527 | 0.0031 | -0.0784 | -0.1022 | -0.5099 | -0.0028 | 0.4028 | 0.2637 | 0.1434 | -0.3625 | -0.2578 | 1 | - | - | - | - |
| SSEIT_sum | 0.0957 | -0.0067 | 0.1988 | 0.2297 | 0.1496 | 0.2075 | 0.181 | 0.2923 | -0.006 | 0.1491 | 0.1796 | 0.0761 | 0.1538 | 0.1706 | 0.2126 | -0.3249 | 0.3231 | 0.4476 | 0.6628 | 0.4247 | -0.1552 | -0.3724 | 0.2743 | 1 | - | - | - |
| DASS_D_sum | -0.0903 | -0.0432 | -0.1214 | -0.0061 | -0.1054 | -0.0698 | -0.1338 | 0.0832 | 0.0755 | 0.1467 | 0.2113 | 0.111 | 0.2453 | 0.1719 | 0.1575 | 0.2843 | 0.0138 | -0.2342 | -0.1704 | -0.13 | 0.3665 | 0.4671 | -0.4386 | -0.2629 | 1 | - | - |
| DASS_A_sum | -0.0024 | -0.0678 | -0.2261 | 6e-04 | -0.1253 | -0.118 | -0.1406 | -0.011 | 0.2158 | 0.1244 | 0.1645 | 0.1032 | 0.1845 | 0.1038 | 0.1719 | 0.3639 | 0.0098 | -0.2408 | -0.2014 | -0.2485 | 0.3339 | 0.2919 | -0.3468 | -0.2544 | 0.6037 | 1 | - |
| DASS_S_sum | 0.1138 | -0.0226 | -0.0654 | 0.0666 | -0.0325 | -0.0428 | -0.0062 | 0.1118 | 0.1298 | 0.1964 | 0.2718 | 0.1633 | 0.2959 | 0.2026 | 0.2314 | 0.3628 | 0.1012 | -0.2507 | -0.1007 | -0.1014 | 0.4869 | 0.2896 | -0.3799 | -0.1588 | 0.7249 | 0.7222 | 1 |
kable(as.data.frame(format(sona_ST_corr2$P, scientific = FALSE)), caption = "SONA Stud - STy - Correlation: p values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.0255 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 0.2005 | 0.0292 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0.0092 | 0.1896 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 0.01 | 0.0831 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 0.0878 | 0.0322 | 0.0177 | 0.1817 | 0.0051 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 0.2614 | 0.4109 | 0 | 0 | 0 | 0.7077 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 0.3546 | 0.0307 | 0.2017 | 0.2318 | 0.8166 | 0.0048 | 0.667 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 0.7563 | 0.5973 | 0.1111 | 0.0113 | 0.0253 | 0.2653 | 0.0028 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 0.5343 | 0.2952 | 0.2083 | 0.006 | 0.0127 | 0.0145 | 0.0088 | 6e-04 | 2e-04 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 0.4484 | 0.0646 | 0.2721 | 0.0747 | 0.0152 | 9e-04 | 0.0197 | 0 | 6e-04 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 0.6785 | 0.241 | 0.07 | 0.024 | 0.0015 | 0.0112 | 2e-04 | 0.001 | 0.0052 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 0.2514 | 0.1971 | 0.3904 | 0.0417 | 0.1738 | 0.0095 | 0.3449 | 0.0018 | 0.0073 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 0.6939 | 0.0281 | 0.2422 | 0.3918 | 0.0375 | 0.0027 | 0.0999 | 1e-04 | 0.0147 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 0.1435 | 0.3735 | 0.5145 | 0.9174 | 0.7401 | 0.0104 | 0.4825 | 0 | 0.0048 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0.416 | 0.2564 | 0.9764 | 0.8726 | 0.7073 | 0.0659 | 0.9538 | 0.463 | 0.0016 | 0.3843 | 0.0785 | 0.0282 | 0.3078 | 0.5997 | 0.1118 | NA | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.3372 | 0.7731 | 0.6142 | 0.0977 | 0.3866 | 0.169 | 0.1267 | 0.0067 | 0.5652 | 0.2611 | 0.4954 | 0.0359 | 0.6643 | 0.9996 | 0.1327 | 0.9283 | NA | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.9211 | 0.5391 | 0.4871 | 0.6393 | 0.9465 | 0.0168 | 0.633 | 0.7227 | 0.0923 | 0.5075 | 0.084 | 0.005 | 0.2713 | 0.817 | 0.6766 | 0 | 0.2785 | NA | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 0.5051 | 0.4294 | 0.0213 | 0.1823 | 0.0949 | 0.0058 | 0.0197 | 0.0011 | 0.7475 | 0.27 | 0.3503 | 0.6144 | 0.1224 | 0.3637 | 0.0499 | 0.0323 | 0 | 0 | NA | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.8407 | 0.6606 | 2e-04 | 0.0097 | 8e-04 | 0.2443 | 0.008 | 0.0392 | 0.0425 | 0.8514 | 0.1275 | 0.0889 | 0.0515 | 0.8207 | 0.5637 | 3e-04 | 0.0018 | 0 | 1e-04 | NA | - | - | - | - | - | - | - |
| NEO_N_sum | 0.4424 | 0.8134 | 0.7185 | 0.6487 | 0.8043 | 0.5482 | 0.7344 | 0.0125 | 0.0466 | 0.0077 | 0.0035 | 0.0042 | 0.0681 | 0.017 | 0.0287 | 0 | 0.9691 | 0 | 0.0017 | 0.7093 | NA | - | - | - | - | - | - |
| RSES_sum | 0.2574 | 0.7995 | 0.2922 | 0.6527 | 0.5738 | 0.0706 | 0.5193 | 0.4785 | 0.5743 | 0.7993 | 0.8623 | 0.4096 | 0.8858 | 0.6047 | 0.2166 | 0.2949 | 0.0954 | 0.0208 | 0 | 0.8619 | 0 | NA | - | - | - | - | - |
| SRS_sum | 0.921 | 0.049 | 0.0033 | 0.1571 | 0.0172 | 0.4117 | 0.0018 | 0.2221 | 0.0056 | 0.0698 | 0.1689 | 0.0437 | 0.9678 | 0.3026 | 0.1782 | 0 | 0.9704 | 0 | 4e-04 | 0.0584 | 0 | 6e-04 | NA | - | - | - | - |
| SSEIT_sum | 0.2078 | 0.9303 | 0.0083 | 0.0022 | 0.0482 | 0.0059 | 0.0165 | 1e-04 | 0.9376 | 0.049 | 0.0174 | 0.3167 | 0.0422 | 0.024 | 0.0047 | 0 | 0 | 0 | 0 | 0 | 0.0403 | 0 | 2e-04 | NA | - | - | - |
| DASS_D_sum | 0.2346 | 0.57 | 0.1096 | 0.9359 | 0.165 | 0.3588 | 0.0774 | 0.2735 | 0.3205 | 0.0527 | 0.005 | 0.1435 | 0.0011 | 0.0229 | 0.0373 | 1e-04 | 0.8557 | 0.0018 | 0.0242 | 0.0864 | 0 | 0 | 0 | 4e-04 | NA | - | - |
| DASS_A_sum | 0.9748 | 0.373 | 0.0026 | 0.9937 | 0.0984 | 0.12 | 0.0635 | 0.885 | 0.0041 | 0.101 | 0.0296 | 0.174 | 0.0145 | 0.1715 | 0.0229 | 0 | 0.8978 | 0.0013 | 0.0075 | 9e-04 | 0 | 1e-04 | 0 | 7e-04 | 0 | NA | - |
| DASS_S_sum | 0.1337 | 0.7665 | 0.3899 | 0.3809 | 0.6693 | 0.5737 | 0.9351 | 0.1408 | 0.0868 | 0.0092 | 3e-04 | 0.0308 | 1e-04 | 0.0072 | 0.0021 | 0 | 0.1825 | 8e-04 | 0.1847 | 0.1819 | 0 | 1e-04 | 0 | 0.0358 | 0 | 0 | NA |
kable(as.data.frame(format(sona_ST_corr2$n, scientific = FALSE)), caption = "SONA Study - ST - Correlation: n values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - | - |
| NEO_N_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - | - |
| RSES_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - | - |
| SRS_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - | - |
| SSEIT_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - | - |
| DASS_D_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - | - |
| DASS_A_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | - |
| DASS_S_sum | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 | 175 |
# create new matrix for r & p vals with different col names
# get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
sona_ST_corr_r <- sona_ST_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
sona_ST_corr_p <- sona_ST_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")
# all vars included
corr_plot_sona_ST_all <- {
corrplot(sona_ST_corr$r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_ST_corr$P, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .5, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# with all SAD subscales
corr_plot_sona_ST_ques_all <- {
corrplot(sona_ST_corr_r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_ST_corr_p, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# final -- with SAD overall only
corr_plot_sona_ST_ques_final <- {
corrplot(sona_ST_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = sona_ST_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_all(oct28)", plot = replayPlot(corr_plot_sona_ST_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_ques_all(oct28)", plot = replayPlot(corr_plot_sona_ST_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_ques_final(oct28)", plot = replayPlot(corr_plot_sona_ST_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
# export csv files of R values & p values
write.csv(sona_ST_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_ST_corr_rvals(oct28).csv", row.names = T)
write.csv(sona_ST_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_ST_corr_pvals(oct28).csv", row.names = T)Study 2: Mturk
mturk_ST_corr <-
Mturk_data %>%
filter(ST == "had ST") %>%
# select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study,
NMPQ_sum,
SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>%
as.matrix() %>%
Hmisc::rcorr(type = c("pearson"))
# create new pilot_corr to shown only lower triangle...
mturk_ST_corr2 <- mturk_ST_corr
# round to 4 decimals...
mturk_ST_corr2$r <- round(mturk_ST_corr2$r, 4)
mturk_ST_corr2$P <- round(mturk_ST_corr2$P, 4)
mturk_ST_corr2$n <- round(mturk_ST_corr2$n, 4)
# remove upper triangle form r, p, and n
mturk_ST_corr2$r[upper.tri(mturk_ST_corr2$r)] <- "-"
mturk_ST_corr2$P[upper.tri(mturk_ST_corr2$P)] <- "-"
mturk_ST_corr2$n[upper.tri(mturk_ST_corr2$n)] <- "-"
# show corr table with flattenCorr
kable(flattenCorrMatrix(mturk_ST_corr$r, mturk_ST_corr$P), caption = "MTURK Study - ST - Correlation: Flat") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| row | column | cor | p |
|---|---|---|---|
| age | age_first_phone | 0.1307575 | 0.0972141 |
| age | com_gen | -0.2172059 | 0.0054965 |
| age_first_phone | com_gen | -0.0615467 | 0.4365496 |
| age | com_unattended | -0.1746741 | 0.0262062 |
| age_first_phone | com_unattended | -0.0037064 | 0.9626655 |
| com_gen | com_unattended | 0.5806989 | 0.0000000 |
| age | com_leave_with_others | -0.2690082 | 0.0005373 |
| age_first_phone | com_leave_with_others | -0.0760462 | 0.3361460 |
| com_gen | com_leave_with_others | 0.5743246 | 0.0000000 |
| com_unattended | com_leave_with_others | 0.7165689 | 0.0000000 |
| age | com_locked | -0.2596600 | 0.0008476 |
| age_first_phone | com_locked | 0.0088970 | 0.9105334 |
| com_gen | com_locked | 0.0765760 | 0.3327802 |
| com_unattended | com_locked | -0.0262810 | 0.7399125 |
| com_leave_with_others | com_locked | -0.0317249 | 0.6885923 |
| age | com_room_task | -0.0506491 | 0.5221228 |
| age_first_phone | com_room_task | -0.1363578 | 0.0835940 |
| com_gen | com_room_task | 0.4943782 | 0.0000000 |
| com_unattended | com_room_task | 0.4967794 | 0.0000000 |
| com_leave_with_others | com_room_task | 0.4732389 | 0.0000000 |
| com_locked | com_room_task | -0.1360283 | 0.0843507 |
| age | dist_daily | -0.3352648 | 0.0000129 |
| age_first_phone | dist_daily | -0.0210228 | 0.7905980 |
| com_gen | dist_daily | 0.3097682 | 0.0000604 |
| com_unattended | dist_daily | 0.3217882 | 0.0000297 |
| com_leave_with_others | dist_daily | 0.3803759 | 0.0000006 |
| com_locked | dist_daily | 0.2384003 | 0.0022505 |
| com_room_task | dist_daily | 0.2082266 | 0.0078378 |
| age | dist_study | -0.2951159 | 0.0001376 |
| age_first_phone | dist_study | 0.0444364 | 0.5744701 |
| com_gen | dist_study | 0.4460343 | 0.0000000 |
| com_unattended | dist_study | 0.6572034 | 0.0000000 |
| com_leave_with_others | dist_study | 0.6679607 | 0.0000000 |
| com_locked | dist_study | 0.1274681 | 0.1059987 |
| com_room_task | dist_study | 0.3564528 | 0.0000032 |
| dist_daily | dist_study | 0.5043825 | 0.0000000 |
| age | NMPQ_sum | -0.2780637 | 0.0003401 |
| age_first_phone | NMPQ_sum | -0.0750927 | 0.3422572 |
| com_gen | NMPQ_sum | 0.3236050 | 0.0000266 |
| com_unattended | NMPQ_sum | 0.4785813 | 0.0000000 |
| com_leave_with_others | NMPQ_sum | 0.5124126 | 0.0000000 |
| com_locked | NMPQ_sum | 0.3050552 | 0.0000791 |
| com_room_task | NMPQ_sum | 0.1759477 | 0.0251163 |
| dist_daily | NMPQ_sum | 0.5916067 | 0.0000000 |
| dist_study | NMPQ_sum | 0.6036131 | 0.0000000 |
| age | SAD_sum | -0.3376564 | 0.0000111 |
| age_first_phone | SAD_sum | -0.0496535 | 0.5303453 |
| com_gen | SAD_sum | 0.3566522 | 0.0000032 |
| com_unattended | SAD_sum | 0.4055109 | 0.0000001 |
| com_leave_with_others | SAD_sum | 0.4928984 | 0.0000000 |
| com_locked | SAD_sum | 0.2655622 | 0.0006368 |
| com_room_task | SAD_sum | 0.1806220 | 0.0214430 |
| dist_daily | SAD_sum | 0.5193548 | 0.0000000 |
| dist_study | SAD_sum | 0.5884648 | 0.0000000 |
| NMPQ_sum | SAD_sum | 0.8156093 | 0.0000000 |
| age | SAD_dep_sum | -0.2918129 | 0.0001647 |
| age_first_phone | SAD_dep_sum | -0.0332230 | 0.6747033 |
| com_gen | SAD_dep_sum | 0.2731173 | 0.0004375 |
| com_unattended | SAD_dep_sum | 0.3150340 | 0.0000444 |
| com_leave_with_others | SAD_dep_sum | 0.3542299 | 0.0000038 |
| com_locked | SAD_dep_sum | 0.2798676 | 0.0003099 |
| com_room_task | SAD_dep_sum | 0.0960376 | 0.2240970 |
| dist_daily | SAD_dep_sum | 0.4583816 | 0.0000000 |
| dist_study | SAD_dep_sum | 0.4549164 | 0.0000000 |
| NMPQ_sum | SAD_dep_sum | 0.7545171 | 0.0000000 |
| SAD_sum | SAD_dep_sum | 0.8841225 | 0.0000000 |
| age | SAD_ea_sum | -0.3335864 | 0.0000144 |
| age_first_phone | SAD_ea_sum | -0.0144421 | 0.8552653 |
| com_gen | SAD_ea_sum | 0.4087319 | 0.0000001 |
| com_unattended | SAD_ea_sum | 0.4449379 | 0.0000000 |
| com_leave_with_others | SAD_ea_sum | 0.5077330 | 0.0000000 |
| com_locked | SAD_ea_sum | 0.1794278 | 0.0223344 |
| com_room_task | SAD_ea_sum | 0.2333126 | 0.0028087 |
| dist_daily | SAD_ea_sum | 0.4439694 | 0.0000000 |
| dist_study | SAD_ea_sum | 0.5957411 | 0.0000000 |
| NMPQ_sum | SAD_ea_sum | 0.6958333 | 0.0000000 |
| SAD_sum | SAD_ea_sum | 0.9095171 | 0.0000000 |
| SAD_dep_sum | SAD_ea_sum | 0.7101516 | 0.0000000 |
| age | SAD_access_sum | -0.2145225 | 0.0061202 |
| age_first_phone | SAD_access_sum | -0.1074462 | 0.1735390 |
| com_gen | SAD_access_sum | 0.1768854 | 0.0243390 |
| com_unattended | SAD_access_sum | 0.2615516 | 0.0007739 |
| com_leave_with_others | SAD_access_sum | 0.3370047 | 0.0000116 |
| com_locked | SAD_access_sum | 0.2986982 | 0.0001130 |
| com_room_task | SAD_access_sum | 0.0133023 | 0.8665780 |
| dist_daily | SAD_access_sum | 0.4092017 | 0.0000001 |
| dist_study | SAD_access_sum | 0.4012338 | 0.0000001 |
| NMPQ_sum | SAD_access_sum | 0.7308838 | 0.0000000 |
| SAD_sum | SAD_access_sum | 0.8233417 | 0.0000000 |
| SAD_dep_sum | SAD_access_sum | 0.7269412 | 0.0000000 |
| SAD_ea_sum | SAD_access_sum | 0.6086329 | 0.0000000 |
| age | SAD_dist_sum | -0.2933899 | 0.0001512 |
| age_first_phone | SAD_dist_sum | -0.0199584 | 0.8009742 |
| com_gen | SAD_dist_sum | 0.4215664 | 0.0000000 |
| com_unattended | SAD_dist_sum | 0.3817526 | 0.0000005 |
| com_leave_with_others | SAD_dist_sum | 0.5326892 | 0.0000000 |
| com_locked | SAD_dist_sum | 0.0824283 | 0.2970442 |
| com_room_task | SAD_dist_sum | 0.3293583 | 0.0000187 |
| dist_daily | SAD_dist_sum | 0.4034278 | 0.0000001 |
| dist_study | SAD_dist_sum | 0.5973895 | 0.0000000 |
| NMPQ_sum | SAD_dist_sum | 0.5122425 | 0.0000000 |
| SAD_sum | SAD_dist_sum | 0.7467922 | 0.0000000 |
| SAD_dep_sum | SAD_dist_sum | 0.4984602 | 0.0000000 |
| SAD_ea_sum | SAD_dist_sum | 0.7504450 | 0.0000000 |
| SAD_access_sum | SAD_dist_sum | 0.4254271 | 0.0000000 |
| age | BIS_sum | -0.1764504 | 0.0246970 |
| age_first_phone | BIS_sum | 0.0035239 | 0.9645022 |
| com_gen | BIS_sum | 0.1591482 | 0.0430885 |
| com_unattended | BIS_sum | 0.4429188 | 0.0000000 |
| com_leave_with_others | BIS_sum | 0.5137268 | 0.0000000 |
| com_locked | BIS_sum | -0.1469196 | 0.0620921 |
| com_room_task | BIS_sum | 0.2698141 | 0.0005162 |
| dist_daily | BIS_sum | 0.2833127 | 0.0002590 |
| dist_study | BIS_sum | 0.5455183 | 0.0000000 |
| NMPQ_sum | BIS_sum | 0.3710240 | 0.0000012 |
| SAD_sum | BIS_sum | 0.3613468 | 0.0000023 |
| SAD_dep_sum | BIS_sum | 0.2454956 | 0.0016395 |
| SAD_ea_sum | BIS_sum | 0.3108449 | 0.0000567 |
| SAD_access_sum | BIS_sum | 0.2483695 | 0.0014384 |
| SAD_dist_sum | BIS_sum | 0.5134919 | 0.0000000 |
| age | NEO_O_sum | 0.0747698 | 0.3443428 |
| age_first_phone | NEO_O_sum | 0.0378789 | 0.6322527 |
| com_gen | NEO_O_sum | -0.3324788 | 0.0000154 |
| com_unattended | NEO_O_sum | -0.5275106 | 0.0000000 |
| com_leave_with_others | NEO_O_sum | -0.5116805 | 0.0000000 |
| com_locked | NEO_O_sum | 0.1102806 | 0.1624019 |
| com_room_task | NEO_O_sum | -0.3084570 | 0.0000651 |
| dist_daily | NEO_O_sum | -0.1213735 | 0.1239109 |
| dist_study | NEO_O_sum | -0.5480151 | 0.0000000 |
| NMPQ_sum | NEO_O_sum | -0.2465749 | 0.0015612 |
| SAD_sum | NEO_O_sum | -0.3051155 | 0.0000788 |
| SAD_dep_sum | NEO_O_sum | -0.2006313 | 0.0104694 |
| SAD_ea_sum | NEO_O_sum | -0.3076001 | 0.0000684 |
| SAD_access_sum | NEO_O_sum | -0.1998109 | 0.0107956 |
| SAD_dist_sum | NEO_O_sum | -0.4405486 | 0.0000000 |
| BIS_sum | NEO_O_sum | -0.4669551 | 0.0000000 |
| age | NEO_C_sum | 0.2630137 | 0.0007211 |
| age_first_phone | NEO_C_sum | -0.0452915 | 0.5671209 |
| com_gen | NEO_C_sum | -0.1900197 | 0.0154381 |
| com_unattended | NEO_C_sum | -0.2340850 | 0.0027166 |
| com_leave_with_others | NEO_C_sum | -0.3267186 | 0.0000220 |
| com_locked | NEO_C_sum | 0.1834118 | 0.0194794 |
| com_room_task | NEO_C_sum | -0.1662989 | 0.0344300 |
| dist_daily | NEO_C_sum | -0.2340654 | 0.0027189 |
| dist_study | NEO_C_sum | -0.4232772 | 0.0000000 |
| NMPQ_sum | NEO_C_sum | -0.1234188 | 0.1176559 |
| SAD_sum | NEO_C_sum | -0.2025699 | 0.0097326 |
| SAD_dep_sum | NEO_C_sum | -0.1126247 | 0.1536022 |
| SAD_ea_sum | NEO_C_sum | -0.2315483 | 0.0030297 |
| SAD_access_sum | NEO_C_sum | -0.0258113 | 0.7443986 |
| SAD_dist_sum | NEO_C_sum | -0.4145875 | 0.0000000 |
| BIS_sum | NEO_C_sum | -0.5932801 | 0.0000000 |
| NEO_O_sum | NEO_C_sum | 0.3167778 | 0.0000401 |
| age | NEO_E_sum | -0.2317987 | 0.0029974 |
| age_first_phone | NEO_E_sum | -0.0690829 | 0.3823813 |
| com_gen | NEO_E_sum | 0.4020972 | 0.0000001 |
| com_unattended | NEO_E_sum | 0.2549491 | 0.0010599 |
| com_leave_with_others | NEO_E_sum | 0.1989484 | 0.0111483 |
| com_locked | NEO_E_sum | 0.1833275 | 0.0195363 |
| com_room_task | NEO_E_sum | 0.2076866 | 0.0080033 |
| dist_daily | NEO_E_sum | 0.0691196 | 0.3821278 |
| dist_study | NEO_E_sum | 0.2932689 | 0.0001522 |
| NMPQ_sum | NEO_E_sum | 0.3010749 | 0.0000990 |
| SAD_sum | NEO_E_sum | 0.3171920 | 0.0000391 |
| SAD_dep_sum | NEO_E_sum | 0.2419712 | 0.0019210 |
| SAD_ea_sum | NEO_E_sum | 0.3744773 | 0.0000009 |
| SAD_access_sum | NEO_E_sum | 0.2200333 | 0.0049014 |
| SAD_dist_sum | NEO_E_sum | 0.2521057 | 0.0012106 |
| BIS_sum | NEO_E_sum | 0.0481384 | 0.5429814 |
| NEO_O_sum | NEO_E_sum | -0.1523628 | 0.0529215 |
| NEO_C_sum | NEO_E_sum | 0.1661095 | 0.0346388 |
| age | NEO_A_sum | 0.3719569 | 0.0000011 |
| age_first_phone | NEO_A_sum | 0.0455250 | 0.5651219 |
| com_gen | NEO_A_sum | -0.3412555 | 0.0000088 |
| com_unattended | NEO_A_sum | -0.4006894 | 0.0000001 |
| com_leave_with_others | NEO_A_sum | -0.5129516 | 0.0000000 |
| com_locked | NEO_A_sum | 0.0655297 | 0.4073937 |
| com_room_task | NEO_A_sum | -0.1949661 | 0.0129114 |
| dist_daily | NEO_A_sum | -0.2446834 | 0.0017008 |
| dist_study | NEO_A_sum | -0.5798737 | 0.0000000 |
| NMPQ_sum | NEO_A_sum | -0.2195097 | 0.0050070 |
| SAD_sum | NEO_A_sum | -0.3225523 | 0.0000284 |
| SAD_dep_sum | NEO_A_sum | -0.2204054 | 0.0048275 |
| SAD_ea_sum | NEO_A_sum | -0.3744520 | 0.0000009 |
| SAD_access_sum | NEO_A_sum | -0.1239874 | 0.1159613 |
| SAD_dist_sum | NEO_A_sum | -0.4904340 | 0.0000000 |
| BIS_sum | NEO_A_sum | -0.5393073 | 0.0000000 |
| NEO_O_sum | NEO_A_sum | 0.5592628 | 0.0000000 |
| NEO_C_sum | NEO_A_sum | 0.6444951 | 0.0000000 |
| NEO_E_sum | NEO_A_sum | -0.1182331 | 0.1340112 |
| age | NEO_N_sum | -0.1193367 | 0.1303924 |
| age_first_phone | NEO_N_sum | 0.0605208 | 0.4442469 |
| com_gen | NEO_N_sum | 0.1929403 | 0.0138987 |
| com_unattended | NEO_N_sum | 0.3731897 | 0.0000010 |
| com_leave_with_others | NEO_N_sum | 0.4563371 | 0.0000000 |
| com_locked | NEO_N_sum | 0.0620059 | 0.4331287 |
| com_room_task | NEO_N_sum | 0.1639464 | 0.0370997 |
| dist_daily | NEO_N_sum | 0.4819029 | 0.0000000 |
| dist_study | NEO_N_sum | 0.5314948 | 0.0000000 |
| NMPQ_sum | NEO_N_sum | 0.4527195 | 0.0000000 |
| SAD_sum | NEO_N_sum | 0.5535497 | 0.0000000 |
| SAD_dep_sum | NEO_N_sum | 0.4725895 | 0.0000000 |
| SAD_ea_sum | NEO_N_sum | 0.4650034 | 0.0000000 |
| SAD_access_sum | NEO_N_sum | 0.3819149 | 0.0000005 |
| SAD_dist_sum | NEO_N_sum | 0.6002000 | 0.0000000 |
| BIS_sum | NEO_N_sum | 0.5827757 | 0.0000000 |
| NEO_O_sum | NEO_N_sum | -0.4415068 | 0.0000000 |
| NEO_C_sum | NEO_N_sum | -0.4714179 | 0.0000000 |
| NEO_E_sum | NEO_N_sum | -0.1358063 | 0.0848633 |
| NEO_A_sum | NEO_N_sum | -0.4645498 | 0.0000000 |
| age | RSES_sum | -0.0790089 | 0.3176034 |
| age_first_phone | RSES_sum | -0.0012781 | 0.9871210 |
| com_gen | RSES_sum | 0.0658748 | 0.4049227 |
| com_unattended | RSES_sum | 0.0365164 | 0.6445598 |
| com_leave_with_others | RSES_sum | 0.0899953 | 0.2547466 |
| com_locked | RSES_sum | -0.0404006 | 0.6097415 |
| com_room_task | RSES_sum | 0.1143942 | 0.1472013 |
| dist_daily | RSES_sum | 0.0279638 | 0.7239133 |
| dist_study | RSES_sum | 0.0177530 | 0.8225813 |
| NMPQ_sum | RSES_sum | -0.0024060 | 0.9757589 |
| SAD_sum | RSES_sum | 0.1398631 | 0.0758785 |
| SAD_dep_sum | RSES_sum | 0.0625850 | 0.4288360 |
| SAD_ea_sum | RSES_sum | 0.1771850 | 0.0240950 |
| SAD_access_sum | RSES_sum | 0.0145956 | 0.8537448 |
| SAD_dist_sum | RSES_sum | 0.2589430 | 0.0008772 |
| BIS_sum | RSES_sum | 0.1069911 | 0.1753785 |
| NEO_O_sum | RSES_sum | -0.1823790 | 0.0201874 |
| NEO_C_sum | RSES_sum | -0.1293733 | 0.1008379 |
| NEO_E_sum | RSES_sum | -0.1026969 | 0.1934509 |
| NEO_A_sum | RSES_sum | -0.1811195 | 0.0210808 |
| NEO_N_sum | RSES_sum | 0.1621063 | 0.0393077 |
| age | SRS_sum | 0.1370199 | 0.0820904 |
| age_first_phone | SRS_sum | 0.0370756 | 0.6394973 |
| com_gen | SRS_sum | -0.0619445 | 0.4335849 |
| com_unattended | SRS_sum | -0.0669465 | 0.3973065 |
| com_leave_with_others | SRS_sum | -0.2272195 | 0.0036402 |
| com_locked | SRS_sum | 0.1132089 | 0.1514663 |
| com_room_task | SRS_sum | -0.0687337 | 0.3847972 |
| dist_daily | SRS_sum | -0.2163067 | 0.0056988 |
| dist_study | SRS_sum | -0.2969710 | 0.0001243 |
| NMPQ_sum | SRS_sum | -0.0459309 | 0.5616554 |
| SAD_sum | SRS_sum | -0.2363605 | 0.0024609 |
| SAD_dep_sum | SRS_sum | -0.1314587 | 0.0954181 |
| SAD_ea_sum | SRS_sum | -0.2563005 | 0.0009945 |
| SAD_access_sum | SRS_sum | -0.0505392 | 0.5230270 |
| SAD_dist_sum | SRS_sum | -0.4891619 | 0.0000000 |
| BIS_sum | SRS_sum | -0.4870836 | 0.0000000 |
| NEO_O_sum | SRS_sum | 0.2368151 | 0.0024125 |
| NEO_C_sum | SRS_sum | 0.6447153 | 0.0000000 |
| NEO_E_sum | SRS_sum | 0.1957821 | 0.0125314 |
| NEO_A_sum | SRS_sum | 0.4994924 | 0.0000000 |
| NEO_N_sum | SRS_sum | -0.5552645 | 0.0000000 |
| RSES_sum | SRS_sum | -0.2088359 | 0.0076546 |
| age | SSEIT_sum | -0.0523189 | 0.5084790 |
| age_first_phone | SSEIT_sum | -0.0832927 | 0.2919899 |
| com_gen | SSEIT_sum | 0.1657945 | 0.0349883 |
| com_unattended | SSEIT_sum | 0.0639303 | 0.4189602 |
| com_leave_with_others | SSEIT_sum | -0.0013469 | 0.9864282 |
| com_locked | SSEIT_sum | 0.3131580 | 0.0000496 |
| com_room_task | SSEIT_sum | 0.1042533 | 0.1867504 |
| dist_daily | SSEIT_sum | 0.0707885 | 0.3707138 |
| dist_study | SSEIT_sum | 0.0493083 | 0.5332113 |
| NMPQ_sum | SSEIT_sum | 0.3573917 | 0.0000030 |
| SAD_sum | SSEIT_sum | 0.2910317 | 0.0001718 |
| SAD_dep_sum | SSEIT_sum | 0.2653529 | 0.0006434 |
| SAD_ea_sum | SSEIT_sum | 0.2816103 | 0.0002831 |
| SAD_access_sum | SSEIT_sum | 0.2811124 | 0.0002905 |
| SAD_dist_sum | SSEIT_sum | 0.0368575 | 0.6414700 |
| BIS_sum | SSEIT_sum | -0.2143255 | 0.0061684 |
| NEO_O_sum | SSEIT_sum | 0.1717714 | 0.0288423 |
| NEO_C_sum | SSEIT_sum | 0.4717414 | 0.0000000 |
| NEO_E_sum | SSEIT_sum | 0.4313103 | 0.0000000 |
| NEO_A_sum | SSEIT_sum | 0.3909145 | 0.0000003 |
| NEO_N_sum | SSEIT_sum | -0.2282346 | 0.0034878 |
| RSES_sum | SSEIT_sum | -0.0157711 | 0.8421133 |
| SRS_sum | SSEIT_sum | 0.5424172 | 0.0000000 |
| age | DASS_D_sum | -0.3420203 | 0.0000084 |
| age_first_phone | DASS_D_sum | -0.0696060 | 0.3787793 |
| com_gen | DASS_D_sum | 0.4466733 | 0.0000000 |
| com_unattended | DASS_D_sum | 0.5342379 | 0.0000000 |
| com_leave_with_others | DASS_D_sum | 0.6287512 | 0.0000000 |
| com_locked | DASS_D_sum | 0.1217265 | 0.1228133 |
| com_room_task | DASS_D_sum | 0.3322710 | 0.0000156 |
| dist_daily | DASS_D_sum | 0.4899719 | 0.0000000 |
| dist_study | DASS_D_sum | 0.6686407 | 0.0000000 |
| NMPQ_sum | DASS_D_sum | 0.5463872 | 0.0000000 |
| SAD_sum | DASS_D_sum | 0.6139742 | 0.0000000 |
| SAD_dep_sum | DASS_D_sum | 0.4893925 | 0.0000000 |
| SAD_ea_sum | DASS_D_sum | 0.5714560 | 0.0000000 |
| SAD_access_sum | DASS_D_sum | 0.4159469 | 0.0000000 |
| SAD_dist_sum | DASS_D_sum | 0.6671587 | 0.0000000 |
| BIS_sum | DASS_D_sum | 0.6195274 | 0.0000000 |
| NEO_O_sum | DASS_D_sum | -0.5217124 | 0.0000000 |
| NEO_C_sum | DASS_D_sum | -0.5821828 | 0.0000000 |
| NEO_E_sum | DASS_D_sum | 0.1265226 | 0.1086362 |
| NEO_A_sum | DASS_D_sum | -0.6990062 | 0.0000000 |
| NEO_N_sum | DASS_D_sum | 0.6813397 | 0.0000000 |
| RSES_sum | DASS_D_sum | 0.2618009 | 0.0007647 |
| SRS_sum | DASS_D_sum | -0.4375019 | 0.0000000 |
| SSEIT_sum | DASS_D_sum | -0.0890170 | 0.2599685 |
| age | DASS_A_sum | -0.3764004 | 0.0000008 |
| age_first_phone | DASS_A_sum | -0.0515841 | 0.5144601 |
| com_gen | DASS_A_sum | 0.5076892 | 0.0000000 |
| com_unattended | DASS_A_sum | 0.5732269 | 0.0000000 |
| com_leave_with_others | DASS_A_sum | 0.6910836 | 0.0000000 |
| com_locked | DASS_A_sum | 0.1550739 | 0.0487901 |
| com_room_task | DASS_A_sum | 0.3482976 | 0.0000056 |
| dist_daily | DASS_A_sum | 0.4652712 | 0.0000000 |
| dist_study | DASS_A_sum | 0.7312125 | 0.0000000 |
| NMPQ_sum | DASS_A_sum | 0.5653998 | 0.0000000 |
| SAD_sum | DASS_A_sum | 0.6917548 | 0.0000000 |
| SAD_dep_sum | DASS_A_sum | 0.5375888 | 0.0000000 |
| SAD_ea_sum | DASS_A_sum | 0.6762214 | 0.0000000 |
| SAD_access_sum | DASS_A_sum | 0.4526624 | 0.0000000 |
| SAD_dist_sum | DASS_A_sum | 0.7553105 | 0.0000000 |
| BIS_sum | DASS_A_sum | 0.6038188 | 0.0000000 |
| NEO_O_sum | DASS_A_sum | -0.5304120 | 0.0000000 |
| NEO_C_sum | DASS_A_sum | -0.5261535 | 0.0000000 |
| NEO_E_sum | DASS_A_sum | 0.2639105 | 0.0006903 |
| NEO_A_sum | DASS_A_sum | -0.6029903 | 0.0000000 |
| NEO_N_sum | DASS_A_sum | 0.6463389 | 0.0000000 |
| RSES_sum | DASS_A_sum | 0.1864430 | 0.0175241 |
| SRS_sum | DASS_A_sum | -0.3871973 | 0.0000004 |
| SSEIT_sum | DASS_A_sum | 0.0541297 | 0.4938960 |
| DASS_D_sum | DASS_A_sum | 0.8570701 | 0.0000000 |
| age | DASS_S_sum | -0.2936001 | 0.0001495 |
| age_first_phone | DASS_S_sum | 0.0341183 | 0.6664558 |
| com_gen | DASS_S_sum | 0.3973249 | 0.0000002 |
| com_unattended | DASS_S_sum | 0.4975096 | 0.0000000 |
| com_leave_with_others | DASS_S_sum | 0.5905186 | 0.0000000 |
| com_locked | DASS_S_sum | 0.1739796 | 0.0268174 |
| com_room_task | DASS_S_sum | 0.2789978 | 0.0003241 |
| dist_daily | DASS_S_sum | 0.5411997 | 0.0000000 |
| dist_study | DASS_S_sum | 0.6757534 | 0.0000000 |
| NMPQ_sum | DASS_S_sum | 0.6110779 | 0.0000000 |
| SAD_sum | DASS_S_sum | 0.7001342 | 0.0000000 |
| SAD_dep_sum | DASS_S_sum | 0.5813110 | 0.0000000 |
| SAD_ea_sum | DASS_S_sum | 0.6170449 | 0.0000000 |
| SAD_access_sum | DASS_S_sum | 0.5042739 | 0.0000000 |
| SAD_dist_sum | DASS_S_sum | 0.7197138 | 0.0000000 |
| BIS_sum | DASS_S_sum | 0.6013027 | 0.0000000 |
| NEO_O_sum | DASS_S_sum | -0.4936969 | 0.0000000 |
| NEO_C_sum | DASS_S_sum | -0.4845171 | 0.0000000 |
| NEO_E_sum | DASS_S_sum | 0.2004774 | 0.0105299 |
| NEO_A_sum | DASS_S_sum | -0.5746052 | 0.0000000 |
| NEO_N_sum | DASS_S_sum | 0.7309030 | 0.0000000 |
| RSES_sum | DASS_S_sum | 0.2228599 | 0.0043648 |
| SRS_sum | DASS_S_sum | -0.3744258 | 0.0000009 |
| SSEIT_sum | DASS_S_sum | 0.0515909 | 0.5144050 |
| DASS_D_sum | DASS_S_sum | 0.8636292 | 0.0000000 |
| DASS_A_sum | DASS_S_sum | 0.8949079 | 0.0000000 |
# print tables using kable
kable(as.data.frame(format(mturk_ST_corr2$r, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: r values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.1308 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | -0.2172 | -0.0615 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | -0.1747 | -0.0037 | 0.5807 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | -0.269 | -0.076 | 0.5743 | 0.7166 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | -0.2597 | 0.0089 | 0.0766 | -0.0263 | -0.0317 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | -0.0506 | -0.1364 | 0.4944 | 0.4968 | 0.4732 | -0.136 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | -0.3353 | -0.021 | 0.3098 | 0.3218 | 0.3804 | 0.2384 | 0.2082 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | -0.2951 | 0.0444 | 0.446 | 0.6572 | 0.668 | 0.1275 | 0.3565 | 0.5044 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | -0.2781 | -0.0751 | 0.3236 | 0.4786 | 0.5124 | 0.3051 | 0.1759 | 0.5916 | 0.6036 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | -0.3377 | -0.0497 | 0.3567 | 0.4055 | 0.4929 | 0.2656 | 0.1806 | 0.5194 | 0.5885 | 0.8156 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | -0.2918 | -0.0332 | 0.2731 | 0.315 | 0.3542 | 0.2799 | 0.096 | 0.4584 | 0.4549 | 0.7545 | 0.8841 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | -0.3336 | -0.0144 | 0.4087 | 0.4449 | 0.5077 | 0.1794 | 0.2333 | 0.444 | 0.5957 | 0.6958 | 0.9095 | 0.7102 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | -0.2145 | -0.1074 | 0.1769 | 0.2616 | 0.337 | 0.2987 | 0.0133 | 0.4092 | 0.4012 | 0.7309 | 0.8233 | 0.7269 | 0.6086 | 1 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | -0.2934 | -0.02 | 0.4216 | 0.3818 | 0.5327 | 0.0824 | 0.3294 | 0.4034 | 0.5974 | 0.5122 | 0.7468 | 0.4985 | 0.7504 | 0.4254 | 1 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | -0.1765 | 0.0035 | 0.1591 | 0.4429 | 0.5137 | -0.1469 | 0.2698 | 0.2833 | 0.5455 | 0.371 | 0.3613 | 0.2455 | 0.3108 | 0.2484 | 0.5135 | 1 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.0748 | 0.0379 | -0.3325 | -0.5275 | -0.5117 | 0.1103 | -0.3085 | -0.1214 | -0.548 | -0.2466 | -0.3051 | -0.2006 | -0.3076 | -0.1998 | -0.4405 | -0.467 | 1 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 0.263 | -0.0453 | -0.19 | -0.2341 | -0.3267 | 0.1834 | -0.1663 | -0.2341 | -0.4233 | -0.1234 | -0.2026 | -0.1126 | -0.2315 | -0.0258 | -0.4146 | -0.5933 | 0.3168 | 1 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | -0.2318 | -0.0691 | 0.4021 | 0.2549 | 0.1989 | 0.1833 | 0.2077 | 0.0691 | 0.2933 | 0.3011 | 0.3172 | 0.242 | 0.3745 | 0.22 | 0.2521 | 0.0481 | -0.1524 | 0.1661 | 1 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0.372 | 0.0455 | -0.3413 | -0.4007 | -0.513 | 0.0655 | -0.195 | -0.2447 | -0.5799 | -0.2195 | -0.3226 | -0.2204 | -0.3745 | -0.124 | -0.4904 | -0.5393 | 0.5593 | 0.6445 | -0.1182 | 1 | - | - | - | - | - | - | - |
| NEO_N_sum | -0.1193 | 0.0605 | 0.1929 | 0.3732 | 0.4563 | 0.062 | 0.1639 | 0.4819 | 0.5315 | 0.4527 | 0.5535 | 0.4726 | 0.465 | 0.3819 | 0.6002 | 0.5828 | -0.4415 | -0.4714 | -0.1358 | -0.4645 | 1 | - | - | - | - | - | - |
| RSES_sum | -0.079 | -0.0013 | 0.0659 | 0.0365 | 0.09 | -0.0404 | 0.1144 | 0.028 | 0.0178 | -0.0024 | 0.1399 | 0.0626 | 0.1772 | 0.0146 | 0.2589 | 0.107 | -0.1824 | -0.1294 | -0.1027 | -0.1811 | 0.1621 | 1 | - | - | - | - | - |
| SRS_sum | 0.137 | 0.0371 | -0.0619 | -0.0669 | -0.2272 | 0.1132 | -0.0687 | -0.2163 | -0.297 | -0.0459 | -0.2364 | -0.1315 | -0.2563 | -0.0505 | -0.4892 | -0.4871 | 0.2368 | 0.6447 | 0.1958 | 0.4995 | -0.5553 | -0.2088 | 1 | - | - | - | - |
| SSEIT_sum | -0.0523 | -0.0833 | 0.1658 | 0.0639 | -0.0013 | 0.3132 | 0.1043 | 0.0708 | 0.0493 | 0.3574 | 0.291 | 0.2654 | 0.2816 | 0.2811 | 0.0369 | -0.2143 | 0.1718 | 0.4717 | 0.4313 | 0.3909 | -0.2282 | -0.0158 | 0.5424 | 1 | - | - | - |
| DASS_D_sum | -0.342 | -0.0696 | 0.4467 | 0.5342 | 0.6288 | 0.1217 | 0.3323 | 0.49 | 0.6686 | 0.5464 | 0.614 | 0.4894 | 0.5715 | 0.4159 | 0.6672 | 0.6195 | -0.5217 | -0.5822 | 0.1265 | -0.699 | 0.6813 | 0.2618 | -0.4375 | -0.089 | 1 | - | - |
| DASS_A_sum | -0.3764 | -0.0516 | 0.5077 | 0.5732 | 0.6911 | 0.1551 | 0.3483 | 0.4653 | 0.7312 | 0.5654 | 0.6918 | 0.5376 | 0.6762 | 0.4527 | 0.7553 | 0.6038 | -0.5304 | -0.5262 | 0.2639 | -0.603 | 0.6463 | 0.1864 | -0.3872 | 0.0541 | 0.8571 | 1 | - |
| DASS_S_sum | -0.2936 | 0.0341 | 0.3973 | 0.4975 | 0.5905 | 0.174 | 0.279 | 0.5412 | 0.6758 | 0.6111 | 0.7001 | 0.5813 | 0.617 | 0.5043 | 0.7197 | 0.6013 | -0.4937 | -0.4845 | 0.2005 | -0.5746 | 0.7309 | 0.2229 | -0.3744 | 0.0516 | 0.8636 | 0.8949 | 1 |
kable(as.data.frame(format(mturk_ST_corr2$P, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: p values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 0.0972 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 0.0055 | 0.4365 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 0.0262 | 0.9627 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 5e-04 | 0.3361 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 8e-04 | 0.9105 | 0.3328 | 0.7399 | 0.6886 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 0.5221 | 0.0836 | 0 | 0 | 0 | 0.0844 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 0 | 0.7906 | 1e-04 | 0 | 0 | 0.0023 | 0.0078 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 1e-04 | 0.5745 | 0 | 0 | 0 | 0.106 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 3e-04 | 0.3423 | 0 | 0 | 0 | 1e-04 | 0.0251 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 0 | 0.5303 | 0 | 0 | 0 | 6e-04 | 0.0214 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 2e-04 | 0.6747 | 4e-04 | 0 | 0 | 3e-04 | 0.2241 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 0 | 0.8553 | 0 | 0 | 0 | 0.0223 | 0.0028 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 0.0061 | 0.1735 | 0.0243 | 8e-04 | 0 | 1e-04 | 0.8666 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 2e-04 | 0.801 | 0 | 0 | 0 | 0.297 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 0.0247 | 0.9645 | 0.0431 | 0 | 0 | 0.0621 | 5e-04 | 3e-04 | 0 | 0 | 0 | 0.0016 | 1e-04 | 0.0014 | 0 | NA | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 0.3443 | 0.6323 | 0 | 0 | 0 | 0.1624 | 1e-04 | 0.1239 | 0 | 0.0016 | 1e-04 | 0.0105 | 1e-04 | 0.0108 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 7e-04 | 0.5671 | 0.0154 | 0.0027 | 0 | 0.0195 | 0.0344 | 0.0027 | 0 | 0.1177 | 0.0097 | 0.1536 | 0.003 | 0.7444 | 0 | 0 | 0 | NA | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 0.003 | 0.3824 | 0 | 0.0011 | 0.0111 | 0.0195 | 0.008 | 0.3821 | 2e-04 | 1e-04 | 0 | 0.0019 | 0 | 0.0049 | 0.0012 | 0.543 | 0.0529 | 0.0346 | NA | - | - | - | - | - | - | - | - |
| NEO_A_sum | 0 | 0.5651 | 0 | 0 | 0 | 0.4074 | 0.0129 | 0.0017 | 0 | 0.005 | 0 | 0.0048 | 0 | 0.116 | 0 | 0 | 0 | 0 | 0.134 | NA | - | - | - | - | - | - | - |
| NEO_N_sum | 0.1304 | 0.4442 | 0.0139 | 0 | 0 | 0.4331 | 0.0371 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0849 | 0 | NA | - | - | - | - | - | - |
| RSES_sum | 0.3176 | 0.9871 | 0.4049 | 0.6446 | 0.2547 | 0.6097 | 0.1472 | 0.7239 | 0.8226 | 0.9758 | 0.0759 | 0.4288 | 0.0241 | 0.8537 | 9e-04 | 0.1754 | 0.0202 | 0.1008 | 0.1935 | 0.0211 | 0.0393 | NA | - | - | - | - | - |
| SRS_sum | 0.0821 | 0.6395 | 0.4336 | 0.3973 | 0.0036 | 0.1515 | 0.3848 | 0.0057 | 1e-04 | 0.5617 | 0.0025 | 0.0954 | 0.001 | 0.523 | 0 | 0 | 0.0024 | 0 | 0.0125 | 0 | 0 | 0.0077 | NA | - | - | - | - |
| SSEIT_sum | 0.5085 | 0.292 | 0.035 | 0.419 | 0.9864 | 0 | 0.1868 | 0.3707 | 0.5332 | 0 | 2e-04 | 6e-04 | 3e-04 | 3e-04 | 0.6415 | 0.0062 | 0.0288 | 0 | 0 | 0 | 0.0035 | 0.8421 | 0 | NA | - | - | - |
| DASS_D_sum | 0 | 0.3788 | 0 | 0 | 0 | 0.1228 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1086 | 0 | 0 | 8e-04 | 0 | 0.26 | NA | - | - |
| DASS_A_sum | 0 | 0.5145 | 0 | 0 | 0 | 0.0488 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7e-04 | 0 | 0 | 0.0175 | 0 | 0.4939 | 0 | NA | - |
| DASS_S_sum | 1e-04 | 0.6665 | 0 | 0 | 0 | 0.0268 | 3e-04 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0105 | 0 | 0 | 0.0044 | 0 | 0.5144 | 0 | 0 | NA |
kable(as.data.frame(format(mturk_ST_corr2$n, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: n values") %>%
kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>%
kable_classic()| age | age_first_phone | com_gen | com_unattended | com_leave_with_others | com_locked | com_room_task | dist_daily | dist_study | NMPQ_sum | SAD_sum | SAD_dep_sum | SAD_ea_sum | SAD_access_sum | SAD_dist_sum | BIS_sum | NEO_O_sum | NEO_C_sum | NEO_E_sum | NEO_A_sum | NEO_N_sum | RSES_sum | SRS_sum | SSEIT_sum | DASS_D_sum | DASS_A_sum | DASS_S_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| age_first_phone | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_gen | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_unattended | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_leave_with_others | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_locked | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| com_room_task | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_daily | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| dist_study | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| NMPQ_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dep_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_ea_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_access_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - | - |
| SAD_dist_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - | - |
| BIS_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - | - |
| NEO_O_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - | - |
| NEO_C_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - | - |
| NEO_E_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - | - |
| NEO_A_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - | - |
| NEO_N_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - | - |
| RSES_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - | - |
| SRS_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - | - |
| SSEIT_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - | - |
| DASS_D_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - | - |
| DASS_A_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | - |
| DASS_S_sum | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 | 162 |
# create new matrix for r & p vals with different col names
# get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
mturk_ST_corr_r <- mturk_ST_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
mturk_ST_corr_p <- mturk_ST_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
colnames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
rownames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")
# all vars included
corr_plot_mturk_ST_all <- {
corrplot(mturk_ST_corr$r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_ST_corr$P, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .5, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# with all SAD subscales
corr_plot_mturk_ST_ques_all <- {
corrplot(mturk_ST_corr_r, method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_ST_corr_p, sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# final -- with SAD overall only
corr_plot_mturk_ST_ques_final <- {
corrplot(mturk_ST_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
col = (colorRampPalette(corr_colours)(5000)),
# type = "upper",
diag = F, # remove central diagonal
tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
addgrid.col = NA, # add grid colour
# addCoef.col = "black", # Add coefficient of correlation
# addCoefasPercent = T # add as coefficient as percent
# number.cex = .25, # size of the coeff
# number.digits = 2, # num decimals in coeff
p.mat = mturk_ST_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
insig = "label_sig", pch.cex = .75, pch.col = "black"
# insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
)
recordPlot()
}Warning in ind1:ind2 :
numerical expression has 10 elements: only the first used
# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_all(oct28)", plot = replayPlot(corr_plot_mturk_ST_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_ques_all(oct28)", plot = replayPlot(corr_plot_mturk_ST_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_ques_final(oct28)", plot = replayPlot(corr_plot_mturk_ST_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
# export csv files of R values & p values
write.csv(mturk_ST_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_ST_corr_rvals(oct28).csv", row.names = T)
write.csv(mturk_ST_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_ST_corr_pvals(oct28).csv", row.names = T)Regressions
This data only included ps who reported both having an iPhone (SONA = 209; Mturk = 248) and had ST activated prior to the study (SONA = 175 ; Mturk = 162) rather than the total sample (SONA = 240; Mturk = 505).
Note: Since this was an exploratory analysis, the predictor of “age of first phone” and other ST-related continuous variables (i.e., total weekly ST, daily pickups, daily notifications) were added to each model.
Study 1: SONA
NMPQ
This regression used the following formula: \(NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 1.84, p = .03, R2 = .17
sig predictors:
- NEO: O, t(156) = -2.36, p = .02
- NEO: N, t(156) = 2.01, p = .05 (.0458)
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-57.669 -13.950 -0.244 15.831 45.745
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 87.397705 43.193181 2.023 0.0447 *
age 0.574800 1.219533 0.471 0.6381
age_first_phone -0.790636 0.974347 -0.811 0.4183
ST_weekly_tot_hours 0.130148 0.094415 1.378 0.1700
ST_daily_pickups -0.003797 0.011045 -0.344 0.7315
ST_daily_not -0.004203 0.008147 -0.516 0.6067
BIS_sum -0.512263 0.713963 -0.717 0.4741
DASS_D_sum 0.228048 0.651958 0.350 0.7270
DASS_A_sum 0.043498 0.725612 0.060 0.9523
DASS_S_sum 0.729499 0.823060 0.886 0.3768
NEO_O_sum -0.750727 0.300345 -2.500 0.0135 *
NEO_C_sum -0.319644 0.449969 -0.710 0.4785
NEO_E_sum 0.376744 0.350890 1.074 0.2846
NEO_A_sum -0.121438 0.300796 -0.404 0.6870
NEO_N_sum 0.531846 0.319224 1.666 0.0977 .
RSES_sum -0.184455 0.459207 -0.402 0.6885
SSEIT_sum 0.302743 0.168967 1.792 0.0751 .
SRS_sum -0.610149 0.494721 -1.233 0.2193
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.96 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1585, Adjusted R-squared: 0.06682
F-statistic: 1.729 on 17 and 156 DF, p-value: 0.04278# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum))SAD
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 3.78, p = .03, R2 = .29
sig predictors:
- ST Hours, t(156) = 2.17, p = .03
- DAS: S, t(156) = 1.99, p = .05 (.048)
- NEO: A, t(156) = -2.36, p = .02
- NEO: N, t(156) = 2.11, p = .04
- SSEIT, t(156) = 3.30, p = .001
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-34.889 -6.901 -0.034 8.106 27.152
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 66.5300294 24.8331579 2.679 0.00817 **
age -0.6727890 0.7011489 -0.960 0.33877
age_first_phone -0.8096564 0.5601836 -1.445 0.15037
ST_weekly_tot_hours 0.1208576 0.0542822 2.226 0.02742 *
ST_daily_pickups -0.0095358 0.0063502 -1.502 0.13521
ST_daily_not 0.0009335 0.0046843 0.199 0.84231
BIS_sum -0.0007044 0.4104802 -0.002 0.99863
DASS_D_sum 0.2068592 0.3748320 0.552 0.58183
DASS_A_sum -0.3832978 0.4171777 -0.919 0.35962
DASS_S_sum 0.9966098 0.4732041 2.106 0.03680 *
NEO_O_sum -0.3368499 0.1726780 -1.951 0.05288 .
NEO_C_sum -0.3480216 0.2587019 -1.345 0.18049
NEO_E_sum 0.0267171 0.2017383 0.132 0.89481
NEO_A_sum -0.3116294 0.1729372 -1.802 0.07348 .
NEO_N_sum 0.2631857 0.1835320 1.434 0.15357
RSES_sum -0.0429989 0.2640131 -0.163 0.87083
SSEIT_sum 0.3261129 0.0971449 3.357 0.00099 ***
SRS_sum 0.0673136 0.2844313 0.237 0.81323
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.63 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2763, Adjusted R-squared: 0.1974
F-statistic: 3.503 on 17 and 156 DF, p-value: 1.549e-05# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_sum ~ ST_weekly_tot_hours + DASS_S_sum + NEO_A_sum + NEO_N_sum + SSEIT_sum))SAD: Dependence
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 3.78, p = .03, R2 = .29
sig predictors:
- NEO: O, t(156) = -2.58, p = .01
- NEO: C, t(156) = -2.13, p = .04
- SSEIT, t(156) = -2.79, p = .006
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-9.8837 -2.7346 0.1241 3.0374 7.8712
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.8308602 8.6117785 2.419 0.01672 *
age -0.1289435 0.2431483 -0.530 0.59665
age_first_phone -0.1405594 0.1942635 -0.724 0.47043
ST_weekly_tot_hours 0.0237016 0.0188243 1.259 0.20988
ST_daily_pickups -0.0032626 0.0022022 -1.482 0.14047
ST_daily_not 0.0004183 0.0016244 0.258 0.79711
BIS_sum -0.0112249 0.1423486 -0.079 0.93725
DASS_D_sum -0.0713859 0.1299863 -0.549 0.58367
DASS_A_sum -0.0895343 0.1446712 -0.619 0.53690
DASS_S_sum 0.2075302 0.1641003 1.265 0.20788
NEO_O_sum -0.1653028 0.0598822 -2.760 0.00646 **
NEO_C_sum -0.1797264 0.0897141 -2.003 0.04687 *
NEO_E_sum -0.0016750 0.0699599 -0.024 0.98093
NEO_A_sum -0.0799761 0.0599721 -1.334 0.18429
NEO_N_sum 0.0783664 0.0636462 1.231 0.22007
RSES_sum 0.0585882 0.0915559 0.640 0.52317
SSEIT_sum 0.0957720 0.0336884 2.843 0.00507 **
SRS_sum -0.0651562 0.0986367 -0.661 0.50986
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.379 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2039, Adjusted R-squared: 0.1171
F-statistic: 2.35 on 17 and 156 DF, p-value: 0.003137# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ NEO_O_sum + NEO_C_sum + SSEIT_sum))SAD: Accessibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 1.91, p = .02, R2 = .17
sig predictors:
- Age first phone, t(156) = -2.10, p = .04
- SSEIT, t(156) = 2.23, p = .03
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-12.3116 -2.3744 0.1514 2.9421 7.8028
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.1611074 7.6182335 1.334 0.1842
age 0.1111423 0.2150961 0.517 0.6061
age_first_phone -0.3674877 0.1718513 -2.138 0.0340 *
ST_weekly_tot_hours 0.0281174 0.0166525 1.688 0.0933 .
ST_daily_pickups -0.0015660 0.0019481 -0.804 0.4227
ST_daily_not -0.0003168 0.0014370 -0.220 0.8258
BIS_sum -0.0333708 0.1259258 -0.265 0.7914
DASS_D_sum 0.1070453 0.1149897 0.931 0.3533
DASS_A_sum -0.1006927 0.1279804 -0.787 0.4326
DASS_S_sum 0.1652732 0.1451680 1.138 0.2567
NEO_O_sum -0.0529182 0.0529736 -0.999 0.3194
NEO_C_sum -0.0177838 0.0793637 -0.224 0.8230
NEO_E_sum -0.0077500 0.0618886 -0.125 0.9005
NEO_A_sum -0.0450726 0.0530531 -0.850 0.3969
NEO_N_sum 0.0798234 0.0563033 1.418 0.1583
RSES_sum -0.0019210 0.0809931 -0.024 0.9811
SSEIT_sum 0.0670841 0.0298018 2.251 0.0258 *
SRS_sum 0.0021102 0.0872569 0.024 0.9807
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.874 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.1659, Adjusted R-squared: 0.07501
F-statistic: 1.825 on 17 and 156 DF, p-value: 0.02917# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age_first_phone + SSEIT_sum))SAD: Emotional Attachment
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 3.74, p < .001, R2 = .29
sig predictors:
- ST Hours, t(156) = 2.14, p = .03
- DAS: S, t(156) = 2.51, p = .01
- NEO: A, t(156) = -2.74, p = .007
- SSEIT, t(156) = 2.37, p = .02
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-11.2842 -2.7840 -0.3623 3.2932 11.5420
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.7324514 8.6601246 2.279 0.02405 *
age -0.3318977 0.2445133 -1.357 0.17662
age_first_phone -0.1824215 0.1953541 -0.934 0.35185
ST_weekly_tot_hours 0.0415069 0.0189299 2.193 0.02981 *
ST_daily_pickups -0.0016532 0.0022145 -0.747 0.45647
ST_daily_not 0.0008103 0.0016336 0.496 0.62056
BIS_sum -0.0271707 0.1431477 -0.190 0.84971
DASS_D_sum 0.1491162 0.1307160 1.141 0.25572
DASS_A_sum -0.1348305 0.1454833 -0.927 0.35547
DASS_S_sum 0.4363509 0.1650216 2.644 0.00903 **
NEO_O_sum -0.0914383 0.0602184 -1.518 0.13093
NEO_C_sum -0.1005374 0.0902177 -1.114 0.26683
NEO_E_sum 0.0548415 0.0703527 0.780 0.43685
NEO_A_sum -0.1319097 0.0603088 -2.187 0.03021 *
NEO_N_sum 0.0383168 0.0640035 0.599 0.55026
RSES_sum -0.0381714 0.0920699 -0.415 0.67901
SSEIT_sum 0.0831594 0.0338776 2.455 0.01520 *
SRS_sum 0.1542970 0.0991904 1.556 0.12184
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.404 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2738, Adjusted R-squared: 0.1947
F-statistic: 3.461 on 17 and 156 DF, p-value: 1.891e-05# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ ST_weekly_tot_hours + DASS_S_sum + NEO_A_sum + SSEIT_sum))SAD: Distractibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 156) = 2.64, p < .001, R2 = .22
sig predictors:
- ST Hours, t(156) = 1.95, p = .05 (almost sig)
- NEO: N, t(156) = 2.05, p = .04
- SSEIT, t(156) = 2.16, p = .03
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-6.0087 -1.7184 0.1769 1.6781 5.1851
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.2722445 4.6456936 1.996 0.0477 *
age -0.2745160 0.1311683 -2.093 0.0380 *
age_first_phone -0.0107735 0.1047970 -0.103 0.9183
ST_weekly_tot_hours 0.0198063 0.0101549 1.950 0.0529 .
ST_daily_pickups -0.0023573 0.0011880 -1.984 0.0490 *
ST_daily_not -0.0004502 0.0008763 -0.514 0.6081
BIS_sum 0.0435259 0.0767911 0.567 0.5717
DASS_D_sum 0.0152891 0.0701222 0.218 0.8277
DASS_A_sum 0.0111565 0.0780440 0.143 0.8865
DASS_S_sum 0.1151906 0.0885252 1.301 0.1951
NEO_O_sum 0.0018944 0.0323039 0.059 0.9533
NEO_C_sum 0.0019202 0.0483970 0.040 0.9684
NEO_E_sum 0.0103869 0.0377404 0.275 0.7835
NEO_A_sum -0.0351583 0.0323524 -1.087 0.2788
NEO_N_sum 0.0526853 0.0343345 1.534 0.1269
RSES_sum -0.0677513 0.0493906 -1.372 0.1721
SSEIT_sum 0.0419260 0.0181735 2.307 0.0224 *
SRS_sum -0.0207847 0.0532103 -0.391 0.6966
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.362 on 156 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.2278, Adjusted R-squared: 0.1436
F-statistic: 2.707 on 17 and 156 DF, p-value: 0.0006272# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ NEO_N_sum + SSEIT_sum))Study 2: Mturk
NMPQ
This regression used the following formula: \(NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 12.24, p < .001, R2 = .59
sig predictors:
- Age, t(143) = -2.42, p = .02
- ST: Pickups, t(143) = -1.96, p = .05 (almost sig)
- DASS: D, t(143) = 2.70, p = .008
- NEO: N, t(143) = 2.86, p = .005
- RSES, t(143) = -2.54, p = .01
- SSEIT, t(143) = 3.57, p < .001
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-54.573 -6.189 0.907 9.160 48.248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -32.727403 27.707927 -1.181 0.239501
age -0.365110 0.158880 -2.298 0.023010 *
age_first_phone -0.004768 0.007286 -0.654 0.513869
ST_weekly_tot_hours -0.143089 0.089873 -1.592 0.113565
ST_daily_pickups -0.018518 0.014539 -1.274 0.204855
ST_daily_not -0.019553 0.010182 -1.920 0.056807 .
BIS_sum 0.522839 0.476589 1.097 0.274466
DASS_D_sum 1.353209 0.533366 2.537 0.012250 *
DASS_A_sum -0.476778 0.580432 -0.821 0.412776
DASS_S_sum 0.917093 0.643874 1.424 0.156528
NEO_O_sum 0.052355 0.284503 0.184 0.854257
NEO_C_sum 0.128775 0.394499 0.326 0.744579
NEO_E_sum 0.287706 0.201761 1.426 0.156055
NEO_A_sum 0.429218 0.324958 1.321 0.188665
NEO_N_sum 0.846372 0.279630 3.027 0.002933 **
RSES_sum -0.636578 0.249775 -2.549 0.011871 *
SSEIT_sum 0.502160 0.136626 3.675 0.000335 ***
SRS_sum 0.306112 0.482068 0.635 0.526444
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 16.92 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.5891, Adjusted R-squared: 0.5402
F-statistic: 12.06 on 17 and 143 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + ST_daily_pickups + DASS_D_sum + NEO_N_sum + RSES_sum + SSEIT_sum))SAD
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 17.02, p < .001, R2 = .67
sig predictors:
- Age, t(143) = -2.58, p = .01
- ST Pickups, t(143) = -2.62, p = .01 (.0099)
- NEO: E, t(143) = -1.96, p = .05 (almost sig)
- NEO: N, t(143) = 3.82, p < .001
- SSEIT, t(143) = 3.99, p < .001
- SRS, t(143) = -2.09, p = .04
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-34.830 -3.350 1.127 4.205 24.621
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.504e+00 1.466e+01 0.239 0.811444
age -2.095e-01 8.408e-02 -2.492 0.013833 *
age_first_phone 5.141e-05 3.856e-03 0.013 0.989380
ST_weekly_tot_hours -3.385e-02 4.756e-02 -0.712 0.477785
ST_daily_pickups -2.057e-02 7.694e-03 -2.674 0.008362 **
ST_daily_not -1.822e-03 5.388e-03 -0.338 0.735730
BIS_sum -2.757e-01 2.522e-01 -1.093 0.276239
DASS_D_sum 2.604e-01 2.822e-01 0.923 0.357815
DASS_A_sum 4.241e-01 3.072e-01 1.381 0.169543
DASS_S_sum 3.836e-01 3.407e-01 1.126 0.262098
NEO_O_sum 7.186e-02 1.506e-01 0.477 0.633871
NEO_C_sum 2.225e-01 2.088e-01 1.066 0.288218
NEO_E_sum 1.920e-01 1.068e-01 1.798 0.074308 .
NEO_A_sum 1.152e-01 1.720e-01 0.670 0.503838
NEO_N_sum 5.687e-01 1.480e-01 3.843 0.000182 ***
RSES_sum -6.248e-02 1.322e-01 -0.473 0.637124
SSEIT_sum 2.911e-01 7.230e-02 4.027 9.14e-05 ***
SRS_sum -5.225e-01 2.551e-01 -2.048 0.042379 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.955 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.6673, Adjusted R-squared: 0.6278
F-statistic: 16.87 on 17 and 143 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + ST_daily_pickups + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD: Dependence
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 8.13, p < .001, R2 = .49
sig predictors:
- Age, t(143) = -2.59, p = .01
- NEO: N, t(143) = 3.11, p = .002
- SSEIT, t(143) = 2.16, p = .03
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-8.6758 -1.6212 0.1576 1.8150 8.5429
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.4719584 5.1721883 -0.285 0.776369
age -0.0731618 0.0296578 -2.467 0.014811 *
age_first_phone -0.0001654 0.0013601 -0.122 0.903367
ST_weekly_tot_hours -0.0094574 0.0167764 -0.564 0.573820
ST_daily_pickups -0.0049859 0.0027140 -1.837 0.068267 .
ST_daily_not -0.0021344 0.0019007 -1.123 0.263340
BIS_sum -0.1209112 0.0889640 -1.359 0.176254
DASS_D_sum 0.0761799 0.0995624 0.765 0.445445
DASS_A_sum 0.0099962 0.1083482 0.092 0.926621
DASS_S_sum 0.1999939 0.1201908 1.664 0.098308 .
NEO_O_sum 0.0402800 0.0531076 0.758 0.449424
NEO_C_sum 0.0642547 0.0736405 0.873 0.384374
NEO_E_sum 0.0416937 0.0376623 1.107 0.270136
NEO_A_sum 0.0399767 0.0606594 0.659 0.510933
NEO_N_sum 0.1768181 0.0521980 3.387 0.000911 ***
RSES_sum -0.0552043 0.0466251 -1.184 0.238376
SSEIT_sum 0.0590287 0.0255037 2.315 0.022064 *
SRS_sum -0.0456686 0.0899868 -0.508 0.612583
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.159 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.4919, Adjusted R-squared: 0.4315
F-statistic: 8.145 on 17 and 143 DF, p-value: 4.548e-14# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + NEO_N_sum + SSEIT_sum))SAD: Accessibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 6.44, p < .001, R2 = .43
sig predictors:
- Age, t(143) = -2.02, p = .05 (.046)
- ST Pickups, t(143) = -2.96, p = .004
- NEO: N, t(143) = 2.39, p = .02
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-9.4038 -1.3937 0.0622 1.9552 6.9396
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.330289 5.039107 -0.264 0.79217
age -0.059547 0.028895 -2.061 0.04113 *
age_first_phone -0.001859 0.001325 -1.403 0.16288
ST_weekly_tot_hours -0.011351 0.016345 -0.694 0.48851
ST_daily_pickups -0.007872 0.002644 -2.977 0.00342 **
ST_daily_not -0.001284 0.001852 -0.693 0.48918
BIS_sum 0.029989 0.086675 0.346 0.72986
DASS_D_sum 0.102462 0.097001 1.056 0.29261
DASS_A_sum -0.070032 0.105560 -0.663 0.50812
DASS_S_sum 0.239075 0.117098 2.042 0.04302 *
NEO_O_sum 0.002430 0.051741 0.047 0.96261
NEO_C_sum 0.097228 0.071746 1.355 0.17750
NEO_E_sum 0.016288 0.036693 0.444 0.65779
NEO_A_sum 0.107022 0.059099 1.811 0.07226 .
NEO_N_sum 0.108150 0.050855 2.127 0.03517 *
RSES_sum -0.074054 0.045425 -1.630 0.10525
SSEIT_sum 0.043344 0.024848 1.744 0.08324 .
SRS_sum -0.007183 0.087671 -0.082 0.93482
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.077 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.4311, Adjusted R-squared: 0.3635
F-statistic: 6.374 on 17 and 143 DF, p-value: 5.678e-11# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + ST_daily_pickups + NEO_N_sum))SAD: Emotional Attachment
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 14.03, p < .001, R2 = .63
sig predictors:
- BIS, t(143) = -2.11, p = .04
- DASS: A, t(143) = 3.11, p = .002
- NEO: E, t(143) = 2.54, p = .01
- NEO: N, t(143) = 3.25, p = .002
- SSEIT, t(143) = 4.74, p < .001
- SRS, t(143) = -2.93, p = .004
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-10.3183 -1.3847 0.3242 1.5276 8.5285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.3024412 5.2726320 0.816 0.41586
age -0.0368347 0.0302337 -1.218 0.22510
age_first_phone 0.0018350 0.0013865 1.323 0.18780
ST_weekly_tot_hours -0.0208992 0.0171022 -1.222 0.22371
ST_daily_pickups -0.0055044 0.0027667 -1.990 0.04855 *
ST_daily_not 0.0008099 0.0019376 0.418 0.67659
BIS_sum -0.1929185 0.0906916 -2.127 0.03512 *
DASS_D_sum 0.0651714 0.1014959 0.642 0.52183
DASS_A_sum 0.3565393 0.1104524 3.228 0.00155 **
DASS_S_sum -0.1939827 0.1225249 -1.583 0.11558
NEO_O_sum 0.0242563 0.0541390 0.448 0.65480
NEO_C_sum 0.0237037 0.0750706 0.316 0.75265
NEO_E_sum 0.0986375 0.0383937 2.569 0.01122 *
NEO_A_sum -0.0680470 0.0618374 -1.100 0.27300
NEO_N_sum 0.1739940 0.0532117 3.270 0.00135 **
RSES_sum 0.0318669 0.0475306 0.670 0.50365
SSEIT_sum 0.1258551 0.0259990 4.841 3.31e-06 ***
SRS_sum -0.2581471 0.0917343 -2.814 0.00558 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.22 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.6269, Adjusted R-squared: 0.5826
F-statistic: 14.13 on 17 and 143 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ BIS_sum + DASS_A_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD: Distractibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(17, 143) = 18.11, p < .001, R2 = .68
sig predictors:
- DASS: A, t(143) = 3.20, p = .002
- NEO: E, t(143) = 1.94, p = .05 (almost sig, .055)
- SSEIT, t(143) = 2.24, p = .03
- SRS, t(143) = -4.66, p < .001
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours +
ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data %>%
filter(ST == "had ST"))
Residuals:
Min 1Q Median 3Q Max
-5.0921 -1.0464 0.0842 1.1036 5.8082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.4420634 2.9527579 1.843 0.06739 .
age -0.0071313 0.0169314 -0.421 0.67425
age_first_phone 0.0004859 0.0007765 0.626 0.53244
ST_weekly_tot_hours -0.0033065 0.0095775 -0.345 0.73043
ST_daily_pickups -0.0010958 0.0015494 -0.707 0.48058
ST_daily_not 0.0016105 0.0010851 1.484 0.13996
BIS_sum 0.0046175 0.0507888 0.091 0.92769
DASS_D_sum -0.0162172 0.0568393 -0.285 0.77581
DASS_A_sum 0.1993537 0.0618551 3.223 0.00157 **
DASS_S_sum 0.0178324 0.0686159 0.260 0.79533
NEO_O_sum -0.0263926 0.0303187 -0.871 0.38548
NEO_C_sum 0.0316367 0.0420407 0.753 0.45297
NEO_E_sum 0.0412085 0.0215011 1.917 0.05729 .
NEO_A_sum -0.0018917 0.0346299 -0.055 0.95651
NEO_N_sum 0.0492056 0.0297994 1.651 0.10089
RSES_sum 0.0477780 0.0266179 1.795 0.07477 .
SSEIT_sum 0.0334370 0.0145599 2.297 0.02310 *
SRS_sum -0.2360795 0.0513727 -4.595 9.4e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.803 on 143 degrees of freedom
(1 observation deleted due to missingness)
Multiple R-squared: 0.683, Adjusted R-squared: 0.6453
F-statistic: 18.12 on 17 and 143 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ NEO_N_sum + SSEIT_sum))Exploratory Analyses: Including Age of First Smartphone Predictor
All Data
Study 1: SONA
NMPQ
This regression used the following formula: \(NMPQ = age + BIS + DASS: D + DASS: A + DASS: S + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 2.78, p < .001, R2 = .15
sig predictors:
- NEO: O, t(225) = -2.19, p = .03
- NEO: N, t(225) = 3.10, p = .002
- SSEIT, t(225) = 2.52, p = .01
- SRS, t(225) = -2.27, p = .02
# run lm + give summary
summary(lm(data = SONA_data, formula = NMPQ_sum ~ age + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = NMPQ_sum ~ age + BIS_sum + DASS_D_sum + DASS_A_sum +
DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum +
NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-61.225 -13.624 -0.002 14.614 55.358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 83.73122 32.30362 2.592 0.01016 *
age -0.64849 0.67399 -0.962 0.33700
BIS_sum -0.47233 0.57083 -0.827 0.40886
DASS_D_sum -0.49156 0.48685 -1.010 0.31373
DASS_A_sum 0.26087 0.58044 0.449 0.65354
DASS_S_sum 0.53740 0.64578 0.832 0.40619
NEO_O_sum -0.59817 0.24687 -2.423 0.01618 *
NEO_C_sum -0.03698 0.36866 -0.100 0.92018
NEO_E_sum 0.21302 0.26232 0.812 0.41763
NEO_A_sum -0.07458 0.24882 -0.300 0.76466
NEO_N_sum 0.70429 0.25400 2.773 0.00602 **
RSES_sum 0.19876 0.36088 0.551 0.58234
SSEIT_sum 0.31572 0.12283 2.570 0.01080 *
SRS_sum -0.92889 0.38813 -2.393 0.01752 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.41 on 226 degrees of freedom
Multiple R-squared: 0.1408, Adjusted R-squared: 0.09133
F-statistic: 2.848 on 13 and 226 DF, p-value: 0.000816# summary(lm(data = SONA_data, formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Overall
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 3.74, p < .001, R2 = .19
sig predictors:
- NEO: A, t(225) = -2.32, p = .02
- NEO: N, t(225) = 2.65, p = .009
- SSEIT, t(225) = 4.34, p < .001
- SRS, t(225) = -2.27, p = .02
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-37.242 -6.550 0.360 8.305 36.126
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 45.25349 19.65132 2.303 0.0222 *
age -0.10234 0.44388 -0.231 0.8179
age_first_phone -0.45945 0.46154 -0.995 0.3206
BIS_sum -0.02004 0.34462 -0.058 0.9537
DASS_D_sum 0.01110 0.29365 0.038 0.9699
DASS_A_sum -0.17344 0.35024 -0.495 0.6209
DASS_S_sum 0.56749 0.38946 1.457 0.1465
NEO_O_sum -0.30357 0.14921 -2.035 0.0431 *
NEO_C_sum -0.01402 0.22234 -0.063 0.9498
NEO_E_sum -0.02445 0.15911 -0.154 0.8780
NEO_A_sum -0.23518 0.15017 -1.566 0.1187
NEO_N_sum 0.31513 0.15324 2.056 0.0409 *
RSES_sum 0.13804 0.21767 0.634 0.5266
SSEIT_sum 0.32438 0.07408 4.379 1.83e-05 ***
SRS_sum -0.37778 0.23453 -1.611 0.1086
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.91 on 225 degrees of freedom
Multiple R-squared: 0.1743, Adjusted R-squared: 0.1229
F-statistic: 3.393 on 14 and 225 DF, p-value: 5.45e-05# summary(lm(data = SONA_data, formula = SAD_sum ~ NEO_A_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Dependency
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 2.39, p = .004, R2 = .13
sig predictors:
- NEO: O, t(225) = -2.93, p = .004
- NEO: N, t(225) = 1.94, p = .05 (almost sig)
- SSEIT, t(225) = 3.03, p = .003
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-12.6431 -2.3611 -0.0162 3.1144 10.2646
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.941037 6.713797 2.225 0.02705 *
age 0.076331 0.151648 0.503 0.61521
age_first_phone -0.114534 0.157682 -0.726 0.46838
BIS_sum -0.019069 0.117737 -0.162 0.87148
DASS_D_sum -0.053462 0.100325 -0.533 0.59464
DASS_A_sum -0.013781 0.119659 -0.115 0.90841
DASS_S_sum 0.041479 0.133059 0.312 0.75553
NEO_O_sum -0.160424 0.050976 -3.147 0.00187 **
NEO_C_sum -0.059395 0.075961 -0.782 0.43509
NEO_E_sum -0.002392 0.054361 -0.044 0.96495
NEO_A_sum -0.053253 0.051304 -1.038 0.30039
NEO_N_sum 0.082210 0.052355 1.570 0.11776
RSES_sum 0.071833 0.074367 0.966 0.33511
SSEIT_sum 0.077352 0.025308 3.056 0.00251 **
SRS_sum -0.145011 0.080125 -1.810 0.07166 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.411 on 225 degrees of freedom
Multiple R-squared: 0.121, Adjusted R-squared: 0.06634
F-statistic: 2.213 on 14 and 225 DF, p-value: 0.008164# summary(lm(data = SONA_data, formula = SAD_dep_sum ~ NEO_O_sum + NEO_N_sum + SSEIT_sum))SAD - Accessibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 3.25, p < .001, R2 = .17
sig predictors:
- Age phone, t(225) = -2.13, p = .03
- NEO: N, t(225) = 3.00, p = .003
- SSEIT, t(225) = 3.47, p < .001
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_access_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_access_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-12.8531 -2.1439 0.1179 2.7771 8.5869
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.069689 5.638846 0.899 0.369579
age -0.025651 0.127368 -0.201 0.840573
age_first_phone -0.271401 0.132436 -2.049 0.041592 *
BIS_sum 0.006306 0.098886 0.064 0.949210
DASS_D_sum 0.066481 0.084262 0.789 0.430955
DASS_A_sum -0.042479 0.100501 -0.423 0.672940
DASS_S_sum 0.071123 0.111754 0.636 0.525146
NEO_O_sum -0.017469 0.042814 -0.408 0.683656
NEO_C_sum 0.063907 0.063799 1.002 0.317566
NEO_E_sum -0.027207 0.045657 -0.596 0.551844
NEO_A_sum -0.028580 0.043089 -0.663 0.507830
NEO_N_sum 0.114849 0.043972 2.612 0.009612 **
RSES_sum 0.040356 0.062460 0.646 0.518865
SSEIT_sum 0.074793 0.021256 3.519 0.000525 ***
SRS_sum -0.048925 0.067296 -0.727 0.467981
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.705 on 225 degrees of freedom
Multiple R-squared: 0.1601, Adjusted R-squared: 0.1078
F-statistic: 3.064 on 14 and 225 DF, p-value: 0.0002304# summary(lm(data = SONA_data, formula = SAD_access_sum ~ age_first_phone + NEO_N_sum + SSEIT_sum))SAD - Emotional Attachement
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 3.32, p < .001, R2 = .17
sig predictors:
- DASS: S, t(225) = 2.28, p = .02
- NEO: A, t(225) = -2.85, p = .005
- SSEIT, t(225) = 3.23, p = .001
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-15.7542 -2.3363 -0.0732 3.0469 10.8366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.00631 6.66122 1.953 0.0521 .
age 0.04841 0.15046 0.322 0.7480
age_first_phone -0.08678 0.15645 -0.555 0.5796
BIS_sum -0.06023 0.11682 -0.516 0.6066
DASS_D_sum 0.06683 0.09954 0.671 0.5026
DASS_A_sum -0.09366 0.11872 -0.789 0.4310
DASS_S_sum 0.30752 0.13202 2.329 0.0207 *
NEO_O_sum -0.09670 0.05058 -1.912 0.0571 .
NEO_C_sum -0.02281 0.07537 -0.303 0.7625
NEO_E_sum 0.04814 0.05393 0.893 0.3731
NEO_A_sum -0.11539 0.05090 -2.267 0.0244 *
NEO_N_sum 0.05463 0.05194 1.052 0.2941
RSES_sum 0.02922 0.07378 0.396 0.6924
SSEIT_sum 0.08303 0.02511 3.306 0.0011 **
SRS_sum -0.02972 0.07950 -0.374 0.7089
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.376 on 225 degrees of freedom
Multiple R-squared: 0.1579, Adjusted R-squared: 0.1055
F-statistic: 3.013 on 14 and 225 DF, p-value: 0.0002864# summary(lm(data = SONA_data, formula = SAD_ea_sum ~ DASS_S_sum + NEO_A_sum + SSEIT_sum))SAD - Distractibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 225) = 3.18, p < .001, R2 = .17
sig predictors:
- SSEIT, t(225) = 3.85, p < .001
- SRS, t(225) = -2.23, p = .03
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = SONA_data)
Residuals:
Min 1Q Median 3Q Max
-6.4716 -1.3710 0.1538 1.6601 5.0845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.2447103 3.6878953 1.693 0.091782 .
age -0.1752607 0.0833006 -2.104 0.036493 *
age_first_phone 0.0815140 0.0866151 0.941 0.347659
BIS_sum 0.0525472 0.0646732 0.813 0.417362
DASS_D_sum -0.0563096 0.0551089 -1.022 0.307978
DASS_A_sum 0.0009866 0.0657290 0.015 0.988038
DASS_S_sum 0.1112816 0.0730892 1.523 0.129277
NEO_O_sum 0.0027741 0.0280013 0.099 0.921171
NEO_C_sum 0.0246781 0.0417255 0.591 0.554820
NEO_E_sum -0.0067688 0.0298603 -0.227 0.820876
NEO_A_sum -0.0304303 0.0281811 -1.080 0.281381
NEO_N_sum 0.0365654 0.0287586 1.271 0.204878
RSES_sum -0.0089838 0.0408496 -0.220 0.826129
SSEIT_sum 0.0542163 0.0139018 3.900 0.000127 ***
SRS_sum -0.1089385 0.0440130 -2.475 0.014056 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.423 on 225 degrees of freedom
Multiple R-squared: 0.1658, Adjusted R-squared: 0.1138
F-statistic: 3.193 on 14 and 225 DF, p-value: 0.000131# summary(lm(data = SONA_data, formula = SAD_dist_sum ~ SSEIT_sum + SRS_sum))Study 2: Mturk
NMPQ
This regression used the following formula: \(NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 29.67, p < .001, R2 = .46
sig predictors: = Age, t(490) = -2.35, p = .02
- DASS: A, t(490) = 1.96, p = .05 (almost sig)
- NEO: C, t(490) = 3.61, p < .001
- NEO: E, t(490) = 3.14, p = .002
- NEO: N, t(490) = 4.06, p < .001
- RSES, t(490) = -1.94, p = .05 (almost sig)
- SSEIT, t(490) = 5.49, p < .001
- SRS, t(490) = -3.30, p = .001
# run lm + give summary
summary(lm(data = Mturk_data, formula = NMPQ_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = NMPQ_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-70.628 -9.748 0.519 11.409 59.418
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.6748210 17.3419358 -0.443 0.658280
age -0.2328261 0.1023487 -2.275 0.023347 *
age_first_phone 0.0009087 0.0056139 0.162 0.871479
BIS_sum 0.1074193 0.3187736 0.337 0.736279
DASS_D_sum 0.4142423 0.3272598 1.266 0.206190
DASS_A_sum 0.7449242 0.3780030 1.971 0.049323 *
DASS_S_sum 0.5661355 0.4028700 1.405 0.160579
NEO_O_sum -0.1422795 0.1606707 -0.886 0.376302
NEO_C_sum 0.8589074 0.2355071 3.647 0.000294 ***
NEO_E_sum 0.4266682 0.1422300 3.000 0.002839 **
NEO_A_sum -0.0458534 0.1823915 -0.251 0.801610
NEO_N_sum 0.7008665 0.1648276 4.252 2.54e-05 ***
RSES_sum -0.2857500 0.1485641 -1.923 0.055007 .
SSEIT_sum 0.4492209 0.0812046 5.532 5.16e-08 ***
SRS_sum -0.8798925 0.2659553 -3.308 0.001007 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 20.26 on 490 degrees of freedom
Multiple R-squared: 0.4595, Adjusted R-squared: 0.4441
F-statistic: 29.76 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = NMPQ_sum ~ age + DASS_A_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))SAD - Overall
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 40.42, p < .001, R2 = .54
sig predictors:
- Age, t(490) = -2.66, p = .008
- DASS: A, t(490) = 3.12, p = .002
- NEO: O, t(490) = -2.17, p = .03
- NEO: C, t(490) = 2.76, p = .006
- NEO: E, t(490) = 3.31, p = .001
- NEO: N, t(490) = 3.83, p < .001
- SSEIT, t(490) = 7.03, p < .001
- SRS, t(490) = -6.12, p = .02
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-48.792 -6.394 0.260 7.554 30.752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.2036387 10.2430265 1.289 0.19799
age -0.1547390 0.0604523 -2.560 0.01078 *
age_first_phone 0.0007647 0.0033159 0.231 0.81771
BIS_sum -0.0739891 0.1882839 -0.393 0.69452
DASS_D_sum 0.2120045 0.1932962 1.097 0.27327
DASS_A_sum 0.6932326 0.2232678 3.105 0.00201 **
DASS_S_sum 0.1684391 0.2379554 0.708 0.47937
NEO_O_sum -0.1926720 0.0949002 -2.030 0.04287 *
NEO_C_sum 0.3926944 0.1391025 2.823 0.00495 **
NEO_E_sum 0.2628272 0.0840083 3.129 0.00186 **
NEO_A_sum 0.0303385 0.1077297 0.282 0.77836
NEO_N_sum 0.3979152 0.0973555 4.087 5.10e-05 ***
RSES_sum -0.1274448 0.0877495 -1.452 0.14704
SSEIT_sum 0.3391977 0.0479636 7.072 5.30e-12 ***
SRS_sum -0.9599181 0.1570867 -6.111 2.02e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.97 on 490 degrees of freedom
Multiple R-squared: 0.5359, Adjusted R-squared: 0.5227
F-statistic: 40.42 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = SAD_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Dependency
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 22.36, p < .001, R2 = .39
sig predictors:
- Age, t(490) = -2.18, p = .03
- DASS: A, t(490) = 2.53, p = .002
- NEO: O, t(490) = -2.13, p = .03
- NEO: C, t(490) = 2.71, p = .007
- NEO: E, t(490) = 2.64, p = .009
- NEO: N, t(490) = 3.06, p = .002
- SSEIT, t(490) = 3.99, p < .001
- SRS, t(490) = -3.78, p < .001
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-12.6146 -2.2688 0.2594 2.3092 10.9457
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.9461820 3.3401040 0.283 0.777082
age -0.0406804 0.0197126 -2.064 0.039575 *
age_first_phone 0.0002557 0.0010813 0.236 0.813152
BIS_sum -0.0065454 0.0613967 -0.107 0.915144
DASS_D_sum -0.0026446 0.0630311 -0.042 0.966550
DASS_A_sum 0.1809055 0.0728044 2.485 0.013295 *
DASS_S_sum 0.1142210 0.0775939 1.472 0.141653
NEO_O_sum -0.0601419 0.0309456 -1.943 0.052531 .
NEO_C_sum 0.1276934 0.0453593 2.815 0.005072 **
NEO_E_sum 0.0664906 0.0273939 2.427 0.015575 *
NEO_A_sum 0.0285640 0.0351291 0.813 0.416547
NEO_N_sum 0.1067656 0.0317462 3.363 0.000831 ***
RSES_sum -0.0362093 0.0286139 -1.265 0.206312
SSEIT_sum 0.0636552 0.0156402 4.070 5.48e-05 ***
SRS_sum -0.1931369 0.0512237 -3.770 0.000183 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.902 on 490 degrees of freedom
Multiple R-squared: 0.3889, Adjusted R-squared: 0.3714
F-statistic: 22.27 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = SAD_dep_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Accessibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 14.25, p < .001, R2 = .29
sig predictors:
- NEO: C, t(490) = 4.00, p < .001
- NEO: E, t(490) = 2.13, p = .03
- NEO: N, t(490) = 3.55, p < .001
- SSEIT, t(490) = 3.59, p < .001
- SRS, t(490) = -2.24, p = .03
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_access_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_access_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-13.2698 -1.9715 0.3715 2.4684 9.5193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.2480834 3.2292835 -1.006 0.314998
age -0.0265568 0.0190586 -1.393 0.164122
age_first_phone -0.0004764 0.0010454 -0.456 0.648814
BIS_sum 0.0384322 0.0593596 0.647 0.517646
DASS_D_sum 0.0653614 0.0609398 1.073 0.283999
DASS_A_sum 0.0567765 0.0703889 0.807 0.420282
DASS_S_sum 0.1009618 0.0750194 1.346 0.178986
NEO_O_sum -0.0124353 0.0299189 -0.416 0.677859
NEO_C_sum 0.1785834 0.0438544 4.072 5.43e-05 ***
NEO_E_sum 0.0498110 0.0264850 1.881 0.060602 .
NEO_A_sum 0.0170237 0.0339636 0.501 0.616430
NEO_N_sum 0.1141062 0.0306929 3.718 0.000224 ***
RSES_sum -0.0439682 0.0276645 -1.589 0.112629
SSEIT_sum 0.0558040 0.0151213 3.690 0.000249 ***
SRS_sum -0.1113305 0.0495242 -2.248 0.025020 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.773 on 490 degrees of freedom
Multiple R-squared: 0.2886, Adjusted R-squared: 0.2683
F-statistic: 14.2 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = SAD_access_sum ~ NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Emotional Attachement
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 39.93, p < .001, R2 = .53
sig predictors:
- Age, t(490) = -2.76, p = .01
- DASS: A, t(490) = 3.76, p < .001
- NEO: O, t(490) = -2.25, p = .03
- NEO: E, t(490) = 3.54, p < .001
- NEO: N, t(490) = 2.82, p = .005
- SSEIT, t(490) = 8.44, p < .001
- SRS, t(490) = -6.46, p < .001
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum +
DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum +
NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-15.4283 -2.2943 0.2534 2.3532 12.1142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.752770 3.355655 2.608 0.009375 **
age -0.054026 0.019804 -2.728 0.006601 **
age_first_phone 0.001016 0.001086 0.936 0.349976
BIS_sum -0.097511 0.061683 -1.581 0.114557
DASS_D_sum 0.100175 0.063325 1.582 0.114312
DASS_A_sum 0.281266 0.073143 3.845 0.000136 ***
DASS_S_sum -0.100372 0.077955 -1.288 0.198508
NEO_O_sum -0.070043 0.031090 -2.253 0.024705 *
NEO_C_sum 0.053255 0.045571 1.169 0.243125
NEO_E_sum 0.097013 0.027521 3.525 0.000463 ***
NEO_A_sum -0.043980 0.035293 -1.246 0.213300
NEO_N_sum 0.090616 0.031894 2.841 0.004682 **
RSES_sum -0.005434 0.028747 -0.189 0.850140
SSEIT_sum 0.131604 0.015713 8.375 5.83e-16 ***
SRS_sum -0.333465 0.051462 -6.480 2.24e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.92 on 490 degrees of freedom
Multiple R-squared: 0.5317, Adjusted R-squared: 0.5183
F-statistic: 39.74 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = SAD_ea_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))SAD - Distractibility
This regression used the following formula: \(SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS\)
Results indicate that:
over all model was sig F(14, 490) = 56.37, p < .001, R2 = .62
sig predictors:
- Age, t(490) = -2.03, p = .04
- DASS: A, t(490) = 3.37, p < .001
- NEO: O, t(490) = -2.95, p = .003
- NEO: E, t(490) = 3.33, p < .001
- SSEIT, t(490) = 6.68, p < .001
- SRS, t(490) = -9.33, p < .001
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
Call:
lm(formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum +
DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum +
NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum +
SRS_sum, data = Mturk_data)
Residuals:
Min 1Q Median 3Q Max
-7.8512 -1.3257 0.0772 1.4103 7.5089
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.819e+00 1.793e+00 4.919 1.19e-06 ***
age -2.187e-02 1.058e-02 -2.067 0.039290 *
age_first_phone -4.303e-05 5.803e-04 -0.074 0.940932
BIS_sum -1.830e-03 3.295e-02 -0.056 0.955748
DASS_D_sum 3.463e-02 3.383e-02 1.024 0.306553
DASS_A_sum 1.329e-01 3.908e-02 3.401 0.000727 ***
DASS_S_sum 2.842e-02 4.165e-02 0.682 0.495276
NEO_O_sum -4.946e-02 1.661e-02 -2.978 0.003048 **
NEO_C_sum -2.598e-02 2.435e-02 -1.067 0.286356
NEO_E_sum 5.000e-02 1.470e-02 3.400 0.000728 ***
NEO_A_sum 6.088e-03 1.886e-02 0.323 0.746925
NEO_N_sum 3.196e-02 1.704e-02 1.876 0.061270 .
RSES_sum -2.073e-02 1.536e-02 -1.350 0.177759
SSEIT_sum 5.546e-02 8.395e-03 6.607 1.03e-10 ***
SRS_sum -2.561e-01 2.749e-02 -9.314 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.094 on 490 degrees of freedom
Multiple R-squared: 0.6171, Adjusted R-squared: 0.6061
F-statistic: 56.4 on 14 and 490 DF, p-value: < 2.2e-16# summary(lm(data = Mturk_data, formula = SAD_dist_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_E_sum + SSEIT_sum + SRS_sum))---
title: "Is it me or my smartphone? Investigating predictors of nomophobia and smartphone dependency"
author: "Ana C. Ruiz Pardo and J.P. Minda"
date: 'October 29, 2021'
output:
  html_notebook:
    toc: yes
    toc_depth: '6'
    toc_float:
      collapsed: no
    theme: lumen
    code_folding: hide
  pdf_document:
    toc: yes
    toc_depth: '6'
  html_document:
    toc: yes
    toc_depth: '6'
    toc_float:
      collapsed: no
    theme: lumen
    code_folding: hide
editor_options: 
  markdown: 
    wrap: 72
---

```{=html}
<style type="text/css">

body{ /* Normal  */
      font-size: 15px;
      color: #414535;}
td {  /* Table  */
  font-size: 14px;}
h1.title {
  font-size: 40px;
  color: #98410B}
h1 { /* Header 1 */
  font-size: 35px;
  color: #08605F;}
h2 { /* Header 2 */
  font-size: 33px;
  color: #08605F;}
h3 { /* Header 3 */
  font-size: 31px;
  color: #08605F;}
h4 { /* Header 4 */
  font-size: 29px;
  color: #08605F;}
h5 { /* Header 5 */
  font-size: 27px;
  color: #08605F;}
h6 { /* Header 6 */
  font-size: 25px;
  color: #08605F;}

</style>
```
<div style="text-align: left">

```{r include=FALSE}
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```

# Introduction

This document outlines the analyses from Study 1 and 2 in **Is it me or
my smartphone? Investigating predictors of nomophobia and smartphone
dependency** (Ruiz Pardo & Minda, October 2021 preprint, \<\>). The
rising prevalence of smartphones has prompted research about how they
can impact cognitive abilities. Therefore, the purpose of this study was
to investigate what individual differences can predict people's
smartphone depenency and nomophobia. To do do, we examined a variety of
individual difference measures: impulsiveness, depression, stress,
anxiety, personality (big five), self-esteem, emotional intelligence,
and self regulation. Additionally, participant's gender, age, and age of
first smartphone were recorded.

Two studies were used to compare university students (study 1) to the
general population (study 2). Each study asked three key questions...
all related to how age may play a role in people's typical smartphone
use and whether or not individual differences were significant
predictors of smartphone dependency and nomophobia (i.e., cohort
differences).

Criterion measures (2):

-   Nomophobia (NMPQ; Yildirim and Correia 2015)

-   Smartphone Attachment and Dependency Inventory (SAD; Ward et al.,

    2017) 

Predictor measures (6):

-   Barratt Impulsiveness Scale-Brief (BIS-brief; Steinberg et al. 2013)

-   Depression, Anxiety, and Stress Scale (DASS-21; Lovibond and
    Lovibond 1995)

-   International Personality Item Pool NEO (IPIP-NEO-60; Maples-Keller
    et al. 2019)

-   Rosenberg Self-Esteem Scale (RSES). Cite: (Rosenberg 1965)

-   Schutte Self-Report Emotional Intelligence Test (SSEIT; Schutte et
    al. 1998)

-   Self-Regulation Scale (SRS; Schwarzer et al. 1999)

Demographic & Smartphone Use measure (1):

-   Smartphone use questionnaire (designed for the present study,
    revised from Ruiz Pardo & Minda, 2021)

## Procedure

LOI/consent \> Questionnaires in the same random assignment:
Demographics, SUQ, NMP-Q, SRS, SAD, DASS-21, BIS-Brief, SSEIT, RSES,
IPIP-NEO-60 \> debriefing \> UWO ethics \> approx 40 min in total.

## Hypotheses

All sig predictors of nomophobia

All sig predictors of smartphone dependency

# Analysis Prep

## Load Libraries

Before importing the raw data, the required libraries were loaded.

```{r load_libs, message=FALSE, warning=FALSE, include=FALSE}
# For plotting performance:

library(ggplot2)

# For running Levene's test:

library(car)

# For performing stats analyses like ANOVAs:

library(ez)

# For formatting tables:

library(kableExtra)
 # set options for kable tables for all future tables
  options(knitr.table.format = "html") 
  

# choose any colours you want, good to help with ppl w/ colour blindness

library(RColorBrewer)

# required for factor analysis
library(psych)


# Tidyverse for data analysis, cleaning, etc.
  # included packages lodaed with this: ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats
library(tidyverse)
  
# for correlations
library(Hmisc)

library(stats) # to perform t-tests

library(ggpubr) # to plot figures
  
library(effsize) # to calculate cohen's d effect size
library(coin) # to run Mann-Whitney U test
  
#install.packages(plyr)
  # library(plyr)

# For formatting ANOVA outputs:

#library(schoRsch)
  
# to plot quick correlation matrix
  library(corrplot)
  #library("PerformanceAnalytics")
  
# to show multiple grobs on page
library(gridExtra)


# rto plot violin plots  
  # install the package
  # install.packages("ggstatsplot")
  
  # Load the package
  library(ggstatsplot)
  
# library(plyr)
library(purrr)
  
# get effect size from white-corrected ANOVA
library(DescTools)
  
# export lm as html table
library(sjPlot)  ## use tab_model()
  
# to tidy lm results (make it a data frame)
library(broom)
```

```{r load_libs_2, message=FALSE, warning=FALSE, include=FALSE, eval=FALSE}
# For plotting performance:

library(ggplot2)

# For running Levene's test:

library(car)

# For performing stats analyses like ANOVAs:

library(ez)

# For formatting tables:

library(kableExtra)
 # set options for kable tables for all future tables
  options(knitr.table.format = "html") 
  

# choose any colours you want, good to help with ppl w/ colour blindness

library(RColorBrewer)

# required for factor analysis
library(psych)


# Tidyverse for data analysis, cleaning, etc.
  # included packages lodaed with this: ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats
library(tidyverse)
  
# for correlations
library(Hmisc)

library(stats) # to perform t-tests

library(ggpubr) # to plot figures
  
library(effsize) # to calculate cohen's d effect size
library(coin) # to run Mann-Whitney U test
  
#install.packages(plyr)
  # library(plyr)

# For formatting ANOVA outputs:

#library(schoRsch)
  
# to plot quick correlation matrix
  library(corrplot)
  #library("PerformanceAnalytics")
  
# to show multiple grobs on page
library(gridExtra)


# rto plot violin plots  
  # install the package
  # install.packages("ggstatsplot")
  
  # Load the package
  library(ggstatsplot)
  
# library(plyr)
library(purrr)
  
# get effect size from white-corrected ANOVA
library(DescTools)
  
# export lm as html table
library(sjPlot)  ## use tab_model()
```

## Additional Functions

### Rounding p-values

<details>

<summary>

This rounding function was adapted from [Dr. Emily Nielsen's
Rpubs](https://rpubs.com/egnielsen). The function ("**p_round(x)**") was
created to assess and print p-values. If $p \ge .005$, the function will
display "\$p = \$" and the value rounded to two decimal places. If \$
.0005 \le p \<.005$, the function will display “$p = \$" and the value
rounded to three decimal places. If $p < .0005$, the function will
display "$p < .001$". *(click to expand)*

</summary>

```{r create_p_round_function, eval=TRUE}

p_round <- function(x){
  if(x > .005)
    {x1 = (paste("= ", gsub("0\\.","\\.", round(x, digits = 2)), sep = ''))
  }  
  else if(x == .005){x1 = (paste("=", gsub("0\\.","\\.", 0.01)))
  }
  else if(x > .0005 & x < .005)
    {x1 = (paste("= ", gsub("0\\.","\\.", round(x, digits = 3)), sep = ''))
  }  
  else if(x == .0005){x1 = (paste("=", gsub("0\\.","\\.", 0.001)))
  }
  else{x1 = (paste("<", gsub("0\\.","\\.", 0.001)))
  } 
  (x1)
}

```

### Flatten Correlation Output

The following takes a correlation output and flattens it to a long
format. Originally from STHDA's "Correlation matrix : Formatting and
visualization"
(<http://www.sthda.com/english/wiki/correlation-matrix-formatting-and-visualization#.YW4IOfbtntM.link>).

```{r create_flattenCorr_function, eval=TRUE}
# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}
```

</details>

## Import Raw Data Files

There are **two** raw data files for the analyses.

**Study 1**

1.  Study 1 - Survey Data

    -   csv file exported from Qualtrics
    -   data collected online

**Study 2**

2.  Study 2 - Survey Data

    -   csv file exported from Qualtrics
    -   data collected in-person

***NOTE: To avoid confusion, the naming convention for the two studies
was as follows for all data, analyses, visualizations, etc.: Study 1 =
"SONA" and Study 2 = "Mturk"***

### 1. Study 1 Data: SONA

First, the raw data was imported.

```{r eval=TRUE}
# this will import the raw excel data file for study 1
  # this file has been anonymized, so any identifiable information has been removed
SONA_raw <- read.csv("SONA_survey_data(oct19)-final.csv", header = TRUE)
```

After importing the raw data, the file is cleaned by removing
participants based on several criteria:

-   **Testing Data**: experimenter data (i.e., testing prior to data
    collection), any irrelevant rows

-   **Incomplete Data**: participants who did not complete the study

    -   `r nrow(SONA_raw %>% filter(DistributionChannel != "test") %>% filter(Finished == 0))`
        did not finish

-   **No consent:** participants who did not consent to the study

    -   `r nrow(SONA_raw %>% filter(Q1.2 != 1))` did not consent

-   **Unnecessary Variables**: columns which are not relevant to the
    analyses (e.g., distribution type, distribution language)

```{r eval=TRUE}
# clean the data
SONA_raw_temp <- 
  # start with removing experimenter and irrelevant rows from the data
    # there is no need to count the ps removed at this stage 
  SONA_raw %>% 
  # remove row 1 & 2 -- not data
  slice(3:nrow(SONA_raw)) %>% 
  # remove testing data
  filter(DistributionChannel != "test") %>% 

  # next, remove ps w/ incomplete data -- include only thos who have finished (i.e., "1")
  filter(Finished == 1) %>% 
  
  # remove any ps who did not consent to the study
  filter(Q1.2 == 1) %>% 
  
  # remove any ps who did not report being at least 18 or older
  filter(!Q2.1 <17) %>% 
  
    # remove p w/ age "2"
    filter(!Q2.1 == 2) %>% 

  # remove unnecessary columns
    # should be done after others since columns used to filter
  select(-c(Status:IPAddress, ResponseId:ExternalReference, DistributionChannel:Q1.2, SC0))
```

Additionally, a "participant" column was added to denote the participant
ID, the columns were renamed for easier reference, any unclear or
inappropriate responses (e.g., non-numeric response for items requiring
a numeric response) were removed, and all variables were formatted as
numeric or factor as appropriate. Lastly, any questionnaire scores were
calculated.

```{r message=FALSE, warning=FALSE, eval=TRUE}
# add a participant column to data
SONA_data_temp <- 
  cbind(data.frame("participant" = c(1:nrow(SONA_raw_temp))), SONA_raw_temp)

SONA_data <- 
  # change data file type to tibble
  as.tibble(SONA_data_temp) %>% 
  # rename columns...
  dplyr::rename(duration_sec = Duration..in.seconds., age = Q2.1, gender = Q2.2, genderO = Q2.3, lang = Q2.4, langO = Q2.5, prof = Q2.6, live = Q2.7, liveO = Q2.8, edu = Q2.9, employ = Q2.10, employ_FT = Q2.10_1, employ_PT = Q2.10_2, employ_SE = Q2.10_3, employ_UE = Q2.10_4, employ_student = Q2.10_5, employ_retired = Q2.10_6, employ_unable = Q2.10_7, employ_PNTS = Q2.10_8, industry = Q2.11, industryO = Q2.12, industry_student = Q2.11_1, industry_goods = Q2.11_2, industry_utilities = Q2.11_3, industry_const = Q2.11_4, industry_manuf = Q2.11_5, industry_service = Q2.11_6, industry_finance_realtor = Q2.11_7, industry_tech = Q2.11_8, industry_business = Q2.11_9, industry_educ = Q2.11_10, industry_health = Q2.11_11, industry_info_culture = Q2.11_12, industry_accomod = Q2.11_13, industry_public_admin = Q2.11_14, industry_other = Q2.11_15, industry_DNA = Q2.11_16,
              # smartphone questions
                # frequency of use
                phone_for_work = Q2.13, specific_app = Q2.14, age_first_phone = Q3.1, app_most_used = Q3.2, app_most_usedO = Q3.3, app_most_used_overall = Q3.4, 
                
                # iphone questions -- ST
                iphone = Q4.1, ST = Q4.2, ST_app_most_used = Q4.4, ST_app_most_usedO = Q4.5, ST_app_text_mess = Q4.6, ST_app_most_used_overall = Q4.7, ST_weekly_tot_hours = Q4.8, ST_daily_pickups = Q4.9, ST_daily_not = Q4.10, 
              
                # comfort level qs
                com_gen = Q5.1_1, com_unattended = Q5.1_2, com_leave_with_others = Q5.1_3, com_locked = Q5.1_4, com_room_task = Q5.1_5, 
              
                #privacy qs
                passcode = Q6.1, passcode_others = Q6.2, passcode_relation = Q6.3, passcode_relation_parents = Q6.3_1, passcode_relation_other_fam = Q6.3_2, passcode_relation_sig_other = Q6.3_3, passcode_relation_friend = Q6.3_4, passcode_relation_work = Q6.3_5, passcode_relation_other = Q6.3_6, passcode_relation_DNA = Q6.3_7, passcode_relation_overall = Q6.4, other_use = Q6.5, other_room = Q6.6, 
              
                # distraction qs
                dist_daily = Q7.1_1, dist_study = Q7.1_2, dist_device = Q7.2, dist_deviceO = Q7.3, dist_device_studywork = Q7.4, dist_device_studyworkO = Q7.5, 
                dist_device_social = Q7.6, dist_device_socialO = Q7.7,
              
                # location & power qs
                pow_not_using = Q8.1_1, pow_notifications_on = Q8.1_2, pow_vibrate = Q8.1_3, pow_study = Q8.1_4, pow_exam = Q8.1_5, pow_lec = Q8.1_6, pow_work = Q8.1_7, pow_meeting = Q8.1_8, pow_sleep = Q8.1_9, 
                loc_typical = Q8.2, loc_study = Q8.3, loc_exam = Q8.4, loc_lec = Q8.5, loc_work = Q8.6, loc_meeting = Q8.7, loc_social = Q8.8,  
              
                # exploratory qs
                communicate = Q9.1, communicateO = Q9.2, phone_use = Q9.3, phone_useO = Q9.4, phone_value = Q9.5, phantom = Q9.6,  
              
              # Questionnaires
                # Nomophobia -- criterion - no subscales
                NMPQ_1 = Q10.1_1, NMPQ_2 = Q10.1_2, NMPQ_3 = Q10.1_3, NMPQ_4 = Q10.1_4, NMPQ_5 = Q10.1_5, NMPQ_6 = Q10.1_6, NMPQ_7 = Q10.1_7, NMPQ_8 = Q10.1_8, NMPQ_9 = Q10.1_9, NMPQ_10 = Q10.2_1, NMPQ_11 = Q10.2_2, NMPQ_12 = Q10.2_3, NMPQ_13 = Q10.2_4, NMPQ_14 = Q10.2_5, NMPQ_15 = Q10.2_6, NMPQ_16 = Q10.2_7, NMPQ_17 = Q10.2_8, NMPQ_18 = Q10.2_9, NMPQ_19 = Q10.2_10, NMPQ_20 = Q10.2_11, 
                
                # SRS -- predictor - no subscales
                # rev coded = 5, 7, 9
                SRS_1 = Q11.1_1, SRS_2 = Q11.1_2, SRS_3 = Q11.1_3, SRS_4 = Q11.1_4, SRS_5 = Q11.1_5, SRS_6 = Q11.1_6, SRS_7 = Q11.1_7, SRS_8 = Q11.1_8, SRS_9 = Q11.1_9, SRS_10 = Q11.1_10, 
              
                # SAD -- predictor - 4 subscales based on Ruiz Pardo & Minda, 2021
                # dependence (1-3), accessibility (5-7), emotional attachment (8-11), distractibility (12-13)
                SAD_dep_1 = Q12.1_1, SAD_dep_2 = Q12.1_2, SAD_dep_3 = Q12.1_3, SAD_4 = Q12.1_4, SAD_access_5 = Q12.1_5, SAD_access_6 = Q12.1_6, SAD_access_7 = Q12.1_7, SAD_ea_8 = Q12.1_8, SAD_ea_9 = Q12.1_9, SAD_ea_10 = Q12.1_10, SAD_ea_11 = Q12.1_11, SAD_dist_12 = Q12.1_12, SAD_dist_13 = Q12.1_13, 
              
                # DASS-21 -- predictor - 3 subscales 
                # depression (3, 5, 10, 13, 16, 17, 21), anxiety (2, 4, 7, 9, 15, 19, 20), stress (1, 6, 8, 11, 12, 14, 18)
                DASS_D_3 = Q13.1_3, DASS_D_5 = Q13.1_5, DASS_D_10 = Q13.1_10, DASS_D_13 = Q13.1_13, DASS_D_16 = Q13.1_16, DASS_D_17 = Q13.1_17, DASS_D_21 = Q13.1_21, DASS_A_2 = Q13.1_2, DASS_A_4 = Q13.1_4, DASS_A_7 = Q13.1_7, DASS_A_9 = Q13.1_9, DASS_A_15 = Q13.1_15, DASS_A_19 = Q13.1_19, DASS_A_20 = Q13.1_20, DASS_S_1 = Q13.1_1, DASS_S_6 = Q13.1_6, DASS_S_8 = Q13.1_8, DASS_S_11 = Q13.1_11, DASS_S_12 = Q13.1_12, DASS_S_14 = Q13.1_14, DASS_S_18 = Q13.1_18,
              
                # BIS-brief -- predictor - no subscales
                # rev coded = 1, 4, 5, 6
                BIS_1 = Q14.1_1, BIS_2 = Q14.1_2, BIS_3 = Q14.1_3, BIS_4 = Q14.1_4, BIS_5 = Q14.1_5, BIS_6 = Q14.1_6, BIS_7 = Q14.1_7, BIS_8 = Q14.1_8,
              
                # SSEIT - predictor - no subscales
                # rev coded = 5, 28, 33
                SSEIT_1 = Q15.1_1, SSEIT_2 = Q15.1_2, SSEIT_3 = Q15.1_3, SSEIT_4 = Q15.1_4, SSEIT_5 = Q15.1_5, SSEIT_6 = Q15.1_6, SSEIT_7 = Q15.1_7, SSEIT_8 = Q15.1_8, SSEIT_9 = Q15.1_9, SSEIT_10 = Q15.1_10, SSEIT_11 = Q15.1_11, SSEIT_12 = Q15.1_12, SSEIT_13 = Q15.1_13, SSEIT_14 = Q15.1_14, SSEIT_15 = Q15.1_15, SSEIT_16 = Q15.1_16, SSEIT_17 = Q15.1_17, SSEIT_18 = Q15.1_18, SSEIT_19 = Q15.1_19, SSEIT_20 = Q15.1_20, SSEIT_21 = Q15.1_21, SSEIT_22 = Q15.1_22, SSEIT_23 = Q15.1_23, SSEIT_24 = Q15.1_24, SSEIT_25 = Q15.1_25, SSEIT_26 = Q15.1_26, SSEIT_27 = Q15.1_27, SSEIT_28 = Q15.1_28, SSEIT_29 = Q15.1_29, SSEIT_30 = Q15.1_30, SSEIT_31 = Q15.1_31, SSEIT_32 = Q15.1_32, SSEIT_33 = Q15.1_33,
              
                # RSES - predictor - no subscales
                # rev coded = 3, 5, 8, 9, 10
                RSES_1 = Q16.1_1, RSES_2 = Q16.1_2, RSES_3 = Q16.1_3, RSES_4 = Q16.1_4, RSES_5 = Q16.1_5, RSES_6 = Q16.1_6, RSES_7 = Q16.1_7, RSES_8 = Q16.1_8, RSES_9 = Q16.1_9, RSES_10 = Q16.1_10,
                
                # IPIP-NEO-60 - predictor - 5 subscales
                # Neuroticism (1-12), Extraversion (13-24), Openness (25-36), Agreeableness (37-48), Conscientiousness (49-60)
                # rev coded = 9-12; 16; 28, 30-34m 36; 39, 40, 43-46; 52, 58-60
                NEO_N_1 = Q17.1_1, NEO_N_2 = Q17.1_2, NEO_N_3 = Q17.1_3, NEO_N_4 = Q17.1_4, NEO_N_5 = Q17.1_5, NEO_N_6 = Q17.1_6, NEO_N_7 = Q17.1_7, NEO_N_8 = Q17.1_8, NEO_N_9 = Q17.1_9, NEO_N_10 = Q17.1_10, NEO_N_11 = Q17.1_11, NEO_N_12 = Q17.1_12, NEO_E_13 = Q17.1_13, NEO_E_14 = Q17.1_14, NEO_E_15 = Q17.1_15, NEO_E_16 = Q17.1_16, NEO_E_17 = Q17.1_17, NEO_E_18 = Q17.1_18, NEO_E_19 = Q17.1_19, NEO_E_20 = Q17.1_20, NEO_E_21 = Q17.1_21, NEO_E_22 = Q17.1_22, NEO_E_23 = Q17.1_23, NEO_E_24 = Q17.1_24, NEO_O_25 = Q17.1_25, NEO_O_26 = Q17.1_26, NEO_O_27 = Q17.1_27, NEO_O_28 = Q17.1_28, NEO_O_29 = Q17.1_29, NEO_O_30 = Q17.1_30, NEO_O_31 = Q17.1_31, NEO_O_32 = Q17.1_32, NEO_O_33 = Q17.1_33, NEO_O_34 = Q17.1_34, NEO_O_35 = Q17.1_35, NEO_O_36 = Q17.1_36, NEO_A_37 = Q17.1_37, NEO_A_38 = Q17.1_38, NEO_A_39 = Q17.1_39, NEO_A_40 = Q17.1_40, NEO_A_41 = Q17.1_41, NEO_A_42 = Q17.1_42, NEO_A_43 = Q17.1_43, NEO_A_44 = Q17.1_44, NEO_A_45 = Q17.1_45, NEO_A_46 = Q17.1_46, NEO_A_47 = Q17.1_47, NEO_A_48 = Q17.1_48, NEO_C_49 = Q17.1_49, NEO_C_50 = Q17.1_50, NEO_C_51 = Q17.1_51, NEO_C_52 = Q17.1_52, NEO_C_53 = Q17.1_53, NEO_C_54 = Q17.1_54, NEO_C_55 = Q17.1_55, NEO_C_56 = Q17.1_56, NEO_C_57 = Q17.1_57, NEO_C_58 = Q17.1_58, NEO_C_59 = Q17.1_59, NEO_C_60 = Q17.1_60
                ) %>% 
  
# replace unclear/inappropriate responses
  # remove non-numeric responses to "age_first_phone"
    # this will force all non-numeric value to "NA"
  mutate(age_first_phone = as.numeric(age_first_phone)) %>% 
  
  # change variables to numeric or factor as needed 
  # demographic qs
  mutate(age = as.numeric(age), age = as.numeric(age), gender = factor(gender, levels = c(1:4), labels = c("Male", "Female", "Other", "Prefer not to say")), lang = factor(lang, levels = c(1:2), labels = c("English", "Other")), prof = factor(prof, levels = c(1:3), labels = c("Low", "Moderate", "High")), live = factor(live, levels = c(1:11), labels = c("North America", "Central America", "South America", "Europe", "Africa", "Asia", "Australia", "Pacific Islander", "Caribbean Islands", "Other", "Prefer not to say")), edu = factor(edu, levels = c(1:9), labels = c("some high school", "high school or equivalent", "some college, no degree", "associate degree", "bachelor’s degree", "master’s degree", "professional degree", "doctorate or higher", "Prefer not to say")), 
         # employ = factor(employ, levels = c(1:8), labels = c("full-time", "patt-time", "self-employed", "unemployed", "student", "retired", "unable to work", "Prefer not to say")), industry = factor(industry, levels = c(1:16), labels = c("student", "goods-producing sector", "utilities", "construction", "manufacturing", "services-producing sector", "finance, insurance, real estate, rental and leasing", "professional, scientific and technical services", "business, building and other support services", "educational services", "health care and social assistance", "information, culture and recreation", "accommodation and food services", "public administration", "other", "does not apply to me")), 

  # phone use -- general         
phone_for_work = factor(phone_for_work, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), specific_app = factor(specific_app, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), app_most_used = factor(app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), 

  # phone use -- iphone / ST
iphone = factor(iphone, levels = c(1:2), labels = c("yes", "no")), ST = factor(ST, levels = c(1:3), labels = c("had ST", "ST new", "no ST")), ST_app_most_used = factor(ST_app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), ST_app_text_mess = as.numeric(ST_app_text_mess), ST_weekly_tot_hours = as.numeric(ST_weekly_tot_hours),  ST_daily_pickups = as.numeric(ST_daily_pickups), ST_daily_not = as.numeric(ST_daily_not), 

  # comfort level qs
com_gen = as.numeric(com_gen), com_unattended = as.numeric(com_unattended), com_leave_with_others = as.numeric(com_leave_with_others), com_locked = as.numeric(com_locked), com_room_task = as.numeric(com_room_task),
  # privacy qs
passcode = factor(passcode, levels = c(1:2), labels = c("yes", "no")), passcode_others = factor(passcode_others, levels = c(1:2), labels = c("yes", "no")), passcode_relation = factor(passcode_relation, levels = c(1:7), labels = c("parents", "other family", "significant other", "friends", "co-worker / supervisor", "other", "DNA")), other_use = factor(other_use, levels = c(1:4), labels = c("Yes, only in my presence.", "Yes, depending on the individual and only in my presence.", "Yes, depending on the individual and does not need to be in my presence.", "No")), other_room = factor(other_room, levels = c(1:2), labels = c("yes", "no")), 

  # dustraction qs
dist_daily = as.numeric(dist_daily), dist_study = as.numeric(dist_study), dist_device = factor(dist_device, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_studywork = factor(dist_device_studywork, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_social = factor(dist_device_social, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), 

  # location & power questions
pow_not_using = as.numeric(pow_not_using), pow_notifications_on = as.numeric(pow_notifications_on), pow_vibrate = as.numeric(pow_vibrate), pow_study = as.numeric(pow_study), pow_exam = as.numeric(pow_exam), pow_lec = as.numeric(pow_lec), pow_work = as.numeric(pow_work), pow_meeting = as.numeric(pow_meeting), pow_sleep = as.numeric(pow_sleep), loc_typical = factor(loc_typical, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_study = factor(loc_study, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_exam = factor(loc_exam, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_lec = factor(loc_lec, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_work = factor(loc_work, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_meeting = factor(loc_meeting, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_social = factor(loc_social, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), 

  # exploratory qs
communicate = factor(communicate, levels = c(1:4), labels = c("Family", "Friends", "Work", "Other")), phone_use = factor(phone_use, levels = c(1:5), labels = c("Calling/Texting", "Social Media", "Games", "Email", "Other")), phone_value = factor(phone_value, levels = c(1:4), labels = c("$0-$20", "$21-$40", "$41-$60", ">$60")), phantom = factor(phantom, levels = c(1:2), labels = c("yes", "no")), 
         ) %>% 
  mutate_at(vars(starts_with("employ_")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("industry_")),funs(as.numeric)) %>% 
  
  mutate_at(vars(starts_with("NMPQ")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SRS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SAD")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("DASS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("BIS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SSEIT")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("RSES")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("NEO")),funs(as.numeric)) %>% 
  
  # reverse code items...
    # this REPLACES original items with new reverse-coded values
    # the subtraction is made based on the likert scale
      # so if the scale is 1-4, then it should be 5-score
  # for SRS: 5, 7, 9
  mutate(SRS_5 = 5-SRS_5) %>% 
  mutate(SRS_7 = 5-SRS_7) %>% 
  mutate(SRS_9 = 5-SRS_9) %>% 
  # for BIS: 1, 4, 5, 6
  mutate(BIS_1 = 5-BIS_1) %>% 
  mutate(BIS_4 = 5-BIS_4) %>% 
  mutate(BIS_5 = 5-BIS_5) %>% 
  mutate(BIS_6 = 5-BIS_6) %>% 
  # for SSEIT: 5, 28, 33
  mutate(SSEIT_5 = 6-SSEIT_5) %>% 
  mutate(SSEIT_28 = 6-SSEIT_28) %>% 
  mutate(SSEIT_33 = 6-SSEIT_33) %>% 
  # for RSES: 3, 5, 8, 9, 10
  mutate(RSES_3 = 5-RSES_3) %>% 
  mutate(RSES_5 = 5-RSES_5) %>% 
  mutate(RSES_8 = 5-RSES_8) %>% 
  mutate(RSES_9 = 5-RSES_9) %>% 
  mutate(RSES_10 = 5-RSES_10) %>% 
  # for NEO: 9-12; 16; 28, 30-34, 36; 39, 40, 43-46; 52, 58-60
  mutate(NEO_N_9 = 6-NEO_N_9) %>% 
  mutate(NEO_N_10 = 6-NEO_N_10) %>% 
  mutate(NEO_N_11 = 6-NEO_N_11) %>% 
  mutate(NEO_N_12 = 6-NEO_N_12) %>% 
  
  mutate(NEO_E_16 = 6-NEO_E_16) %>% 
  
  mutate(NEO_O_28 = 6-NEO_O_28) %>% 
  mutate(NEO_O_30 = 6-NEO_O_30) %>% 
  mutate(NEO_O_31 = 6-NEO_O_31) %>% 
  mutate(NEO_O_32 = 6-NEO_O_32) %>% 
  mutate(NEO_O_33 = 6-NEO_O_33) %>% 
  mutate(NEO_O_34 = 6-NEO_O_34) %>% 
  mutate(NEO_O_36 = 6-NEO_O_36) %>% 
  
  mutate(NEO_A_39 = 6-NEO_A_39) %>% 
  mutate(NEO_A_40 = 6-NEO_A_40) %>% 
  mutate(NEO_A_43 = 6-NEO_A_43) %>% 
  mutate(NEO_A_44 = 6-NEO_A_44) %>% 
  mutate(NEO_A_45 = 6-NEO_A_45) %>% 
  mutate(NEO_A_46 = 6-NEO_A_46) %>% 
  
  mutate(NEO_C_52 = 6-NEO_C_52) %>% 
  mutate(NEO_C_58 = 6-NEO_C_58) %>% 
  mutate(NEO_C_59 = 6-NEO_C_59) %>% 
  mutate(NEO_C_60 = 6-NEO_C_60) %>% 
  
  # add scores for each questionnaire... rowwise() %>% dplyr::mutate(SAD_score = sum(c_across(SAD_1:SAD_13)))
  # for each p....
  # get NMPQ score -- SUM
  rowwise() %>% dplyr::mutate(NMPQ_sum = sum(c_across(NMPQ_1:NMPQ_20))) %>% 
  
  # get SRS score -- SUM
  rowwise() %>% dplyr::mutate(SRS_sum = sum(c_across(SRS_1:SRS_10))) %>% 
  
  # get SAD score -- SUM
    # overall (1-13)
    rowwise() %>% dplyr::mutate(SAD_sum = sum(c_across(starts_with("SAD")))) %>%
    # dependency (1-3)
    rowwise() %>% dplyr::mutate(SAD_dep_sum = sum(c_across(SAD_dep_1:SAD_dep_3))) %>%
    # accessibility (5-7)
    rowwise() %>% dplyr::mutate(SAD_access_sum = sum(c_across(SAD_access_5:SAD_access_7))) %>%
    # emotional attachment (8-11)
    rowwise() %>% dplyr::mutate(SAD_ea_sum = sum(c_across(SAD_ea_8:SAD_ea_11))) %>%
    # distractibility (12-13)
    rowwise() %>% dplyr::mutate(SAD_dist_sum = sum(c_across(SAD_dist_12:SAD_dist_13))) %>%
  
  # get DASS score -- SUM
    # depression (3, 5, 10, 13, 16, 17, 21)
    rowwise() %>% dplyr::mutate(DASS_D_sum = sum(c_across(starts_with("DASS_D")))) %>% 
    # anxiety (2, 4, 7, 9, 15, 19, 20)
    rowwise() %>% dplyr::mutate(DASS_A_sum = sum(c_across(starts_with("DASS_A")))) %>% 
    # stress (1, 6, 8, 11, 12, 14, 18)
    rowwise() %>% dplyr::mutate(DASS_S_sum = sum(c_across(starts_with("DASS_S")))) %>% 
  
  
  # get BIS score -- SUM
  rowwise() %>% dplyr::mutate(BIS_sum = sum(c_across(starts_with("BIS")))) %>% 
  
  # get SSEIT score -- SUM
  rowwise() %>% dplyr::mutate(SSEIT_sum = sum(c_across(starts_with("SSEIT")))) %>% 
  
  # get RSES score -- SUM
  rowwise() %>% dplyr::mutate(RSES_sum = sum(c_across(starts_with("RSES")))) %>% 
  
  # get NEO score -- SUM
    # Neuroticism (1-12) 
    rowwise() %>% dplyr::mutate(NEO_N_sum = sum(c_across(starts_with("NEO_N")))) %>% 
    # Extraversion (13-24)
    rowwise() %>% dplyr::mutate(NEO_E_sum = sum(c_across(starts_with("NEO_E")))) %>% 
    # Openness (25-36)
    rowwise() %>% dplyr::mutate(NEO_O_sum = sum(c_across(starts_with("NEO_O")))) %>% 
    # Agreeableness (37-48)
    rowwise() %>% dplyr::mutate(NEO_A_sum = sum(c_across(starts_with("NEO_A")))) %>% 
    # Conscientiousness (49-60)
    rowwise() %>% dplyr::mutate(NEO_C_sum = sum(c_across(starts_with("NEO_C"))))
  
  # example to get measn: get NMPQ score -- MEAN
  # rowwise() %>% dplyr::mutate(mean(c_across(NMPQ_1:NMPQ_20))) %>% 

# export final data file
  write.csv(SONA_data,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/SONA_data(oct28).csv", row.names = FALSE)
```

### 2. Study 2 Data: Mturk

First, the raw data was imported.

```{r eval=TRUE}
# this will import the raw excel data file for study 1
  # this file has been anonymized, so any identifiable information has been removed
Mturk_raw <- read.csv("Mturk_survey_data(oct19)-final.csv", header = TRUE)
```

After importing the raw data, the file is cleaned by removing
participants based on several criteria:

-   **Testing Data**: experimenter data (i.e., testing prior to data
    collection), any irrelevant rows

-   **Incomplete Data**: participants who did not complete the study

    -   `r nrow(Mturk_raw %>% filter(DistributionChannel != "test") %>% filter(Finished == 0))`
        did not finish

-   **No consent:** participants who did not consent to the study

    -   `r nrow(Mturk_raw %>% filter(Q1.2 != 1))` did not consent

-   **Unnecessary Variables**: columns which are not relevant to the
    analyses (e.g., distribution type, distribution language)

```{r eval=TRUE}
# clean the data
Mturk_raw_temp <- 
  # start with removing experimenter and irrelevant rows from the data
    # there is no need to count the ps removed at this stage 
  Mturk_raw %>% 
  # remove row 1 & 2 -- not data
  slice(3:nrow(Mturk_raw)) %>% 
  # remove testing data
  filter(DistributionChannel != "test") %>% 

  # next, remove ps w/ incomplete data -- include only thos who have finished (i.e., "1")
  filter(Finished == 1) %>% 
  
  # remove any ps who did not consent to the study
  filter(Q1.2 == 1) %>% 
  
  # remove any ps who did not report being at least 18 or older
  filter(Q2.1 >17) %>% 

  # remove unnecessary columns
    # should be done after others since columns used to filter
  select(-c(Status:IPAddress, ResponseId:ExternalReference, DistributionChannel:Q1.2, SC0))
```

Additionally, a "participant" column was added to denote the participant
ID, the columns were renamed for easier reference, any unclear or
inappropriate responses (e.g., non-numeric response for items requiring
a numeric response) were removed, and all variables were formatted as
numeric or factor as appropriate. Lastly, any questionnaire scores were
calculated.

```{r message=FALSE, warning=FALSE, eval=TRUE}
# add a participant column to data
Mturk_data_temp <- 
  cbind(data.frame("participant" = c(1:nrow(Mturk_raw_temp))), Mturk_raw_temp)

Mturk_data <- 
  # change data file type to tibble 
  as.tibble(Mturk_data_temp) %>% 
  # rename columns...
  dplyr::rename(duration_sec = Duration..in.seconds., age = Q2.1, gender = Q2.2, genderO = Q2.3, lang = Q2.4, langO = Q2.5, prof = Q2.6, live = Q2.7, liveO = Q2.8, edu = Q2.9, employ = Q2.10, employ_FT = Q2.10_1, employ_PT = Q2.10_2, employ_SE = Q2.10_3, employ_UE = Q2.10_4, employ_student = Q2.10_5, employ_retired = Q2.10_6, employ_unable = Q2.10_7, employ_PNTS = Q2.10_8, industry = Q2.11, industryO = Q2.12, industry_student = Q2.11_1, industry_goods = Q2.11_2, industry_utilities = Q2.11_3, industry_const = Q2.11_4, industry_manuf = Q2.11_5, industry_service = Q2.11_6, industry_finance_realtor = Q2.11_7, industry_tech = Q2.11_8, industry_business = Q2.11_9, industry_educ = Q2.11_10, industry_health = Q2.11_11, industry_info_culture = Q2.11_12, industry_accomod = Q2.11_13, industry_public_admin = Q2.11_14, industry_other = Q2.11_15, industry_DNA = Q2.11_16,
              # smartphone questions
                # frequency of use
                phone_for_work = Q2.13, specific_app = Q2.14, age_first_phone = Q3.1, app_most_used = Q3.2, app_most_usedO = Q3.3, app_most_used_overall = Q3.4, 
                
                # iphone questions -- ST
                iphone = Q4.1, ST = Q4.2, ST_app_most_used = Q4.4, ST_app_most_usedO = Q4.5, ST_app_text_mess = Q4.6, ST_app_most_used_overall = Q4.7, ST_weekly_tot_hours = Q4.8, ST_daily_pickups = Q4.9, ST_daily_not = Q4.10, 
              
                # comfort level qs
                com_gen = Q5.1_1, com_unattended = Q5.1_2, com_leave_with_others = Q5.1_3, com_locked = Q5.1_4, com_room_task = Q5.1_5, 
              
                #privacy qs
                passcode = Q6.1, passcode_others = Q6.2, passcode_relation = Q6.3, passcode_relation_parents = Q6.3_1, passcode_relation_other_fam = Q6.3_2, passcode_relation_sig_other = Q6.3_3, passcode_relation_friend = Q6.3_4, passcode_relation_work = Q6.3_5, passcode_relation_other = Q6.3_6, passcode_relation_DNA = Q6.3_7, passcode_relation_overall = Q6.4, other_use = Q6.5, other_room = Q6.6,  
              
                # distraction qs
                dist_daily = Q7.1_1, dist_study = Q7.1_2, dist_device = Q7.2, dist_deviceO = Q7.3, dist_device_studywork = Q7.4, dist_device_studyworkO = Q7.5, 
                dist_device_social = Q7.6, dist_device_socialO = Q7.7,
              
                # location & power qs
                pow_not_using = Q8.1_1, pow_notifications_on = Q8.1_2, pow_vibrate = Q8.1_3, pow_study = Q8.1_4, pow_exam = Q8.1_5, pow_lec = Q8.1_6, pow_work = Q8.1_7, pow_meeting = Q8.1_8, pow_sleep = Q8.1_9, 
                loc_typical = Q8.2, loc_study = Q8.3, loc_exam = Q8.4, loc_lec = Q8.5, loc_work = Q8.6, loc_meeting = Q8.7, loc_social = Q8.8,  
              
                # exploratory qs
                communicate = Q9.1, communicateO = Q9.2, phone_use = Q9.3, phone_useO = Q9.4, phone_value = Q9.5, phantom = Q9.6,  
              
              # Questionnaires
                # Nomophobia -- criterion - no subscales
                NMPQ_1 = Q10.1_1, NMPQ_2 = Q10.1_2, NMPQ_3 = Q10.1_3, NMPQ_4 = Q10.1_4, NMPQ_5 = Q10.1_5, NMPQ_6 = Q10.1_6, NMPQ_7 = Q10.1_7, NMPQ_8 = Q10.1_8, NMPQ_9 = Q10.1_9, NMPQ_10 = Q10.2_1, NMPQ_11 = Q10.2_2, NMPQ_12 = Q10.2_3, NMPQ_13 = Q10.2_4, NMPQ_14 = Q10.2_5, NMPQ_15 = Q10.2_6, NMPQ_16 = Q10.2_7, NMPQ_17 = Q10.2_8, NMPQ_18 = Q10.2_9, NMPQ_19 = Q10.2_10, NMPQ_20 = Q10.2_11, 
                
                # SRS -- predictor - no subscales
                # rev coded = 5, 7, 9
                SRS_1 = Q11.1_1, SRS_2 = Q11.1_2, SRS_3 = Q11.1_3, SRS_4 = Q11.1_4, SRS_5 = Q11.1_5, SRS_6 = Q11.1_6, SRS_7 = Q11.1_7, SRS_8 = Q11.1_8, SRS_9 = Q11.1_9, SRS_10 = Q11.1_10, 
              
                # SAD -- predictor - 4 subscales based on Ruiz Pardo & Minda, 2021
                # dependence (1-3), accessibility (5-7), emotional attachment (8-11), distractibility (12-13)
                SAD_dep_1 = Q12.1_1, SAD_dep_2 = Q12.1_2, SAD_dep_3 = Q12.1_3, SAD_4 = Q12.1_4, SAD_access_5 = Q12.1_5, SAD_access_6 = Q12.1_6, SAD_access_7 = Q12.1_7, SAD_ea_8 = Q12.1_8, SAD_ea_9 = Q12.1_9, SAD_ea_10 = Q12.1_10, SAD_ea_11 = Q12.1_11, SAD_dist_12 = Q12.1_12, SAD_dist_13 = Q12.1_13, 
              
                # DASS-21 -- predictor - 3 subscales 
                # depression (3, 5, 10, 13, 16, 17, 21), anxiety (2, 4, 7, 9, 15, 19, 20), stress (1, 6, 8, 11, 12, 14, 18)
                DASS_D_3 = Q13.1_3, DASS_D_5 = Q13.1_5, DASS_D_10 = Q13.1_10, DASS_D_13 = Q13.1_13, DASS_D_16 = Q13.1_16, DASS_D_17 = Q13.1_17, DASS_D_21 = Q13.1_21, DASS_A_2 = Q13.1_2, DASS_A_4 = Q13.1_4, DASS_A_7 = Q13.1_7, DASS_A_9 = Q13.1_9, DASS_A_15 = Q13.1_15, DASS_A_19 = Q13.1_19, DASS_A_20 = Q13.1_20, DASS_S_1 = Q13.1_1, DASS_S_6 = Q13.1_6, DASS_S_8 = Q13.1_8, DASS_S_11 = Q13.1_11, DASS_S_12 = Q13.1_12, DASS_S_14 = Q13.1_14, DASS_S_18 = Q13.1_18,
              
                # BIS-brief -- predictor - no subscales
                # rev coded = 1, 4, 5, 6
                BIS_1 = Q14.1_1, BIS_2 = Q14.1_2, BIS_3 = Q14.1_3, BIS_4 = Q14.1_4, BIS_5 = Q14.1_5, BIS_6 = Q14.1_6, BIS_7 = Q14.1_7, BIS_8 = Q14.1_8,
              
                # SSEIT - predictor - no subscales
                # rev coded = 5, 28, 33
                SSEIT_1 = Q15.1_1, SSEIT_2 = Q15.1_2, SSEIT_3 = Q15.1_3, SSEIT_4 = Q15.1_4, SSEIT_5 = Q15.1_5, SSEIT_6 = Q15.1_6, SSEIT_7 = Q15.1_7, SSEIT_8 = Q15.1_8, SSEIT_9 = Q15.1_9, SSEIT_10 = Q15.1_10, SSEIT_11 = Q15.1_11, SSEIT_12 = Q15.1_12, SSEIT_13 = Q15.1_13, SSEIT_14 = Q15.1_14, SSEIT_15 = Q15.1_15, SSEIT_16 = Q15.1_16, SSEIT_17 = Q15.1_17, SSEIT_18 = Q15.1_18, SSEIT_19 = Q15.1_19, SSEIT_20 = Q15.1_20, SSEIT_21 = Q15.1_21, SSEIT_22 = Q15.1_22, SSEIT_23 = Q15.1_23, SSEIT_24 = Q15.1_24, SSEIT_25 = Q15.1_25, SSEIT_26 = Q15.1_26, SSEIT_27 = Q15.1_27, SSEIT_28 = Q15.1_28, SSEIT_29 = Q15.1_29, SSEIT_30 = Q15.1_30, SSEIT_31 = Q15.1_31, SSEIT_32 = Q15.1_32, SSEIT_33 = Q15.1_33,
              
                # RSES - predictor - no subscales
                # rev coded = 3, 5, 8, 9, 10
                RSES_1 = Q16.1_1, RSES_2 = Q16.1_2, RSES_3 = Q16.1_3, RSES_4 = Q16.1_4, RSES_5 = Q16.1_5, RSES_6 = Q16.1_6, RSES_7 = Q16.1_7, RSES_8 = Q16.1_8, RSES_9 = Q16.1_9, RSES_10 = Q16.1_10,
                
                # IPIP-NEO-60 - predictor - 5 subscales
                # Neuroticism (1-12), Extraversion (13-24), Openness (25-36), Agreeableness (37-48), Conscientiousness (49-60)
                # rev coded = 9-12; 16; 28, 30-34m 36; 39, 40, 43-46; 52, 58-60
                NEO_N_1 = Q17.1_1, NEO_N_2 = Q17.1_2, NEO_N_3 = Q17.1_3, NEO_N_4 = Q17.1_4, NEO_N_5 = Q17.1_5, NEO_N_6 = Q17.1_6, NEO_N_7 = Q17.1_7, NEO_N_8 = Q17.1_8, NEO_N_9 = Q17.1_9, NEO_N_10 = Q17.1_10, NEO_N_11 = Q17.1_11, NEO_N_12 = Q17.1_12, NEO_E_13 = Q17.1_13, NEO_E_14 = Q17.1_14, NEO_E_15 = Q17.1_15, NEO_E_16 = Q17.1_16, NEO_E_17 = Q17.1_17, NEO_E_18 = Q17.1_18, NEO_E_19 = Q17.1_19, NEO_E_20 = Q17.1_20, NEO_E_21 = Q17.1_21, NEO_E_22 = Q17.1_22, NEO_E_23 = Q17.1_23, NEO_E_24 = Q17.1_24, NEO_O_25 = Q17.1_25, NEO_O_26 = Q17.1_26, NEO_O_27 = Q17.1_27, NEO_O_28 = Q17.1_28, NEO_O_29 = Q17.1_29, NEO_O_30 = Q17.1_30, NEO_O_31 = Q17.1_31, NEO_O_32 = Q17.1_32, NEO_O_33 = Q17.1_33, NEO_O_34 = Q17.1_34, NEO_O_35 = Q17.1_35, NEO_O_36 = Q17.1_36, NEO_A_37 = Q17.1_37, NEO_A_38 = Q17.1_38, NEO_A_39 = Q17.1_39, NEO_A_40 = Q17.1_40, NEO_A_41 = Q17.1_41, NEO_A_42 = Q17.1_42, NEO_A_43 = Q17.1_43, NEO_A_44 = Q17.1_44, NEO_A_45 = Q17.1_45, NEO_A_46 = Q17.1_46, NEO_A_47 = Q17.1_47, NEO_A_48 = Q17.1_48, NEO_C_49 = Q17.1_49, NEO_C_50 = Q17.1_50, NEO_C_51 = Q17.1_51, NEO_C_52 = Q17.1_52, NEO_C_53 = Q17.1_53, NEO_C_54 = Q17.1_54, NEO_C_55 = Q17.1_55, NEO_C_56 = Q17.1_56, NEO_C_57 = Q17.1_57, NEO_C_58 = Q17.1_58, NEO_C_59 = Q17.1_59, NEO_C_60 = Q17.1_60
                ) %>% 
  
# replace unclear/inappropriate responses
  # remove non-numeric responses to "age_first_phone"
    # this will force all non-numeric value to "NA"
  mutate(age_first_phone = as.numeric(age_first_phone)) %>% 
  
  # change variables to numeric or factor as needed
  # demographic qs
  mutate(age = as.numeric(age), age = as.numeric(age), gender = factor(gender, levels = c(1:4), labels = c("Male", "Female", "Other", "Prefer not to say")), lang = factor(lang, levels = c(1:2), labels = c("English", "Other")), prof = factor(prof, levels = c(1:3), labels = c("Low", "Moderate", "High")), live = factor(live, levels = c(1:11), labels = c("North America", "Central America", "South America", "Europe", "Africa", "Asia", "Australia", "Pacific Islander", "Caribbean Islands", "Other", "Prefer not to say")), edu = factor(edu, levels = c(1:9), labels = c("some high school", "high school or equivalent", "some college, no degree", "associate degree", "bachelor’s degree", "master’s degree", "professional degree", "doctorate or higher", "Prefer not to say")), 
         
         # employ = factor(employ, levels = c(1:8), labels = c("full-time", "patt-time", "self-employed", "unemployed", "student", "retired", "unable to work", "Prefer not to say")), industry = factor(industry, levels = c(1:16), labels = c("student", "goods-producing sector", "utilities", "construction", "manufacturing", "services-producing sector", "finance, insurance, real estate, rental and leasing", "professional, scientific and technical services", "business, building and other support services", "educational services", "health care and social assistance", "information, culture and recreation", "accommodation and food services", "public administration", "other", "does not apply to me")), 

  # phone use -- general         
phone_for_work = factor(phone_for_work, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), specific_app = factor(specific_app, levels = c(1:3), labels = c("yes", "no", "does not apply to me")), app_most_used = factor(app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), 

  # phone use -- iphone / ST
iphone = factor(iphone, levels = c(1:2), labels = c("yes", "no")), ST = factor(ST, levels = c(1:3), labels = c("had ST", "ST new", "no ST")), ST_app_most_used = factor(ST_app_most_used, levels = c(1:5), labels = c("Games", "Social Networking", "Entertainment", "Business/Productivity", "Other")), ST_app_text_mess = as.numeric(ST_app_text_mess), ST_weekly_tot_hours = as.numeric(ST_weekly_tot_hours),  ST_daily_pickups = as.numeric(ST_daily_pickups), ST_daily_not = as.numeric(ST_daily_not), 

  # comfort level qs
com_gen = as.numeric(com_gen), com_unattended = as.numeric(com_unattended), com_leave_with_others = as.numeric(com_leave_with_others), com_locked = as.numeric(com_locked), com_room_task = as.numeric(com_room_task),
  # privacy qs
passcode = factor(passcode, levels = c(1:2), labels = c("yes", "no")), passcode_others = factor(passcode_others, levels = c(1:2), labels = c("yes", "no")), passcode_relation = factor(passcode_relation, levels = c(1:7), labels = c("parents", "other family", "significant other", "friends", "co-worker / supervisor", "other", "DNA")), other_use = factor(other_use, levels = c(1:4), labels = c("Yes, only in my presence.", "Yes, depending on the individual and only in my presence.", "Yes, depending on the individual and does not need to be in my presence.", "No")), other_room = factor(other_room, levels = c(1:2), labels = c("yes", "no")), 

  # dustraction qs
dist_daily = as.numeric(dist_daily), dist_study = as.numeric(dist_study), dist_device = factor(dist_device, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_studywork = factor(dist_device_studywork, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), dist_device_social = factor(dist_device_social, levels = c(1:5), labels = c("Computer", "Phone", "iPad/Tablet", "Smartwatch", "Other")), 

  # location & power questions
pow_not_using = as.numeric(pow_not_using), pow_notifications_on = as.numeric(pow_notifications_on), pow_vibrate = as.numeric(pow_vibrate), pow_study = as.numeric(pow_study), pow_exam = as.numeric(pow_exam), pow_lec = as.numeric(pow_lec), pow_work = as.numeric(pow_work), pow_meeting = as.numeric(pow_meeting), pow_sleep = as.numeric(pow_sleep), loc_typical = factor(loc_typical, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_study = factor(loc_study, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_exam = factor(loc_exam, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_lec = factor(loc_lec, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_work = factor(loc_work, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_meeting = factor(loc_meeting, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), loc_social = factor(loc_social, levels = c(1:3), labels = c("On my desk", "In my pocket or bag", "In another room")), 

  # exploratory qs
communicate = factor(communicate, levels = c(1:4), labels = c("Family", "Friends", "Work", "Other")), phone_use = factor(phone_use, levels = c(1:5), labels = c("Calling/Texting", "Social Media", "Games", "Email", "Other")), phone_value = factor(phone_value, levels = c(1:4), labels = c("$0-$20", "$21-$40", "$41-$60", ">$60")), phantom = factor(phantom, levels = c(1:2), labels = c("yes", "no")), 
         ) %>% 
  mutate_at(vars(starts_with("employ_")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("industry_")),funs(as.numeric)) %>% 
  
  mutate_at(vars(starts_with("NMPQ")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SRS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SAD")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("DASS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("BIS")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("SSEIT")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("RSES")),funs(as.numeric)) %>% 
  mutate_at(vars(starts_with("NEO")),funs(as.numeric)) %>% 
  
  # reverse code items...
    # this REPLACES original items with new reverse-coded values
    # the subtraction is made based on the likert scale
      # so if the scale is 1-4, then it should be 5-score
  # for SRS: 5, 7, 9
  mutate(SRS_5 = 5-SRS_5) %>% 
  mutate(SRS_7 = 5-SRS_7) %>% 
  mutate(SRS_9 = 5-SRS_9) %>% 
  # for BIS: 1, 4, 5, 6
  mutate(BIS_1 = 5-BIS_1) %>% 
  mutate(BIS_4 = 5-BIS_4) %>% 
  mutate(BIS_5 = 5-BIS_5) %>% 
  mutate(BIS_6 = 5-BIS_6) %>% 
  # for SSEIT: 5, 28, 33
  mutate(SSEIT_5 = 6-SSEIT_5) %>% 
  mutate(SSEIT_28 = 6-SSEIT_28) %>% 
  mutate(SSEIT_33 = 6-SSEIT_33) %>% 
  # for RSES: 3, 5, 8, 9, 10
  mutate(RSES_3 = 5-RSES_3) %>% 
  mutate(RSES_5 = 5-RSES_5) %>% 
  mutate(RSES_8 = 5-RSES_8) %>% 
  mutate(RSES_9 = 5-RSES_9) %>% 
  mutate(RSES_10 = 5-RSES_10) %>% 
  # for NEO: 9-12; 16; 28, 30-34, 36; 39, 40, 43-46; 52, 58-60
  mutate(NEO_N_9 = 6-NEO_N_9) %>% 
  mutate(NEO_N_10 = 6-NEO_N_10) %>% 
  mutate(NEO_N_11 = 6-NEO_N_11) %>% 
  mutate(NEO_N_12 = 6-NEO_N_12) %>% 
  
  mutate(NEO_E_16 = 6-NEO_E_16) %>% 
  
  mutate(NEO_O_28 = 6-NEO_O_28) %>% 
  mutate(NEO_O_30 = 6-NEO_O_30) %>% 
  mutate(NEO_O_31 = 6-NEO_O_31) %>% 
  mutate(NEO_O_32 = 6-NEO_O_32) %>% 
  mutate(NEO_O_33 = 6-NEO_O_33) %>% 
  mutate(NEO_O_34 = 6-NEO_O_34) %>% 
  mutate(NEO_O_36 = 6-NEO_O_36) %>% 
  
  mutate(NEO_A_39 = 6-NEO_A_39) %>% 
  mutate(NEO_A_40 = 6-NEO_A_40) %>% 
  mutate(NEO_A_43 = 6-NEO_A_43) %>% 
  mutate(NEO_A_44 = 6-NEO_A_44) %>% 
  mutate(NEO_A_45 = 6-NEO_A_45) %>% 
  mutate(NEO_A_46 = 6-NEO_A_46) %>% 
  
  mutate(NEO_C_52 = 6-NEO_C_52) %>% 
  mutate(NEO_C_58 = 6-NEO_C_58) %>% 
  mutate(NEO_C_59 = 6-NEO_C_59) %>% 
  mutate(NEO_C_60 = 6-NEO_C_60) %>% 
  
  # add scores for each questionnaire... rowwise() %>% dplyr::mutate(SAD_score = sum(c_across(SAD_1:SAD_13)))
  # for each p....
  # get NMPQ score -- SUM
  rowwise() %>% dplyr::mutate(NMPQ_sum = sum(c_across(NMPQ_1:NMPQ_20))) %>% 
  
  # get SRS score -- SUM
  rowwise() %>% dplyr::mutate(SRS_sum = sum(c_across(SRS_1:SRS_10))) %>% 
  
  # get SAD score -- SUM
    # overall (1-13)
    rowwise() %>% dplyr::mutate(SAD_sum = sum(c_across(starts_with("SAD")))) %>%
    # dependency (1-3)
    rowwise() %>% dplyr::mutate(SAD_dep_sum = sum(c_across(SAD_dep_1:SAD_dep_3))) %>%
    # accessibility (5-7)
    rowwise() %>% dplyr::mutate(SAD_access_sum = sum(c_across(SAD_access_5:SAD_access_7))) %>%
    # emotional attachment (8-11)
    rowwise() %>% dplyr::mutate(SAD_ea_sum = sum(c_across(SAD_ea_8:SAD_ea_11))) %>%
    # distractibility (12-13)
    rowwise() %>% dplyr::mutate(SAD_dist_sum = sum(c_across(SAD_dist_12:SAD_dist_13))) %>%
  
  # get DASS score -- SUM
    # depression (3, 5, 10, 13, 16, 17, 21)
    rowwise() %>% dplyr::mutate(DASS_D_sum = sum(c_across(starts_with("DASS_D")))) %>% 
    # anxiety (2, 4, 7, 9, 15, 19, 20)
    rowwise() %>% dplyr::mutate(DASS_A_sum = sum(c_across(starts_with("DASS_A")))) %>% 
    # stress (1, 6, 8, 11, 12, 14, 18)
    rowwise() %>% dplyr::mutate(DASS_S_sum = sum(c_across(starts_with("DASS_S")))) %>% 
  
  
  # get BIS score -- SUM
  rowwise() %>% dplyr::mutate(BIS_sum = sum(c_across(starts_with("BIS")))) %>% 
  
  # get SSEIT score -- SUM
  rowwise() %>% dplyr::mutate(SSEIT_sum = sum(c_across(starts_with("SSEIT")))) %>% 
  
  # get RSES score -- SUM
  rowwise() %>% dplyr::mutate(RSES_sum = sum(c_across(starts_with("RSES")))) %>% 
  
  # get NEO score -- SUM
    # Neuroticism (1-12) 
    rowwise() %>% dplyr::mutate(NEO_N_sum = sum(c_across(starts_with("NEO_N")))) %>% 
    # Extraversion (13-24)
    rowwise() %>% dplyr::mutate(NEO_E_sum = sum(c_across(starts_with("NEO_E")))) %>% 
    # Openness (25-36)
    rowwise() %>% dplyr::mutate(NEO_O_sum = sum(c_across(starts_with("NEO_O")))) %>% 
    # Agreeableness (37-48)
    rowwise() %>% dplyr::mutate(NEO_A_sum = sum(c_across(starts_with("NEO_A")))) %>% 
    # Conscientiousness (49-60)
    rowwise() %>% dplyr::mutate(NEO_C_sum = sum(c_across(starts_with("NEO_C"))))
  
  # example to get measn: get NMPQ score -- MEAN
  # rowwise() %>% dplyr::mutate(mean(c_across(NMPQ_1:NMPQ_20))) %>% 

# export final data file
  write.csv(Mturk_data,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/Mturk_data(oct28).csv", row.names = FALSE)
```

# Descriptive Statistics

## NOMINAL (study 1 & study 2)

### Demographic & Typical Smartphone Use

```{r}

# output is a list of tibbles, each with: $xxx = var name, x = level(s), n = count, pct = percentage

## FOR SONA DATA
sona_nom_demo <- 
  SONA_data %>% 
  # get subset of data with only nominal vars
  select(gender, lang, prof, live, edu, 
         phone_for_work, specific_app, app_most_used, iphone, ST, ST_app_most_used, passcode, passcode_others, starts_with("passcode_relation_"), other_use, other_room, dist_device, dist_device_studywork, dist_device_social, loc_typical:loc_social, communicate, phone_use, phone_value, phantom) %>% 
  # get freq & proportion for each var
  purrr::map(~ count(tibble(x = .x), x) %>% 
               mutate(pct = (n / sum(n) * 100)))

# make list into 1 long data frame to show as table...
sona_nom_demo_t <- 
  # this keeps the var name in the 1st column (using-- .id = "tib")
  map_dfr(sona_nom_demo, bind_rows, .id = "tib")

# for the employ and industry counts
  # employ
  sona_nom_demo_employ <- 
    SONA_data %>% 
    select(starts_with("employ_")) %>% 
    purrr::map(~ count(tibble(x = .x), x) %>% 
                 mutate(pct = (n / sum(n) * 100)))
  
  sona_nom_demo_employ_t <- 
    # this keeps the var name in the 1st column (using-- .id = "tib")
    map_dfr(sona_nom_demo_employ, bind_rows, .id = "tib")
  #industry
  sona_nom_demo_industry <- 
    SONA_data %>% 
    select(starts_with("industry_")) %>% 
    purrr::map(~ count(tibble(x = .x), x) %>% 
                 mutate(pct = (n / sum(n) * 100)))
  
  sona_nom_demo_industry_t <- 
    # this keeps the var name in the 1st column (using-- .id = "tib")
    map_dfr(sona_nom_demo_industry, bind_rows, .id = "tib")

## FOR MTURK DATA
mturk_nom_demo <- 
  Mturk_data %>% 
  # get subset of data with only nominal vars
  select(gender, lang, prof, live, edu, 
         phone_for_work, specific_app, app_most_used, iphone, ST, ST_app_most_used, passcode, passcode_others, starts_with("passcode_relation_"), other_use, other_room, dist_device, dist_device_studywork, dist_device_social, loc_typical:loc_social, communicate, phone_use, phone_value, phantom) %>% 
  # get freq & proportion for each var
  purrr::map(~ count(tibble(x = .x), x) %>% 
               mutate(pct = (n / sum(n) * 100)))

# make list into 1 long data frame to show as table...
mturk_nom_demo_t <- 
  # this keeps the var name in the 1st column (using-- .id = "tib")
  map_dfr(mturk_nom_demo, bind_rows, .id = "tib")

# for the employ and industry counts
  # employ
  mturk_nom_demo_employ <- 
    Mturk_data %>% 
    select(starts_with("employ_")) %>% 
    purrr::map(~ count(tibble(x = .x), x) %>% 
                 mutate(pct = (n / sum(n) * 100)))
  
  mturk_nom_demo_employ_t <- 
    # this keeps the var name in the 1st column (using-- .id = "tib")
    map_dfr(mturk_nom_demo_employ, bind_rows, .id = "tib")
  #industry
  mturk_nom_demo_industry <- 
    Mturk_data %>% 
    select(starts_with("industry_")) %>% 
    purrr::map(~ count(tibble(x = .x), x) %>% 
                 mutate(pct = (n / sum(n) * 100)))
  
  mturk_nom_demo_industry_t <- 
    # this keeps the var name in the 1st column (using-- .id = "tib")
    map_dfr(mturk_nom_demo_industry, bind_rows, .id = "tib")
```

```{r}
# SONA
kable(sona_nom_demo_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(sona_nom_demo_employ_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA(Employment Status).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(sona_nom_demo_industry_t, caption = "Frequency & percentage for nominal vars STUDY 1 - SONA (Industry).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(plyr::count(as.factor(SONA_data$langO)), caption = "Frequency & percentage for language other var STUDY 1 - SONA.", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()


# MTURK
kable(mturk_nom_demo_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(mturk_nom_demo_employ_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk(Employment Status).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(mturk_nom_demo_industry_t, caption = "Frequency & percentage for nominal vars STUDY 2 - mturk (Industry).", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(plyr::count(as.factor(Mturk_data$langO)), caption = "Frequency & percentage for language other var STUDY 2 - mturk", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()


# export tables as csv files
  write.csv(sona_nom_demo_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_t(oct28).csv", row.names = FALSE)
  write.csv(sona_nom_demo_employ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_employ_t(oct28).csv", row.names = FALSE)
  write.csv(sona_nom_demo_industry_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_industry_t(oct28).csv", row.names = FALSE)
  write.csv(plyr::count(as.factor(SONA_data$langO)),"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_demo_employ_t(oct28).csv", row.names = FALSE)

  write.csv(mturk_nom_demo_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_t(oct28).csv", row.names = FALSE)
  write.csv(mturk_nom_demo_employ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_employ_t(oct28).csv", row.names = FALSE)
  write.csv(mturk_nom_demo_industry_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_industry_t(oct28).csv", row.names = FALSE)
  write.csv(plyr::count(as.factor(Mturk_data$langO)),"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_demo_employ_t(oct28).csv", row.names = FALSE)
```

### Questionnaires (levels)

#### Tables

```{r}

# output is a list of tibbles, each with: $xxx = var name, x = level(s), n = count, pct = percentage

## FOR STUDY 1 DATA -- SONA
sona_nom_ques_all <- 
  SONA_data %>% 
  # get subset of data with only nominal vars
  select(ends_with("_sum")) %>% 
  # divide sum scores into L>M>H
   # details provided for 1st instance... 
  mutate(NMPQ_sum = # name of var, this replaces existing b/c its the same
           cut(NMPQ_sum, # state var 
               breaks = c(20, 43, 67, 91, 115, 140), # this is providing a seq from 20 >> 140, with breaks of 40 (40 det by breaking up the range into 3: 140-20 = 120, 120/3 = 40)
               labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% # state the new labels for the levels
  mutate(SAD_sum = cut(SAD_sum, breaks = c(7, 23, 40, 57, 74, 91), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_dep_sum = cut(SAD_dep_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_ea_sum = cut(SAD_ea_sum, breaks = c(4, 8, 13, 18, 23, 28), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_access_sum = cut(SAD_access_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
  mutate(SAD_dist_sum = cut(SAD_dist_sum, breaks = c(2, 4, 6, 8, 11, 14), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(BIS_sum = cut(BIS_sum, breaks = c(8, 12, 17, 22, 27, 32), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_N_sum = cut(NEO_N_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_E_sum = cut(NEO_E_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_O_sum = cut(NEO_O_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_A_sum = cut(NEO_A_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_C_sum = cut(NEO_C_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
  mutate(RSES_sum = cut(RSES_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SRS_sum = cut(SRS_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SSEIT_sum = cut(SSEIT_sum, breaks = c(33, 59, 86, 113, 140, 165), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 9, 13, 20, 27, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 7, 9, 14, 19, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 14, 18, 25, 33, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T))
  # get freq & proportion for each var
  purrr::map(~ count(tibble(x = .x), x) %>% 
               mutate(pct = (n / sum(n) * 100)))

# make list into 1 long data frame to show as table...
sona_nom_ques_all_t <- 
  # this keeps the var name in the 1st column (using-- .id = "tib")
  map_dfr(sona_nom_ques_all, bind_rows, .id = "tib")


## FOR STUDY 2 DATA -- MTURK
mturk_nom_ques_all <- 
  Mturk_data %>% 
  # get subset of data with only nominal vars
  select(ends_with("_sum")) %>% 
  # divide sum scores into L>M>H
   # details provided for 1st instance... 
  mutate(NMPQ_sum = # name of var, this replaces existing b/c its the same
           cut(NMPQ_sum, # state var 
               breaks = c(20, 43, 67, 91, 115, 140), # this is providing a seq from 20 >> 140, with breaks of 40 (40 det by breaking up the range into 3: 140-20 = 120, 120/3 = 40)
               labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% # state the new labels for the levels
  mutate(SAD_sum = cut(SAD_sum, breaks = c(7, 23, 40, 57, 74, 91), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_dep_sum = cut(SAD_dep_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_ea_sum = cut(SAD_ea_sum, breaks = c(4, 8, 13, 18, 23, 28), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SAD_access_sum = cut(SAD_access_sum, breaks = c(3, 6, 10, 14, 18, 21), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
  mutate(SAD_dist_sum = cut(SAD_dist_sum, breaks = c(2, 4, 6, 8, 11, 14), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(BIS_sum = cut(BIS_sum, breaks = c(8, 12, 17, 22, 27, 32), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_N_sum = cut(NEO_N_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_E_sum = cut(NEO_E_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_O_sum = cut(NEO_O_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_A_sum = cut(NEO_A_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(NEO_C_sum = cut(NEO_C_sum, breaks = c(12, 21, 31, 41, 51, 60), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>%
  mutate(RSES_sum = cut(RSES_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SRS_sum = cut(SRS_sum, breaks = c(10, 15, 21, 27, 33, 40), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(SSEIT_sum = cut(SSEIT_sum, breaks = c(33, 59, 86, 113, 140, 165), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 9, 13, 20, 27, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 7, 9, 14, 19, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 14, 18, 25, 33, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_D_sum = cut(DASS_D_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_A_sum = cut(DASS_A_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T)) %>% 
  # mutate(DASS_S_sum = cut(DASS_S_sum, breaks = c(0, 8, 17, 26, 35, 42), labels = c("Low", "Mild", "Moderate", "High", "Very High"), include.lowest = T))
  # get freq & proportion for each var
  purrr::map(~ count(tibble(x = .x), x) %>% 
               mutate(pct = (n / sum(n) * 100)))

# make list into 1 long data frame to show as table...
mturk_nom_ques_all_t <- 
  # this keeps the var name in the 1st column (using-- .id = "tib")
  map_dfr(mturk_nom_ques_all, bind_rows, .id = "tib")
```

```{r}
kable(sona_nom_ques_all_t, caption = "Frequency & percentage for nominal vars - ques- STUDY 1 - SONA.", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  footnote(general = "There was no task completed during study 1.") %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

kable(mturk_nom_ques_all_t, caption = "Frequency & percentage for nominal vars - ques- STUDY 2 - MTURK", align = rep('crcc'), col.names = c("Var", "level" ,"n", "%"), row.names = TRUE) %>% 
  footnote(general = "There was no task completed during study 2.") %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic()

# export table as csv file
  write.csv(sona_nom_ques_all_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_nom_ques_all_t(oct28).csv", row.names = FALSE)
  write.csv(mturk_nom_ques_all_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_nom_ques_all_t(oct28).csv", row.names = FALSE)
```

#### Stacked Bar Plots

Show the questionnaire data as a stacked bar plot for each study for
visualization comparison.

```{r}
# FOR SAD OVERALL ONLY

# create color pallet for stacked bar plots
# stacked_colours <- c("#7E1044", "#981570", "#6B138B", "#491296", "#170FA7")

stacked_colours <- c("#98410B", "#F0803C", "#3F826D", "#08605F", "#414535")

# Study 1
# Make data file
sona_nom_ques_stack_data <- 
  sona_nom_ques_all_t %>% 
  select(-n) %>% 
  mutate_at("tib", str_replace, "_sum", "") %>%  # remove "_sum" from factor
  # remove SAD subscales
  filter(!tib == "SAD_dep") %>% 
  filter(!tib == "SAD_ea") %>% 
  filter(!tib == "SAD_access") %>% 
  filter(!tib == "SAD_dist")

sona_nom_ques_stack_plot <- 
  ggplot(sona_nom_ques_stack_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) + 
    geom_bar(position="stack", stat="identity") +
    scale_x_discrete(limits = c("NMPQ", "SAD", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
    scale_fill_manual(values=stacked_colours) + 
    # geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
    # add labels
    labs(
       # title = "Stacked Barplot for Questionnaire Scores: Study 1",
       # subtitle = "x",
       # caption = "Note: ",
       x = "Measure", 
       y = "Proportion (%)",
       fill = "Level"
       ) + 
    theme_classic() +
    # add specific formatting
    theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
        plot.subtitle = element_text(color = "black", size = 13),
        plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
        text = element_text(size = 13), 
        axis.ticks.x = element_blank(), 
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        axis.text = element_text(color = "black"))

# show plot
sona_nom_ques_stack_plot

# Study 2
# Make data file
mturk_nom_ques_stack_data <- 
  mturk_nom_ques_all_t %>% 
  select(-n) %>% 
  mutate_at("tib", str_replace, "_sum", "") %>%  # remove "_sum" from factor
  # remove SAD subscales
  filter(!tib == "SAD_dep") %>% 
  filter(!tib == "SAD_ea") %>% 
  filter(!tib == "SAD_access") %>% 
  filter(!tib == "SAD_dist")

mturk_nom_ques_stack_plot <- 
  ggplot(mturk_nom_ques_stack_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) + 
    geom_bar(position="stack", stat="identity") +
    scale_x_discrete(limits = c("NMPQ", "SAD", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) +
    scale_fill_manual(values=stacked_colours) + 
    # geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
    # add labels
    labs(
       # title = "Stacked Barplot for Questionnaire Scores: Study 2",
       # subtitle = "x",
       # caption = "Note: ",
       x = "Measure", 
       y = "Proportion (%)",
       fill = "Level"
       ) + 
    theme_classic() +
    # add specific formatting
    theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
        plot.subtitle = element_text(color = "black", size = 13),
        plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
        text = element_text(size = 13), 
        axis.ticks.x = element_blank(), 
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        axis.text = element_text(color = "black"))

# show plot
mturk_nom_ques_stack_plot

# export plots
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_nom_ques_stack_plot(oct28)", plot = sona_nom_ques_stack_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_nom_ques_stack_plot(oct28)", plot = mturk_nom_ques_stack_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

```

```{r}
# FOR ALL SUBSCALES + SAD OVERALL

# create color pallet for stacked bar plots
stacked_colours_exp <- c("#98410B", "#F0803C", "#3F826D", "#08605F", "#414535")

# Study 1
# Make data file
sona_nom_ques_stack_exp_data <- 
  sona_nom_ques_all_t %>% 
  select(-n) %>% 
  mutate_at("tib", str_replace, "_sum", "") # remove "_sum" from factor
  # filter(!tib == "SAD") %>%  # remove SAD general

sona_nom_ques_stack_exp_plot <- 
  ggplot(sona_nom_ques_stack_exp_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) + 
    geom_bar(position="stack", stat="identity") +
    scale_x_discrete(limits = c("NMPQ", "SAD", "SAD_dep", "SAD_ea", "SAD_access", "SAD_dist", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) + 
    # geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
    # add labels
    labs(
       title = "Stacked Barplot for Questionnaire Scores: Study 1 -- ALL Exploratory",
       # subtitle = "x",
       # caption = "Note: ",
       x = "Measure", 
       y = "Proportion (%)",
       fill = "Level"
       ) + 
    theme_classic() +
    # add specific formatting
    theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
        plot.subtitle = element_text(color = "black", size = 13),
        plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
        text = element_text(size = 13), 
        axis.ticks.x = element_blank(), 
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        axis.text = element_text(color = "black"))

# show plot
sona_nom_ques_stack_exp_plot

# Study 2
# Make data file
mturk_nom_ques_stack_exp_data <- 
  mturk_nom_ques_all_t %>% 
  select(-n) %>% 
  mutate_at("tib", str_replace, "_sum", "") # remove "_sum" from factor
  # filter(!tib == "SAD") %>%  # remove SAD general

mturk_nom_ques_stack_exp_plot <- 
  ggplot(mturk_nom_ques_stack_exp_data, aes(fill=factor(x, levels = c("Very High", "High", "Moderate", "Mild", "Low")), y=pct, x=tib)) + 
    geom_bar(position="stack", stat="identity") +
    scale_x_discrete(limits = c("NMPQ", "SAD", "SAD_dep", "SAD_ea", "SAD_access", "SAD_dist", "BIS", "NEO_O", "NEO_C", "NEO_E", "NEO_A", "NEO_N", "RSES", "SRS", "SSEIT", "DASS_D", "DASS_A", "DASS_S"), labels = c("NMPQ"="NMPQ", "SAD"="SAD", "BIS"="BIS", "NEO_O"="NEO: O", "NEO_C"="NEO: C", "NEO_E"="NEO: E", "NEO_A"="NEO: A", "NEO_N"="NEO: N", "RSES"="RSES", "SRS"="SRS", "SSEIT"="SSEIT", "DASS_D"="DASS: D", "DASS_A"="DASS: A", "DASS_S"="DASS: S")) + 
    # geom_text(aes(label = round(pct,0)), size = 3, hjust = 0.5, vjust = 0, position = "stack") +
    # add labels
    labs(
       title = "Stacked Barplot for Questionnaire Scores: Study 2 -- ALL Exploratory",
       # subtitle = "x",
       # caption = "Note: ",
       x = "Measure", 
       y = "Proportion (%)",
       fill = "Level"
       ) + 
    theme_classic() +
    # add specific formatting
    theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
        plot.subtitle = element_text(color = "black", size = 13),
        plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
        text = element_text(size = 13), 
        axis.ticks.x = element_blank(), 
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        axis.text = element_text(color = "black"))

# show plot
mturk_nom_ques_stack_exp_plot

# export plots
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_nom_ques_stack_exp_plot(oct28)", plot = sona_nom_ques_stack_exp_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_nom_ques_stack_exp_plot(oct28)", plot = mturk_nom_ques_stack_exp_plot, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

```

```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# attempting a "jitter plot"...
pilot_nom_ques_jitter <- tibble("ques" = c(rep("MPIQ", nrow(pilot_nom_ques_t)), rep("MPIQ2", nrow(pilot_nom_ques_t))), 
         "level" = c(rep("low", pilot_nom_ques$MPIQ_sum$n[1]), rep("mod", pilot_nom_ques$MPIQ_sum$n[2]), rep("high", pilot_nom_ques$MPIQ_sum$n[3]),
                     rep("low", pilot_nom_ques$MPIQ_sum$n[1]), rep("mod", pilot_nom_ques$MPIQ_sum$n[2]), rep("high", pilot_nom_ques$MPIQ_sum$n[3]))
         )

pilot_nom_ques_jitter_plot <- 
  ggplot(pilot_nom_ques_jitter, aes(x=ques, y=level)) + 
    # geom_bar(position="stack", stat="identity") +
    geom_jitter() +
    scale_color_manual(values=c('lightseagreen','darkorchid', 'deepskyblue4')) + 
    # add labels
    labs(
       title = "Stacked Barplot for Questionnaire Scores: Study 1",
       subtitle = "x",
       caption = "Note: ",
       x = "Smartphone Location", 
       y = "Memory Composite Score"
       ) + 
    theme_classic() +
    # add specific formatting
    theme(plot.title = element_text(color = "black", size = 14, face = "bold"),
        plot.subtitle = element_text(color = "black", size = 13),
        plot.caption = element_text(hjust = 0, size = 12, face = "italic"),
        text = element_text(size = 13), 
        axis.ticks.x = element_blank(), 
        axis.text = element_text(color = "black"))
```

## CONTINUOUS data (pilot & main)

### Demographic & Typical Smartphone Use

```{r}
## FOR SONA
sona_cont_demo <- 
  SONA_data %>% 
  # select continuous vars
  select(age, age_first_phone, ST_weekly_tot_hours:ST_daily_not, com_gen:com_room_task, dist_daily, dist_study, pow_not_using:pow_sleep) %>% 
  # apply describe fn for: n  mean   sd median trimmed  mad min max range  skew kurtosis   se
  psych::describe()

## FOR MTURK
mturk_cont_demo <- 
  Mturk_data %>% 
  # select continuous vars
  select(age, age_first_phone, ST_weekly_tot_hours:ST_daily_not, com_gen:com_room_task, dist_daily, dist_study, pow_not_using:pow_sleep) %>% 
  # apply describe fn for: n  mean   sd median trimmed  mad min max range  skew kurtosis   se
  psych::describe()
```

```{r}
# show all with kable 
## SONA
kable(sona_cont_demo, caption = "Descriptive statistics for continuous vars - STUDY 1 - SONA", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic() 

## MTURK
kable(mturk_cont_demo, caption = "Descriptive statistics for continuous vars - STUDY 2 - MTURK", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic() 

# export tables as csv files
write.csv(sona_cont_demo,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_cont_demo(oct28).csv", row.names = T)

write.csv(mturk_cont_demo,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_cont_demo(oct28).csv", row.names = T)
```

### Questionnaires

```{r}
## FOR SONA
sona_cont_ques <- 
  SONA_data %>% 
  # select continuous vars
  select(starts_with("NMPQ"), starts_with("SRS"), starts_with("SAD"), starts_with("DASS"), starts_with("BIS"), starts_with("SSEIT"), starts_with("RSES"), starts_with("NEO")) %>% 
  # apply describe fn for: n  mean   sd median trimmed  mad min max range  skew kurtosis   se
  psych::describe()

## FOR MTURK
mturk_cont_ques <- 
  Mturk_data %>% 
  # select continuous vars
  select(starts_with("NMPQ"), starts_with("SRS"), starts_with("SAD"), starts_with("DASS"), starts_with("BIS"), starts_with("SSEIT"), starts_with("RSES"), starts_with("NEO")) %>% 
  # apply describe fn for: n  mean   sd median trimmed  mad min max range  skew kurtosis   se
  psych::describe()
```

```{r}
# show all with kable 
## SONA
kable(sona_cont_ques, caption = "Descriptive statistics for questionnaires - STUDY 1 - SONA", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic() 

## MTURK
kable(mturk_cont_ques, caption = "Descriptive statistics for questionnaires - STUDY 1 - MTURK", align = rep('crcc'), row.names = TRUE) %>% 
  kable_styling(bootstrap_options = "striped", "hover", full_width = F) %>%
  kable_classic() 

# export tables as csv files
write.csv(sona_cont_ques,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_cont_ques(oct28).csv", row.names = T)

write.csv(mturk_cont_ques,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_cont_ques(oct28).csv", row.names = T)
```

# Analyses

## Assumption Tests

<details>

<summary>

All assumptions were met for the correlation analysis. Click here to see
the assumption tests.

</summary>

The assumptions for a correlation analysis were tested. There were two
assumptions: (1) Linearity and (2) Normality.

1.  Linearity was visually inspected from the scatter plots (see plots
    in the "*Figures: Scatter Plots*" section).

2.  Normality was tested using (a) the Shapiro-Wilk normality test and
    (b) visually inspected using a normality plot.

    -   The Shapiro-Wilk normality test will show two values (i.e., *W*
        and *p*) and each test uses the following hypotheses: H~N~: the
        data are normally distributed and H~A~: the data are not
        normally distributed.

    -   The normality plots used were Q-Q plots (quantile-quantile
        plots), which draw the correlation between a given sample and
        the theoretical normal distribution.

        </summary>

### (a) Shapiro-Wilk Normality Test

<details>

<summary>

The table depicts the results of the Shapiro-Wilks normality test for
each measure. *(click to see code)*

</summary>

```{r message=FALSE, warning=FALSE}
# create a table to show results of all normality tests
corrs_reg_norm_test_sona_all <- 
  as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D", 
                        "DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"), 
                 rbind(c(shapiro.test(SONA_data$NMPQ_sum)$statistic, shapiro.test(SONA_data$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
                 c(shapiro.test(SONA_data$SAD_sum)$statistic, shapiro.test(SONA_data$SAD_sum)$p.value), # W & p-value for SAD_sum
                 c(shapiro.test(SONA_data$SAD_dep_sum)$statistic, shapiro.test(SONA_data$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
                 c(shapiro.test(SONA_data$SAD_access_sum)$statistic, shapiro.test(SONA_data$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
                 c(shapiro.test(SONA_data$SAD_ea_sum)$statistic, shapiro.test(SONA_data$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
                 c(shapiro.test(SONA_data$SAD_dist_sum)$statistic, shapiro.test(SONA_data$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
                 c(shapiro.test(SONA_data$age)$statistic, shapiro.test(SONA_data$age)$p.value), # W & p-value for age
                 c(shapiro.test(SONA_data$SRS_sum)$statistic, shapiro.test(SONA_data$SRS_sum)$p.value), # W & p-value for SRS_sum
                 c(shapiro.test(SONA_data$DASS_D_sum)$statistic, shapiro.test(SONA_data$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
                 c(shapiro.test(SONA_data$DASS_A_sum)$statistic, shapiro.test(SONA_data$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
                 c(shapiro.test(SONA_data$DASS_S_sum)$statistic, shapiro.test(SONA_data$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
                 c(shapiro.test(SONA_data$BIS_sum)$statistic, shapiro.test(SONA_data$BIS_sum)$p.value), # W & p-value for BIS_sum
                 c(shapiro.test(SONA_data$SSEIT_sum)$statistic, shapiro.test(SONA_data$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
                 c(shapiro.test(SONA_data$RSES_sum)$statistic, shapiro.test(SONA_data$RSES_sum)$p.value), # W & p-value for RSES_sum
                 c(shapiro.test(SONA_data$NEO_N_sum)$statistic, shapiro.test(SONA_data$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
                 c(shapiro.test(SONA_data$NEO_E_sum)$statistic, shapiro.test(SONA_data$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
                 c(shapiro.test(SONA_data$NEO_O_sum)$statistic, shapiro.test(SONA_data$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
                 c(shapiro.test(SONA_data$NEO_A_sum)$statistic, shapiro.test(SONA_data$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
                 c(shapiro.test(SONA_data$NEO_C_sum)$statistic, shapiro.test(SONA_data$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
                 )))
  colnames(corrs_reg_norm_test_sona_all) <- c("Measure", "W", "p")
  # make cols numeric & round to 3 decimals
  corrs_reg_norm_test_sona_all$W <- as.numeric(corrs_reg_norm_test_sona_all$W) %>% round(.,3)
  corrs_reg_norm_test_sona_all$p <- as.numeric(corrs_reg_norm_test_sona_all$p) %>% round(.,3)

corrs_reg_norm_test_mturk_all <- 
  as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D", 
                        "DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"), 
                 rbind(c(shapiro.test(Mturk_data$NMPQ_sum)$statistic, shapiro.test(Mturk_data$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
                 c(shapiro.test(Mturk_data$SAD_sum)$statistic, shapiro.test(Mturk_data$SAD_sum)$p.value), # W & p-value for SAD_sum
                 c(shapiro.test(Mturk_data$SAD_dep_sum)$statistic, shapiro.test(Mturk_data$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
                 c(shapiro.test(Mturk_data$SAD_access_sum)$statistic, shapiro.test(Mturk_data$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
                 c(shapiro.test(Mturk_data$SAD_ea_sum)$statistic, shapiro.test(Mturk_data$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
                 c(shapiro.test(Mturk_data$SAD_dist_sum)$statistic, shapiro.test(Mturk_data$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
                 c(shapiro.test(Mturk_data$age)$statistic, shapiro.test(Mturk_data$age)$p.value), # W & p-value for age
                 c(shapiro.test(Mturk_data$SRS_sum)$statistic, shapiro.test(Mturk_data$SRS_sum)$p.value), # W & p-value for SRS_sum
                 c(shapiro.test(Mturk_data$DASS_D_sum)$statistic, shapiro.test(Mturk_data$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
                 c(shapiro.test(Mturk_data$DASS_A_sum)$statistic, shapiro.test(Mturk_data$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
                 c(shapiro.test(Mturk_data$DASS_S_sum)$statistic, shapiro.test(Mturk_data$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
                 c(shapiro.test(Mturk_data$BIS_sum)$statistic, shapiro.test(Mturk_data$BIS_sum)$p.value), # W & p-value for BIS_sum
                 c(shapiro.test(Mturk_data$SSEIT_sum)$statistic, shapiro.test(Mturk_data$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
                 c(shapiro.test(Mturk_data$RSES_sum)$statistic, shapiro.test(Mturk_data$RSES_sum)$p.value), # W & p-value for RSES_sum
                 c(shapiro.test(Mturk_data$NEO_N_sum)$statistic, shapiro.test(Mturk_data$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
                 c(shapiro.test(Mturk_data$NEO_E_sum)$statistic, shapiro.test(Mturk_data$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
                 c(shapiro.test(Mturk_data$NEO_O_sum)$statistic, shapiro.test(Mturk_data$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
                 c(shapiro.test(Mturk_data$NEO_A_sum)$statistic, shapiro.test(Mturk_data$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
                 c(shapiro.test(Mturk_data$NEO_C_sum)$statistic, shapiro.test(Mturk_data$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
                 )))
  colnames(corrs_reg_norm_test_mturk_all) <- c("Measure", "W", "p")
  # make cols numeric & round to 3 decimals
  corrs_reg_norm_test_mturk_all$W <- as.numeric(corrs_reg_norm_test_mturk_all$W) %>% round(.,3)
  corrs_reg_norm_test_mturk_all$p <- as.numeric(corrs_reg_norm_test_mturk_all$p) %>% round(.,3)
  
# join into 1 large table
corrs_reg_norm_tests_t <- cbind(corrs_reg_norm_test_sona_all, corrs_reg_norm_test_mturk_all[,2:3])
```

</details>

As seen in the table, most measures showed a significant result,
indicating non-normality. The visual inspection of the Q-Q plots was
completed next.

```{r echo=TRUE, message=FALSE, warning=FALSE}
# show table using kable
kable(corrs_reg_norm_tests_t, caption = "Shapiro-Wilks Normality Test for All Measures", align = rep('lcccc')) %>% 
  kable_paper(full_width = F) %>%
  add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  pack_rows("Criterion(s)", 1, 6) %>%
  # column_spec(1, width = "10em") %>% 
  pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()
```

### (b) Q-Q Plots

<details>

<summary>

Overall, the Q-Q plots show the data for all measures except age and age
of first smartphone did fall along or close to the line. The age
measures were not expected to be normally distributed; therefore, the
analysis was completed for each measure. *(click to see code)*

</summary>

```{r echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE}
# SONA - All
grid.arrange(ggqqplot(SONA_data$NMPQ_sum, ylab = "NMPQ"),
             ggqqplot(SONA_data$SAD_sum, ylab = "SAD"),
             ggqqplot(SONA_data$SAD_dep_sum, ylab = "SAD_dep"),
             ggqqplot(SONA_data$SAD_access_sum, ylab = "SAD_access"),
             ggqqplot(SONA_data$SAD_ea_sum, ylab = "SAD_ea"),
             ggqqplot(SONA_data$SAD_dist_sum, ylab = "SAD_dist"),
             ggqqplot(SONA_data$age, ylab = "age"),
             ggqqplot(SONA_data$SRS_sum, ylab = "SRS"),
             ggqqplot(SONA_data$DASS_D_sum, ylab = "DASS_D"),
             ggqqplot(SONA_data$DASS_A_sum, ylab = "DASS_A"),
             ggqqplot(SONA_data$DASS_S_sum, ylab = "DASS_S"),
             ggqqplot(SONA_data$BIS_sum, ylab = "BIS"),
             ggqqplot(SONA_data$SSEIT_sum, ylab = "SSEIT"),
             ggqqplot(SONA_data$RSES_sum, ylab = "RSES"),
             ggqqplot(SONA_data$NEO_N_sum, ylab = "NEO_N"),
             ggqqplot(SONA_data$NEO_E_sum, ylab = "NEO_E"),
             ggqqplot(SONA_data$NEO_O_sum, ylab = "NEO_O"),
             ggqqplot(SONA_data$NEO_A_sum, ylab = "NEO_A"),
             ggqqplot(SONA_data$NEO_C_sum, ylab = "NEO_C"),
             nrow = 4,
             top = text_grob("Q-Q Plots For SONA - ALL", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to the line.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
# for MTURK - ALL
grid.arrange(ggqqplot(Mturk_data$NMPQ_sum, ylab = "NMPQ"),
             ggqqplot(Mturk_data$SAD_sum, ylab = "SAD"),
             ggqqplot(Mturk_data$SAD_dep_sum, ylab = "SAD_dep"),
             ggqqplot(Mturk_data$SAD_access_sum, ylab = "SAD_access"),
             ggqqplot(Mturk_data$SAD_ea_sum, ylab = "SAD_ea"),
             ggqqplot(Mturk_data$SAD_dist_sum, ylab = "SAD_dist"),
             ggqqplot(Mturk_data$age, ylab = "age"),
             ggqqplot(Mturk_data$SRS_sum, ylab = "SRS"),
             ggqqplot(Mturk_data$DASS_D_sum, ylab = "DASS_D"),
             ggqqplot(Mturk_data$DASS_A_sum, ylab = "DASS_A"),
             ggqqplot(Mturk_data$DASS_S_sum, ylab = "DASS_S"),
             ggqqplot(Mturk_data$BIS_sum, ylab = "BIS"),
             ggqqplot(Mturk_data$SSEIT_sum, ylab = "SSEIT"),
             ggqqplot(Mturk_data$RSES_sum, ylab = "RSES"),
             ggqqplot(Mturk_data$NEO_N_sum, ylab = "NEO_N"),
             ggqqplot(Mturk_data$NEO_E_sum, ylab = "NEO_E"),
             ggqqplot(Mturk_data$NEO_O_sum, ylab = "NEO_O"),
             ggqqplot(Mturk_data$NEO_A_sum, ylab = "NEO_A"),
             ggqqplot(Mturk_data$NEO_C_sum, ylab = "NEO_C"),
             nrow = 4,
             top = text_grob("Q-Q Plots For Mturk - ALL", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to the line.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
```

</details>

### (c) Histogram Plots

<details>

<summary>

Overall, the Q-Q plots show the data for all measures except age and age
of first smartphone did fall along or close to the line. The age
measures were not expected to be normally distributed; therefore, the
analysis was completed for each measure. *(click to see code)*

</summary>

```{r echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE}
# SONA - All
grid.arrange(qplot(SONA_data$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
               # qplot(SONA_data$NMPQ_sum, geom = "density"),
             qplot(SONA_data$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(SONA_data$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             nrow = 4,
             top = text_grob("Histogram For SONA Data - ALL", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
# for MTURK - ALL
grid.arrange(qplot(Mturk_data$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
               # qplot(Mturk_data$NMPQ_sum, geom = "density"),
             qplot(Mturk_data$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot(Mturk_data$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             nrow = 4,
             top = text_grob("Histogram For Mturk Data - ALL", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
```

</details>

## Correlations

Get correlations b/w all cont vars for (1) all data and (2) ST data
only.

### All Data

All data collected was analyzed. This was done for
`r nrow(SONA_data)`SONA ps and `r nrow(Mturk_data)` Mturk ps. \#\#\#\#

#### Study 1: SONA

```{r }
sona_corr <- 
  SONA_data %>% 
  # select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
  select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, 
         NMPQ_sum, 
         SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
         BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>% 
  as.matrix() %>% 
  Hmisc::rcorr(type = c("pearson"))

# create new pilot_corr to shown only lower triangle... 
sona_corr2 <- sona_corr
# round to 4 decimals... 
sona_corr2$r <- round(sona_corr2$r, 4)
sona_corr2$P <- round(sona_corr2$P, 4)
sona_corr2$n <- round(sona_corr2$n, 4)
# remove upper triangle form r, p, and n
sona_corr2$r[upper.tri(sona_corr2$r)] <- "-"
sona_corr2$P[upper.tri(sona_corr2$P)] <- "-"
sona_corr2$n[upper.tri(sona_corr2$n)] <- "-"

# show corr table with flattenCorr

kable(flattenCorrMatrix(sona_corr$r, sona_corr$P), caption = "SONA Study - Correlation: Flat") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# print tables using kable
kable(as.data.frame(format(sona_corr2$r, scientific = FALSE)), caption = "SONA Study - Correlation: r values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()
  

kable(as.data.frame(format(sona_corr2$P, scientific = FALSE)), caption = "SONA Study - Correlation: p values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

kable(as.data.frame(format(sona_corr2$n, scientific = FALSE)), caption = "SONA Study - Correlation: n values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# create new matrix for r & p vals with different col names
  # get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
sona_corr_r <- sona_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  
sona_corr_p <- sona_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(sona_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")


# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")

# all vars included
corr_plot_sona_all <- {
  corrplot(sona_corr$r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_corr$P, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .5, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}


# with all SAD subscales
corr_plot_sona_ques_all <- {
  corrplot(sona_corr_r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_corr_p, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# final -- with SAD overall only
corr_plot_sona_ques_final <- {
  corrplot(sona_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_all(oct28)", plot = replayPlot(corr_plot_sona_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ques_all(oct28)", plot = replayPlot(corr_plot_sona_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ques_final(oct28)", plot = replayPlot(corr_plot_sona_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

# export csv files of R values & p values
  write.csv(sona_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_corr_rvals(oct28).csv", row.names = T)
  
  write.csv(sona_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_corr_pvals(oct28).csv", row.names = T)
```

#### Study 2: Mturk

```{r }
mturk_corr <- 
  Mturk_data %>% 
  # select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
  select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, 
         NMPQ_sum, 
         SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
         BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>% 
  as.matrix() %>% 
  Hmisc::rcorr(type = c("pearson"))

# create new pilot_corr to shown only lower triangle... 
mturk_corr2 <- mturk_corr
# round to 4 decimals... 
mturk_corr2$r <- round(mturk_corr2$r, 4)
mturk_corr2$P <- round(mturk_corr2$P, 4)
mturk_corr2$n <- round(mturk_corr2$n, 4)
# remove upper triangle form r, p, and n
mturk_corr2$r[upper.tri(mturk_corr2$r)] <- "-"
mturk_corr2$P[upper.tri(mturk_corr2$P)] <- "-"
mturk_corr2$n[upper.tri(mturk_corr2$n)] <- "-"

# show corr table with flattenCorr

kable(flattenCorrMatrix(mturk_corr$r, mturk_corr$P), caption = "MTURK Study - Correlation: Flat") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# print tables using kable
kable(as.data.frame(format(mturk_corr2$r, scientific = FALSE)), caption = "MTURK Study - Correlation: r values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()
  

kable(as.data.frame(format(mturk_corr2$P, scientific = FALSE)), caption = "MTURK Study - Correlation: p values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

kable(as.data.frame(format(mturk_corr2$n, scientific = FALSE)), caption = "MTURK Study - Correlation: n values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# create new matrix for r & p vals with different col names
  # get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
mturk_corr_r <- mturk_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  
mturk_corr_p <- mturk_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(mturk_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")


# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")

# all vars included
corr_plot_mturk_all <- {
  corrplot(mturk_corr$r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_corr$P, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .5, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}


# with all SAD subscales
corr_plot_mturk_ques_all <- {
  corrplot(mturk_corr_r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_corr_p, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# final -- with SAD overall only
corr_plot_mturk_ques_final <- {
  corrplot(mturk_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_all(oct28)", plot = replayPlot(corr_plot_mturk_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ques_all(oct28)", plot = replayPlot(corr_plot_mturk_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ques_final(oct28)", plot = replayPlot(corr_plot_mturk_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

# export csv files of R values & p values 
  write.csv(mturk_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_corr_rvals(oct28).csv", row.names = T)
  
  write.csv(mturk_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_corr_pvals(oct28).csv", row.names = T)
```


## Regressions

A regression analysis was completed to investigate which measure was a
significant predictor of nomophobia (NMPQ) and smartphone dependency
(SAD: Dep.).

The Linear models were as follows:

-   $NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$
-   $SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$
-   $SAD: Dep. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$
-   $SAD: Access. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$
-   $SAD: EA = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$
-   $SAD: Dist. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

### All Data

#### Study 1: SONA

##### NMPQ

This regression used the following formula:
$NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_NMPQ)$fstatistic[2])`, `r as.numeric(summary(sona_reg_NMPQ)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_NMPQ)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_NMPQ)$fstatistic[1], summary(sona_reg_NMPQ)$fstatistic[2], summary(sona_reg_NMPQ)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_NMPQ)$r.squared, 2)`

```{r}
# run lm
sona_reg_NMPQ <- lm(data = SONA_data, formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_NMPQ)

# use this to plot assumption plots (e.g., q-q)
# plot(sona_reg_NMPQ)

# summary(lm(data = SONA_data, formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum + SSEIT_sum + SRS_sum))

# create table depicting main stats to export later
sona_reg_NMPQ_t <- 
      rbind(tidy(sona_reg_NMPQ), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_NMPQ)$fstatistic[2], summary(sona_reg_NMPQ)$r.squared, nrow(sona_reg_NMPQ$model)), 
             "std.error" = c(summary(sona_reg_NMPQ)$fstatistic[3], summary(sona_reg_NMPQ)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_NMPQ)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_NMPQ)$fstatistic[1], summary(sona_reg_NMPQ)$fstatistic[2], summary(sona_reg_NMPQ)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_NMPQ_t, caption = "Multiple Regression Model Predicting NMPQ Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
# reg_colours <- c("#7E1044", "#170FA7", "black", "red", "purple", "grey", "yellow")
# reg_colours <- c("#98410B", "#F0803C", "#DDA448", "#858585", "#3F826D", "#08605F", "#414535") # orange >green
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_NMPQ_plot <- 
  sjPlot::plot_model(model = sona_reg_NMPQ, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     #axis.lim = c(-.25,.25)
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_NMPQ_plot

# this plots the non-standardized betas
  # sjPlot::plot_model(sona_reg_NMPQ, type = "est") #ither otpions: c("est", "eff", "pred", "int", "std", "std2", "slope", "resid", "diag")
# plot the table using sjPlot
  # sjPlot::tab_model(sona_reg_NMPQ, show.stat = T, show.std = T)

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_NMPQ_plot(oct28)", plot = sona_reg_NMPQ_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_NMPQ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_NMPQ_t(oct28).csv", row.names = T)
```

```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# test out the tab_model() fn in sjPlot package
  # cant seem to get it to actually print out the table in r notebook
  # will only print out in "viewer" window... 
test_tabmodel <- sjPlot::tab_model(lm(data = SONA_data, formula = NMPQ_sum ~ age + age_first_phone + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum), show.stat = T)
```

##### SAD - Overall

This regression used the following formula:
$SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_SAD)$fstatistic[2])`, `r as.numeric(summary(sona_reg_SAD)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_SAD)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_SAD)$fstatistic[1], summary(sona_reg_SAD)$fstatistic[2], summary(sona_reg_SAD)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_SAD)$r.squared, 2)`

```{r}
# run lm
sona_reg_SAD <- lm(data = SONA_data, formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_SAD)

# create table depicting main stats to export later
sona_reg_SAD_t <- 
      rbind(tidy(sona_reg_SAD), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_SAD)$fstatistic[2], summary(sona_reg_SAD)$r.squared, nrow(sona_reg_SAD$model)), 
             "std.error" = c(summary(sona_reg_SAD)$fstatistic[3], summary(sona_reg_SAD)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_SAD)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_SAD)$fstatistic[1], summary(sona_reg_SAD)$fstatistic[2], summary(sona_reg_SAD)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_SAD_t, caption = "Multiple Regression Model Predicting SAD Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_SAD_plot <- 
  sjPlot::plot_model(model = sona_reg_SAD, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_SAD_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_plot(oct28)", plot = sona_reg_SAD_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_SAD_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_t(oct28).csv", row.names = T)
```

##### SAD - Dependency

This regression used the following formula:
$SAD: Dep = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_SAD_dep)$fstatistic[2])`, `r as.numeric(summary(sona_reg_SAD_dep)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_SAD_dep)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_SAD_dep)$fstatistic[1], summary(sona_reg_SAD_dep)$fstatistic[2], summary(sona_reg_SAD_dep)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_SAD_dep)$r.squared, 2)`

```{r}
# run lm
sona_reg_SAD_dep <- lm(data = SONA_data, formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_SAD_dep)

# create table depicting main stats to export later
sona_reg_SAD_dep_t <- 
      rbind(tidy(sona_reg_SAD_dep), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_SAD_dep)$fstatistic[2], summary(sona_reg_SAD_dep)$r.squared, nrow(sona_reg_SAD_dep$model)), 
             "std.error" = c(summary(sona_reg_SAD_dep)$fstatistic[3], summary(sona_reg_SAD_dep)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_SAD_dep)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_SAD_dep)$fstatistic[1], summary(sona_reg_SAD_dep)$fstatistic[2], summary(sona_reg_SAD_dep)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_SAD_dep_t, caption = "Multiple Regression Model Predicting SAD_dep Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_SAD_dep_plot <- 
  sjPlot::plot_model(model = sona_reg_SAD_dep, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_SAD_dep_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_dep_plot(oct28)", plot = sona_reg_SAD_dep_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_SAD_dep_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_dep_t(oct28).csv", row.names = T)
```

##### SAD - Accessibility

This regression used the following formula:
$SAD: Access. = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_SAD_access)$fstatistic[2])`, `r as.numeric(summary(sona_reg_SAD_access)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_SAD_access)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_SAD_access)$fstatistic[1], summary(sona_reg_SAD_access)$fstatistic[2], summary(sona_reg_SAD_access)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_SAD_access)$r.squared, 2)`

```{r}
# run lm
sona_reg_SAD_access <- lm(data = SONA_data, formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_SAD_access)

# create table depicting main stats to export later
sona_reg_SAD_access_t <- 
      rbind(tidy(sona_reg_SAD_access), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_SAD_access)$fstatistic[2], summary(sona_reg_SAD_access)$r.squared, nrow(sona_reg_SAD_access$model)), 
             "std.error" = c(summary(sona_reg_SAD_access)$fstatistic[3], summary(sona_reg_SAD_access)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_SAD_access)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_SAD_access)$fstatistic[1], summary(sona_reg_SAD_access)$fstatistic[2], summary(sona_reg_SAD_access)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_SAD_access_t, caption = "Multiple Regression Model Predicting SAD_access Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_SAD_access_plot <- 
  sjPlot::plot_model(model = sona_reg_SAD_access, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_SAD_access_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_access_plot(oct28)", plot = sona_reg_SAD_access_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_SAD_access_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_access_t(oct28).csv", row.names = T)
```

##### SAD - Emotional Attachement

This regression used the following formula:
$SAD: EA = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_SAD_ea)$fstatistic[2])`, `r as.numeric(summary(sona_reg_SAD_ea)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_SAD_ea)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_SAD_ea)$fstatistic[1], summary(sona_reg_SAD_ea)$fstatistic[2], summary(sona_reg_SAD_ea)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_SAD_ea)$r.squared, 2)`

```{r}
# run lm
sona_reg_SAD_ea <- lm(data = SONA_data, formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_SAD_ea)

# create table depicting main stats to export later
sona_reg_SAD_ea_t <- 
      rbind(tidy(sona_reg_SAD_ea), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_SAD_ea)$fstatistic[2], summary(sona_reg_SAD_ea)$r.squared, nrow(sona_reg_SAD_ea$model)), 
             "std.error" = c(summary(sona_reg_SAD_ea)$fstatistic[3], summary(sona_reg_SAD_ea)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_SAD_ea)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_SAD_ea)$fstatistic[1], summary(sona_reg_SAD_ea)$fstatistic[2], summary(sona_reg_SAD_ea)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_SAD_ea_t, caption = "Multiple Regression Model Predicting SAD_ea Score", align = rep('lcccc'), digits = 4) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_SAD_ea_plot <- 
  sjPlot::plot_model(model = sona_reg_SAD_ea, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_SAD_ea_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_ea_plot(oct28)", plot = sona_reg_SAD_ea_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_SAD_ea_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_ea_t(oct28).csv", row.names = T)
```

##### SAD - Distractibility

This regression used the following formula:
$SAD: Dist = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(sona_reg_SAD_dist)$fstatistic[2])`, `r as.numeric(summary(sona_reg_SAD_dist)$fstatistic[3])`) = `r as.numeric(summary(sona_reg_SAD_dist)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(sona_reg_SAD_dist)$fstatistic[1], summary(sona_reg_SAD_dist)$fstatistic[2], summary(sona_reg_SAD_dist)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(sona_reg_SAD_dist)$r.squared, 2)`

```{r}
# run lm
sona_reg_SAD_dist <- lm(data = SONA_data, formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(sona_reg_SAD_dist)

# create table depicting main stats to export later
sona_reg_SAD_dist_t <- 
      rbind(tidy(sona_reg_SAD_dist), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(sona_reg_SAD_dist)$fstatistic[2], summary(sona_reg_SAD_dist)$r.squared, nrow(sona_reg_SAD_dist$model)), 
             "std.error" = c(summary(sona_reg_SAD_dist)$fstatistic[3], summary(sona_reg_SAD_dist)$adj.r.squared, NA), 
             "statistic" = c(summary(sona_reg_SAD_dist)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(sona_reg_SAD_dist)$fstatistic[1], summary(sona_reg_SAD_dist)$fstatistic[2], summary(sona_reg_SAD_dist)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(sona_reg_SAD_dist_t, caption = "Multiple Regression Model Predicting SAD_dist Score", align = rep('lcccc'), digits = 4) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
sona_reg_SAD_dist_plot <- 
  sjPlot::plot_model(model = sona_reg_SAD_dist, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (SONA)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
sona_reg_SAD_dist_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/sona_reg_SAD_dist_plot(oct28)", plot = sona_reg_SAD_dist_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)

# export csv files of lm table
  write.csv(sona_reg_SAD_dist_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_reg_SAD_dist_t(oct28).csv", row.names = T)
```

<!-- ##### Visualizations: Panels -->

<!-- ###### NMPQ & SAD -->

```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm -- NMPQ
  # plot standardized betas to compare coefficients
sona_reg_NMPQ_plot_pan <- 
  sjPlot::plot_model(model = sona_reg_NMPQ, # lm output 
                     title = c("(A) NMPQ"),
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     #axis.lim = c(-.25,.25)
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# plot lm -- SAD
  # plot standardized betas to compare coefficients
sona_reg_SAD_plot_pan <- 
  sjPlot::plot_model(model = sona_reg_SAD, # lm output 
                     title = c("(B) SAD"),
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# create figure caption
scatall_note_sona_reg_NMPQ_SAD_plot_pan = "Note: add note here... "
scatall_note_sona_reg_NMPQ_SAD_plot_pan = paste0(strwrap(scatall_note_sona_reg_NMPQ_SAD_plot_pan, width = 90), collapse = "\n")

# make fig
sona_reg_NMPQ_SAD_plot_pan <- 
  grid.arrange(sona_reg_NMPQ_plot_pan, sona_reg_SAD_plot_pan,
               nrow = 1,
               top = text_grob("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ & SAD Score (SONA)",
                               face = "bold", size = 10), # show text in bold
               bottom = text_grob(scatall_note_sona_reg_NMPQ_SAD_plot_pan,
                                  hjust = 0, # specify horizontal justification
                                  x = 0.05, # specify orientation on x-axis (bottom L = 0)
                                  face = "italic") # show text in italics
               )

# show fig
sona_reg_NMPQ_SAD_plot_pan

# export plots
  # remove caption & title if needed before exporting
# ggsave(filename="anova_violin_DSGR2_option2", plot = anova_violin_DSGR2, device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
```

#### Study 2: Mturk

##### NMPQ

This regression used the following formula:
$NMPQ = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_NMPQ)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_NMPQ)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_NMPQ)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_NMPQ)$fstatistic[1], summary(mturk_reg_NMPQ)$fstatistic[2], summary(mturk_reg_NMPQ)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_NMPQ)$r.squared, 2)`

```{r}
# run lm
mturk_reg_NMPQ <- lm(data = Mturk_data, formula = NMPQ_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_NMPQ)

# create table depicting main stats to export later
mturk_reg_NMPQ_t <- 
      rbind(tidy(mturk_reg_NMPQ), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_NMPQ)$fstatistic[2], summary(mturk_reg_NMPQ)$r.squared, nrow(mturk_reg_NMPQ$model)), 
             "std.error" = c(summary(mturk_reg_NMPQ)$fstatistic[3], summary(mturk_reg_NMPQ)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_NMPQ)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_NMPQ)$fstatistic[1], summary(mturk_reg_NMPQ)$fstatistic[2], summary(mturk_reg_NMPQ)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_NMPQ_t, caption = "Multiple Regression Model Predicting NMPQ Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_NMPQ_plot <- 
  sjPlot::plot_model(model = mturk_reg_NMPQ, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_NMPQ_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_NMPQ_plot(oct28)", plot = mturk_reg_NMPQ_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_NMPQ_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_NMPQ_t(oct28).csv", row.names = T)
```

##### SAD - Overall

This regression used the following formula:
$SAD = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_SAD)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_SAD)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_SAD)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_SAD)$fstatistic[1], summary(mturk_reg_SAD)$fstatistic[2], summary(mturk_reg_SAD)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_SAD)$r.squared, 2)`

```{r}
# run lm
mturk_reg_SAD <- lm(data = Mturk_data, formula = SAD_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_SAD)

# create table depicting main stats to export later
mturk_reg_SAD_t <- 
      rbind(tidy(mturk_reg_SAD), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_SAD)$fstatistic[2], summary(mturk_reg_SAD)$r.squared, nrow(mturk_reg_SAD$model)), 
             "std.error" = c(summary(mturk_reg_SAD)$fstatistic[3], summary(mturk_reg_SAD)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_SAD)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_SAD)$fstatistic[1], summary(mturk_reg_SAD)$fstatistic[2], summary(mturk_reg_SAD)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_SAD_t, caption = "Multiple Regression Model Predicting SAD Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_SAD_plot <- 
  sjPlot::plot_model(model = mturk_reg_SAD, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_SAD_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_plot(oct28)", plot = mturk_reg_SAD_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_SAD_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_t(oct28).csv", row.names = T)
```

##### SAD - Dependency

This regression used the following formula:
$SAD_dep = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_SAD_dep)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_SAD_dep)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_SAD_dep)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_SAD_dep)$fstatistic[1], summary(mturk_reg_SAD_dep)$fstatistic[2], summary(mturk_reg_SAD_dep)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_SAD_dep)$r.squared, 2)`

```{r}
# run lm
mturk_reg_SAD_dep <- lm(data = Mturk_data, formula = SAD_dep_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_SAD_dep)

# create table depicting main stats to export later
mturk_reg_SAD_dep_t <- 
      rbind(tidy(mturk_reg_SAD_dep), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_SAD_dep)$fstatistic[2], summary(mturk_reg_SAD_dep)$r.squared, nrow(mturk_reg_SAD_dep$model)), 
             "std.error" = c(summary(mturk_reg_SAD_dep)$fstatistic[3], summary(mturk_reg_SAD_dep)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_SAD_dep)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_SAD_dep)$fstatistic[1], summary(mturk_reg_SAD_dep)$fstatistic[2], summary(mturk_reg_SAD_dep)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_SAD_dep_t, caption = "Multiple Regression Model Predicting SAD_dep Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_SAD_dep_plot <- 
  sjPlot::plot_model(model = mturk_reg_SAD_dep, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_SAD_dep_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_dep_plot(oct28)", plot = mturk_reg_SAD_dep_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_SAD_dep_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_dep_t(oct28).csv", row.names = T)
```

##### SAD - Accessibility

This regression used the following formula:
$SAD_access = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_SAD_access)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_SAD_access)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_SAD_access)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_SAD_access)$fstatistic[1], summary(mturk_reg_SAD_access)$fstatistic[2], summary(mturk_reg_SAD_access)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_SAD_access)$r.squared, 2)`

```{r}
# run lm
mturk_reg_SAD_access <- lm(data = Mturk_data, formula = SAD_access_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_SAD_access)

# create table depicting main stats to export later
mturk_reg_SAD_access_t <- 
      rbind(tidy(mturk_reg_SAD_access), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_SAD_access)$fstatistic[2], summary(mturk_reg_SAD_access)$r.squared, nrow(mturk_reg_SAD_access$model)), 
             "std.error" = c(summary(mturk_reg_SAD_access)$fstatistic[3], summary(mturk_reg_SAD_access)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_SAD_access)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_SAD_access)$fstatistic[1], summary(mturk_reg_SAD_access)$fstatistic[2], summary(mturk_reg_SAD_access)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_SAD_access_t, caption = "Multiple Regression Model Predicting SAD_access Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_SAD_access_plot <- 
  sjPlot::plot_model(model = mturk_reg_SAD_access, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_SAD_access_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_access_plot(oct28)", plot = mturk_reg_SAD_access_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_SAD_access_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_access_t(oct28).csv", row.names = T)
```


##### SAD - Emotional Attachment

This regression used the following formula:
$SAD_ea = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_SAD_ea)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_SAD_ea)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_SAD_ea)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_SAD_ea)$fstatistic[1], summary(mturk_reg_SAD_ea)$fstatistic[2], summary(mturk_reg_SAD_ea)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_SAD_ea)$r.squared, 2)`

```{r}
# run lm
mturk_reg_SAD_ea <- lm(data = Mturk_data, formula = SAD_ea_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_SAD_ea)

# create table depicting main stats to export later
mturk_reg_SAD_ea_t <- 
      rbind(tidy(mturk_reg_SAD_ea), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_SAD_ea)$fstatistic[2], summary(mturk_reg_SAD_ea)$r.squared, nrow(mturk_reg_SAD_ea$model)), 
             "std.error" = c(summary(mturk_reg_SAD_ea)$fstatistic[3], summary(mturk_reg_SAD_ea)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_SAD_ea)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_SAD_ea)$fstatistic[1], summary(mturk_reg_SAD_ea)$fstatistic[2], summary(mturk_reg_SAD_ea)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_SAD_ea_t, caption = "Multiple Regression Model Predicting SAD_ea Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_SAD_ea_plot <- 
  sjPlot::plot_model(model = mturk_reg_SAD_ea, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_SAD_ea_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_ea_plot(oct28)", plot = mturk_reg_SAD_ea_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_SAD_ea_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_ea_t(oct28).csv", row.names = T)
```


##### SAD - Distractibility

This regression used the following formula:
$SAD_dist = age + BIS + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SRS + SSEIT + DASS: D + DASS: A + DASS: S$

Results indicate that:

-   over all model was significant, F(`r as.numeric(summary(mturk_reg_SAD_dist)$fstatistic[2])`, `r as.numeric(summary(mturk_reg_SAD_dist)$fstatistic[3])`) = `r as.numeric(summary(mturk_reg_SAD_dist)$fstatistic[1])`, p `r p_round(as.numeric(pf(summary(mturk_reg_SAD_dist)$fstatistic[1], summary(mturk_reg_SAD_dist)$fstatistic[2], summary(mturk_reg_SAD_dist)$fstatistic[3], lower.tail = F)))`, R2 = `r round(summary(mturk_reg_SAD_dist)$r.squared, 2)`

```{r}
# run lm
mturk_reg_SAD_dist <- lm(data = Mturk_data, formula = SAD_dist_sum ~ age + BIS_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SRS_sum + SSEIT_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum)

# show summary of lm
summary(mturk_reg_SAD_dist)

# create table depicting main stats to export later
mturk_reg_SAD_dist_t <- 
      rbind(tidy(mturk_reg_SAD_dist), # use tidy() in broom package
      tibble("term" = c("f_stat", "R2/Adj.R2", "N"), 
             "estimate" = c(summary(mturk_reg_SAD_dist)$fstatistic[2], summary(mturk_reg_SAD_dist)$r.squared, nrow(mturk_reg_SAD_dist$model)), 
             "std.error" = c(summary(mturk_reg_SAD_dist)$fstatistic[3], summary(mturk_reg_SAD_dist)$adj.r.squared, NA), 
             "statistic" = c(summary(mturk_reg_SAD_dist)$fstatistic[1], rep(NA, 2)), 
             "p.value" = c(pf(summary(mturk_reg_SAD_dist)$fstatistic[1], summary(mturk_reg_SAD_dist)$fstatistic[2], summary(mturk_reg_SAD_dist)$fstatistic[3], lower.tail = F), rep(NA, 2))
             )
      )

# remove NA from kable tables
options(knitr.kable.NA = '') 

# show kable table
kable(mturk_reg_SAD_dist_t, caption = "Multiple Regression Model Predicting SAD_dist Score", align = rep('lcccc'), digits =3) %>% 
  kable_paper(full_width = F) %>%
  # add_header_above(c(" ", "mturk" = 2, "Mturk" = 2)) %>%
  # pack_rows("Criterion(s)", 1, 6) %>%
  # # column_spec(1, width = "10em") %>% 
  # pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()

# reset NA vals in kable tables to "NA"
options(knitr.kable.NA = NA) 

# make colour pal
reg_colours <- c("#414535", "#08605F", "#3F826D", "#858585", "#DDA448", "#F0803C", "#98410B" ) # green > orange

# plot lm
  # plot standardized betas to compare cooefficients
mturk_reg_SAD_dist_plot <- 
  sjPlot::plot_model(model = mturk_reg_SAD_dist, # lm output 
                     # title = c("Standardized beta Coefficients for Multiple Regression Model Predicting NMPQ Score (Mturk)"),
                     title = "", # to remove title
                     type = "std", # type "std" = standardized betas
                     #sort.est = T, # sort based on betas (highest on top)
                     wrap.title = 55, # char length of each title line
                     # axis.labels = c("INSERT PRED NAMES")
                     grid.breaks = .25, # x-axis breaks 
                     #se = T # use se instead of CI
                     colors = reg_colours,
                     show.values = T, # show betas
                     show.p = T, # show * for sig p
                     value.offset = 0.40, # offset to beta labels
                     value.size = 3, # size of beta labels
                     digits = 2, # num decimals in beta labels
                     dot.size = 2.5, line.size = 1.5, 
                     vline.color = "black",
                     group.terms = c(1, 2, 3, 3, 3, 3, 3, 4, 5, 6, 7, 7, 7),
                     axis.labels = c("DASS: S", "DASS: A", "DASS: D", "SSEIT", "SRS", "RSES", "NEO: N", "NEO: A", "NEO: E", "NEO: C", "NEO: O", "BIS", "Age"), 
                     axis.title = "Standardized Beta Coefficient", 
                     width = .5
                     ) +
    font_size(offset.x = 10) +
    font_size(base.theme = theme_classic())

# show plot
mturk_reg_SAD_dist_plot

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/mturk_reg_SAD_dist_plot(oct28)", plot = mturk_reg_SAD_dist_plot, device = "jpeg", height = 6.5, width = 5, units = "in", dpi = 1000)
  
# export csv files of lm table
  write.csv(mturk_reg_SAD_dist_t,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_reg_SAD_dist_t(oct28).csv", row.names = T)
```



# Exploratory Analyses: Assessing Screen Time

This data only included ps who reported both having an iPhone (SONA =
`r nrow(SONA_data %>% filter(iphone == "yes"))`; Mturk =
`r nrow(Mturk_data %>% filter(iphone == "yes"))`) and had ST activated
prior to the study (SONA =
`r nrow(SONA_data %>% filter(ST == "had ST"))` ; Mturk =
`r nrow(Mturk_data %>% filter(ST == "had ST"))`) rather than the total
sample (SONA = `r nrow(SONA_data)`; Mturk = `r nrow(Mturk_data)`).

## Assumption Tests

<details>

<summary>

All assumptions were met for the correlation analysis. Click here to see
the assumption tests.

</summary>

The assumptions for a correlation analysis were tested. There were two
assumptions: (1) Linearity and (2) Normality.

1.  Linearity was visually inspected from the scatter plots (see plots
    in the "*Figures: Scatter Plots*" section).

2.  Normality was tested using (a) the Shapiro-Wilk normality test and
    (b) visually inspected using a normality plot.

    -   The Shapiro-Wilk normality test will show two values (i.e., *W*
        and *p*) and each test uses the following hypotheses: H~N~: the
        data are normally distributed and H~A~: the data are not
        normally distributed.

    -   The normality plots used were Q-Q plots (quantile-quantile
        plots), which draw the correlation between a given sample and
        the theoretical normal distribution.

        </summary>

### (a) Shapiro-Wilk Normality Test

<details>

<summary>

The table depicts the results of the Shapiro-Wilks normality test for
each measure. *(click to see code)*

</summary>

```{r message=FALSE, warning=FALSE}
# create a table to show results of all normality tests
corrs_reg_norm_test_sona_ST <- 
  as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D", 
                        "DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"), 
                 rbind(c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_sum)$p.value), # W & p-value for SAD_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$age)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$age)$p.value), # W & p-value for age
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SRS_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SRS_sum)$p.value), # W & p-value for SRS_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$BIS_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$BIS_sum)$p.value), # W & p-value for BIS_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$RSES_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$RSES_sum)$p.value), # W & p-value for RSES_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
                 c(shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum)$statistic, shapiro.test((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
                 )))
  colnames(corrs_reg_norm_test_sona_ST) <- c("Measure", "W", "p")
  # make cols numeric & round to 3 decimals
  corrs_reg_norm_test_sona_ST$W <- as.numeric(corrs_reg_norm_test_sona_ST$W) %>% round(.,3)
  corrs_reg_norm_test_sona_ST$p <- as.numeric(corrs_reg_norm_test_sona_ST$p) %>% round(.,3)

corrs_reg_norm_test_mturk_ST <- 
  as.data.frame(cbind(c("NMPQ", "SAD", "SAD_dep", "SAD_access", "SAD_ea", "SAD_dist", "age", "SRS", "DASS_D", 
                        "DASS_A", "DASS_S", "BIS", "SSEIT", "RSES", "NEO_N", "NEO_E", "NEO_O", "NEO_A", "NEO_C"), 
                 rbind(c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum)$p.value), # W & p-value for NMPQ_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_sum)$p.value), # W & p-value for SAD_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum)$p.value), # W & p-value for SAD_dep_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum)$p.value), # W & p-value for SAD_access_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum)$p.value), # W & p-value for SAD_ea_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum)$p.value), # W & p-value for SAD_dist_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$age)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$age)$p.value), # W & p-value for age
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SRS_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SRS_sum)$p.value), # W & p-value for SRS_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum)$p.value), # W & p-value for DASS_D_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum)$p.value), # W & p-value for DASS_A_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum)$p.value), # W & p-value for DASS_S_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$BIS_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$BIS_sum)$p.value), # W & p-value for BIS_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum)$p.value), # W & p-value for SSEIT_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$RSES_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$RSES_sum)$p.value), # W & p-value for RSES_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum)$p.value), # W & p-value for NEO_N_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum)$p.value), # W & p-value for NEO_E_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum)$p.value), # W & p-value for NEO_O_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum)$p.value), # W & p-value for NEO_A_sum
                 c(shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum)$statistic, shapiro.test((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum)$p.value) # W & p-value for NEO_C_sum
                 )))
  colnames(corrs_reg_norm_test_mturk_ST) <- c("Measure", "W", "p")
  # make cols numeric & round to 3 decimals
  corrs_reg_norm_test_mturk_ST$W <- as.numeric(corrs_reg_norm_test_mturk_ST$W) %>% round(.,3)
  corrs_reg_norm_test_mturk_ST$p <- as.numeric(corrs_reg_norm_test_mturk_ST$p) %>% round(.,3)
  
# join into 1 large table
corrs_reg_norm_tests_ST_t <- cbind(corrs_reg_norm_test_sona_ST, corrs_reg_norm_test_mturk_ST[,2:3])
```

</details>

As seen in the table, most measures showed a significant result,
indicating non-normality. The visual inspection of the Q-Q plots was
completed next.

```{r echo=TRUE, message=FALSE, warning=FALSE}
# show table using kable
kable(corrs_reg_norm_tests_ST_t, caption = "Shapiro-Wilks Normality Test for All Measures -- ST Data only", align = rep('lcccc')) %>% 
  kable_paper(full_width = F) %>%
  add_header_above(c(" ", "SONA" = 2, "Mturk" = 2)) %>%
  pack_rows("Criterion(s)", 1, 6) %>%
  # column_spec(1, width = "10em") %>% 
  pack_rows("Predictor(s)", 7, 19) %>%
  footnote(general = "Normality is met for non-significant p-values (i.e., greater than 0.05).") %>% 
  column_spec(c(4), border_left = T) %>% 
  kable_classic()
```

### (b) Q-Q Plots

<details>

<summary>

Overall, the Q-Q plots show the data for all measures except age and age
of first smartphone did fall along or close to the line. The age
measures were not expected to be normally distributed; therefore, the
analysis was completed for each measure. *(click to see code)*

</summary>

```{r echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE}
# SONA - All
grid.arrange(ggqqplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, ylab = "NMPQ"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_sum, ylab = "SAD"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum, ylab = "SAD_dep"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum, ylab = "SAD_access"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum, ylab = "SAD_ea"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum, ylab = "SAD_dist"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$age, ylab = "age"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SRS_sum, ylab = "SRS"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum, ylab = "DASS_D"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum, ylab = "DASS_A"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum, ylab = "DASS_S"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$BIS_sum, ylab = "BIS"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum, ylab = "SSEIT"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$RSES_sum, ylab = "RSES"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum, ylab = "NEO_N"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum, ylab = "NEO_E"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum, ylab = "NEO_O"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum, ylab = "NEO_A"),
             ggqqplot((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum, ylab = "NEO_C"),
             nrow = 4,
             top = text_grob("Q-Q Plots For SONA - ST", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to the line.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
# for MTURK - ALL
grid.arrange(ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, ylab = "NMPQ"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_sum, ylab = "SAD"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum, ylab = "SAD_dep"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum, ylab = "SAD_access"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum, ylab = "SAD_ea"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum, ylab = "SAD_dist"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$age, ylab = "age"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SRS_sum, ylab = "SRS"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum, ylab = "DASS_D"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum, ylab = "DASS_A"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum, ylab = "DASS_S"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$BIS_sum, ylab = "BIS"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum, ylab = "SSEIT"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$RSES_sum, ylab = "RSES"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum, ylab = "NEO_N"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum, ylab = "NEO_E"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum, ylab = "NEO_O"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum, ylab = "NEO_A"),
             ggqqplot((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum, ylab = "NEO_C"),
             nrow = 4,
             top = text_grob("Q-Q Plots For Mturk - ST", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to the line.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
```

</details>

### (c) Histogram Plots

<details>

<summary>

Overall, the Q-Q plots show the data for all measures except age and age
of first smartphone did fall along or close to the line. The age
measures were not expected to be normally distributed; therefore, the
analysis was completed for each measure. *(click to see code)*

</summary>

```{r echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE}
# SONA - ST
grid.arrange(qplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
               # qplot((SONA_data %>% filter(ST == "had ST"))$NMPQ_sum, geom = "density"),
             qplot((SONA_data %>% filter(ST == "had ST"))$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((SONA_data %>% filter(ST == "had ST"))$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             nrow = 4,
             top = text_grob("Histogram For SONA Data - ST", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
# for MTURK - ST
grid.arrange(qplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, main = "NMPQ", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
               # qplot((Mturk_data %>% filter(ST == "had ST"))$NMPQ_sum, geom = "density"),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_sum, main = "SAD", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dep_sum, main = "SAD_dep", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_access_sum, main = "SAD_access", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_ea_sum, main = "SAD_ea", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SAD_dist_sum, main = "SAD_dist", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$age, main = "age", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SRS_sum, main = "SRS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_D_sum, main = "DASS_D", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_A_sum, main = "DASS_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$DASS_S_sum, main = "DASS_S", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$BIS_sum, main = "BIS", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$SSEIT_sum, main = "SSEIT", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$RSES_sum, main = "RSES", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_N_sum, main = "NEO_N", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_E_sum, main = "NEO_E", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_O_sum, main = "NEO_O", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_A_sum, main = "NEO_A", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             qplot((Mturk_data %>% filter(ST == "had ST"))$NEO_C_sum, main = "NEO_C", ylab = "freq", xlab = "Score", binwidth = 1) + theme_classic() + theme(axis.text = element_text(colour = "black")),
             nrow = 4,
             top = text_grob("Histogram For Mturk Data - ST", 
                             face = "bold"), 
             bottom = text_grob("Normality is met for plots were data falls along or close to a normal curve.", 
                                face = "italic", 
                                x = 0.05, 
                                hjust = 0)
             )
```

</details>


### Correlations

This data only included ps who reported both having an iPhone (SONA =
`r nrow(SONA_data %>% filter(iphone == "yes"))`; Mturk =
`r nrow(Mturk_data %>% filter(iphone == "yes"))`) and had ST activated
prior to the study (SONA =
`r nrow(SONA_data %>% filter(ST == "had ST"))` ; Mturk =
`r nrow(Mturk_data %>% filter(ST == "had ST"))`) rather than the total
sample (SONA = `r nrow(SONA_data)`; Mturk = `r nrow(Mturk_data)`).

#### Study 1: SONA

```{r }
sona_ST_corr <- 
  SONA_data %>% 
  filter(ST == "had ST") %>% 
  # select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
  select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, 
         NMPQ_sum, 
         SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
         BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>% 
  as.matrix() %>% 
  Hmisc::rcorr(type = c("pearson"))

# create new pilot_corr to shown only lower triangle... 
sona_ST_corr2 <- sona_ST_corr
# round to 4 decimals... 
sona_ST_corr2$r <- round(sona_ST_corr2$r, 4)
sona_ST_corr2$P <- round(sona_ST_corr2$P, 4)
sona_ST_corr2$n <- round(sona_ST_corr2$n, 4)
# remove upper triangle form r, p, and n
sona_ST_corr2$r[upper.tri(sona_ST_corr2$r)] <- "-"
sona_ST_corr2$P[upper.tri(sona_ST_corr2$P)] <- "-"
sona_ST_corr2$n[upper.tri(sona_ST_corr2$n)] <- "-"

# show corr table with flattenCorr

kable(flattenCorrMatrix(sona_ST_corr$r, sona_ST_corr$P), caption = "SONA Study - ST - Correlation: Flat") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# print tables using kable
kable(as.data.frame(format(sona_ST_corr2$r, scientific = FALSE)), caption = "SONA Study - ST - Correlation: r values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()
  

kable(as.data.frame(format(sona_ST_corr2$P, scientific = FALSE)), caption = "SONA Stud - STy - Correlation: p values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

kable(as.data.frame(format(sona_ST_corr2$n, scientific = FALSE)), caption = "SONA Study - ST - Correlation: n values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# create new matrix for r & p vals with different col names
  # get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
sona_ST_corr_r <- sona_ST_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  
sona_ST_corr_p <- sona_ST_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(sona_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")


# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")

# all vars included
corr_plot_sona_ST_all <- {
  corrplot(sona_ST_corr$r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_ST_corr$P, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .5, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}


# with all SAD subscales
corr_plot_sona_ST_ques_all <- {
  corrplot(sona_ST_corr_r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_ST_corr_p, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# final -- with SAD overall only
corr_plot_sona_ST_ques_final <- {
  corrplot(sona_ST_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = sona_ST_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_all(oct28)", plot = replayPlot(corr_plot_sona_ST_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_ques_all(oct28)", plot = replayPlot(corr_plot_sona_ST_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_sona_ST_ques_final(oct28)", plot = replayPlot(corr_plot_sona_ST_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

# export csv files of R values & p values 
  write.csv(sona_ST_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_ST_corr_rvals(oct28).csv", row.names = T)
  write.csv(sona_ST_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/sona_ST_corr_pvals(oct28).csv", row.names = T)
```

#### Study 2: Mturk

```{r }
mturk_ST_corr <- 
  Mturk_data %>% 
  filter(ST == "had ST") %>% 
  # select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, ends_with("_sum")) %>%
  select(age, age_first_phone, com_gen:com_room_task, dist_daily, dist_study, 
         NMPQ_sum, 
         SAD_sum, SAD_dep_sum, SAD_ea_sum, SAD_access_sum, SAD_dist_sum, # add or remove the SAD subscales / overall
         BIS_sum, NEO_O_sum, NEO_C_sum, NEO_E_sum, NEO_A_sum, NEO_N_sum, RSES_sum, SRS_sum, SSEIT_sum, DASS_D_sum, DASS_A_sum, DASS_S_sum) %>% 
  as.matrix() %>% 
  Hmisc::rcorr(type = c("pearson"))

# create new pilot_corr to shown only lower triangle... 
mturk_ST_corr2 <- mturk_ST_corr
# round to 4 decimals... 
mturk_ST_corr2$r <- round(mturk_ST_corr2$r, 4)
mturk_ST_corr2$P <- round(mturk_ST_corr2$P, 4)
mturk_ST_corr2$n <- round(mturk_ST_corr2$n, 4)
# remove upper triangle form r, p, and n
mturk_ST_corr2$r[upper.tri(mturk_ST_corr2$r)] <- "-"
mturk_ST_corr2$P[upper.tri(mturk_ST_corr2$P)] <- "-"
mturk_ST_corr2$n[upper.tri(mturk_ST_corr2$n)] <- "-"

# show corr table with flattenCorr

kable(flattenCorrMatrix(mturk_ST_corr$r, mturk_ST_corr$P), caption = "MTURK Study - ST - Correlation: Flat") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# print tables using kable
kable(as.data.frame(format(mturk_ST_corr2$r, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: r values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()
  

kable(as.data.frame(format(mturk_ST_corr2$P, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: p values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

kable(as.data.frame(format(mturk_ST_corr2$n, scientific = FALSE)), caption = "MTURK Study - ST - Correlation: n values") %>%
  kable_styling(bootstrap_options = c("hover", "striped"), full_width = F) %>% 
  kable_classic()

# create new matrix for r & p vals with different col names
  # get age, age_first_phone, dist_daily, and all ques data (21 = SAD overall--not used)
mturk_ST_corr_r <- mturk_ST_corr$r[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  
mturk_ST_corr_p <- mturk_ST_corr$P[c(10:15, 1, 16:27),c(10:15, 1, 16:27)]
  colnames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")
  rownames(mturk_ST_corr_r) <- c("NMPQ", "SAD", "SAD: Dep.", "SAD: EA", "SAD: A", "SAD: Dist.", "Age", "BIS", "NEO: O", "NEO: C", "NEO: E", "NEO: A", "NEO: N", "RSES", "SRS", "SSEIT", "DASS: D", "DASS: A", "DASS: S")


# create colour pallet for corr matrix
# corr_colours <- c("#7E1044", "#981570", "#F3F5F7", "#491296", "#170FA7")
corr_colours <- c("#98410B", "#F0803C", "#DDA448", "#F7F7FF", "#3F826D", "#08605F", "#414535")

# all vars included
corr_plot_mturk_ST_all <- {
  corrplot(mturk_ST_corr$r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_ST_corr$P, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .5, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}


# with all SAD subscales
corr_plot_mturk_ST_ques_all <- {
  corrplot(mturk_ST_corr_r, method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_ST_corr_p, sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# final -- with SAD overall only
corr_plot_mturk_ST_ques_final <- {
  corrplot(mturk_ST_corr_r[c(1,2,7:19), c(1,2,7:19)], method = 'color',
         col = (colorRampPalette(corr_colours)(5000)),
         # type = "upper",
         diag = F, # remove central diagonal
         tl.col = "black", tl.srt = 90, tl.cex = .8, #Text label color and rotation
         addgrid.col = NA, # add grid colour
         # addCoef.col = "black", # Add coefficient of correlation
         # addCoefasPercent = T # add as coefficient as percent
         # number.cex = .25, # size of the coeff
         # number.digits = 2, # num decimals in coeff
         p.mat = mturk_ST_corr_p[c(1,2,7:19), c(1,2,7:19)], sig.level = c(.001, .01, .05),
         insig = "label_sig", pch.cex = .75, pch.col = "black"
         # insig = "blank"# insig = "pch", pch = 10, pch.col = "red", pch.cex = .1, # add this instead of insig above to denot insig p values with red dot
         )
  recordPlot()
}

# export corr matrix using ggsave() & replayPlot() to call on previously made plot
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_all(oct28)", plot = replayPlot(corr_plot_mturk_ST_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_ques_all(oct28)", plot = replayPlot(corr_plot_mturk_ST_ques_all), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)
ggsave(filename="/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Figures/corr_plot_mturk_ST_ques_final(oct28)", plot = replayPlot(corr_plot_mturk_ST_ques_final), device = "jpeg", height = 5, width = 8, units = "in", dpi = 1000)

# export csv files of R values & p values 
  write.csv(mturk_ST_corr$r,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_ST_corr_rvals(oct28).csv", row.names = T)
  write.csv(mturk_ST_corr$P,"/Users/anaceciliaruizpardo/RStudio/Smartphone_study/Study 3- Individual Differences/Dissertation (10:18:21)/Excel Outputs/mturk_ST_corr_pvals(oct28).csv", row.names = T)
```


### Regressions

This data only included ps who reported both having an iPhone (SONA =
`r nrow(SONA_data %>% filter(iphone == "yes"))`; Mturk =
`r nrow(Mturk_data %>% filter(iphone == "yes"))`) and had ST activated
prior to the study (SONA =
`r nrow(SONA_data %>% filter(ST == "had ST"))` ; Mturk =
`r nrow(Mturk_data %>% filter(ST == "had ST"))`) rather than the total
sample (SONA = `r nrow(SONA_data)`; Mturk = `r nrow(Mturk_data)`).

*Note: Since this was an exploratory analysis, the predictor of "age of
first phone" and other ST-related continuous variables (i.e., total
weekly ST, daily pickups, daily notifications) were added to each
model.*

#### Study 1: SONA

##### NMPQ

This regression used the following formula:
$NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 1.84, p = .03, R2 = .17

-   sig predictors:

    -   NEO: O, t(156) = -2.36, p = .02
    -   NEO: N, t(156) = 2.01, p = .05 (.0458)

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum))
```

##### SAD

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 3.78, p = .03, R2 = .29

-   sig predictors:

    -   ST Hours, t(156) = 2.17, p = .03
    -   DAS: S, t(156) = 1.99, p = .05 (.048)
    -   NEO: A, t(156) = -2.36, p = .02
    -   NEO: N, t(156) = 2.11, p = .04
    -   SSEIT, t(156) = 3.30, p = .001

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_sum ~ ST_weekly_tot_hours + DASS_S_sum + NEO_A_sum + NEO_N_sum + SSEIT_sum))
```

##### SAD: Dependence

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 3.78, p = .03, R2 = .29

-   sig predictors:

    -   NEO: O, t(156) = -2.58, p = .01
    -   NEO: C, t(156) = -2.13, p = .04
    -   SSEIT, t(156) = -2.79, p = .006

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ NEO_O_sum + NEO_C_sum + SSEIT_sum))
```

##### SAD: Accessibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 1.91, p = .02, R2 = .17

-   sig predictors:

    -   Age first phone, t(156) = -2.10, p = .04
    -   SSEIT, t(156) = 2.23, p = .03

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age_first_phone + SSEIT_sum))
```

##### SAD: Emotional Attachment

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 3.74, p \< .001, R2 = .29

-   sig predictors:

    -   ST Hours, t(156) = 2.14, p = .03
    -   DAS: S, t(156) = 2.51, p = .01
    -   NEO: A, t(156) = -2.74, p = .007
    -   SSEIT, t(156) = 2.37, p = .02

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ ST_weekly_tot_hours + DASS_S_sum + NEO_A_sum + SSEIT_sum))
```

##### SAD: Distractibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 156) = 2.64, p \< .001, R2 = .22

-   sig predictors:

    -   ST Hours, t(156) = 1.95, p = .05 (almost sig)
    -   NEO: N, t(156) = 2.05, p = .04
    -   SSEIT, t(156) = 2.16, p = .03

```{r}
# run lm + give summary
summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ NEO_N_sum + SSEIT_sum))
```

#### Study 2: Mturk

##### NMPQ

This regression used the following formula:
$NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 12.24, p \< .001, R2 = .59

-   sig predictors:

    -   Age, t(143) = -2.42, p = .02
    -   ST: Pickups, t(143) = -1.96, p = .05 (almost sig)
    -   DASS: D, t(143) = 2.70, p = .008
    -   NEO: N, t(143) = 2.86, p = .005
    -   RSES, t(143) = -2.54, p = .01
    -   SSEIT, t(143) = 3.57, p \< .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = NMPQ_sum ~ age + ST_daily_pickups + DASS_D_sum + NEO_N_sum + RSES_sum + SSEIT_sum))
```

##### SAD

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 17.02, p \< .001, R2 = .67

-   sig predictors:

    -   Age, t(143) = -2.58, p = .01
    -   ST Pickups, t(143) = -2.62, p = .01 (.0099)
    -   NEO: E, t(143) = -1.96, p = .05 (almost sig)
    -   NEO: N, t(143) = 3.82, p \< .001
    -   SSEIT, t(143) = 3.99, p \< .001
    -   SRS, t(143) = -2.09, p = .04

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_sum ~ age + ST_daily_pickups + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

##### SAD: Dependence

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 8.13, p \< .001, R2 = .49

-   sig predictors:

    -   Age, t(143) = -2.59, p = .01
    -   NEO: N, t(143) = 3.11, p = .002
    -   SSEIT, t(143) = 2.16, p = .03

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dep_sum ~ age + NEO_N_sum + SSEIT_sum))
```

##### SAD: Accessibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 6.44, p \< .001, R2 = .43

-   sig predictors:

    -   Age, t(143) = -2.02, p = .05 (.046)
    -   ST Pickups, t(143) = -2.96, p = .004
    -   NEO: N, t(143) = 2.39, p = .02

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_access_sum ~ age + ST_daily_pickups + NEO_N_sum))
```

##### SAD: Emotional Attachment

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 14.03, p \< .001, R2 = .63

-   sig predictors:

    -   BIS, t(143) = -2.11, p = .04
    -   DASS: A, t(143) = 3.11, p = .002
    -   NEO: E, t(143) = 2.54, p = .01
    -   NEO: N, t(143) = 3.25, p = .002
    -   SSEIT, t(143) = 4.74, p \< .001
    -   SRS, t(143) = -2.93, p = .004

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_ea_sum ~ BIS_sum + DASS_A_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

##### SAD: Distractibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(17, 143) = 18.11, p \< .001, R2 = .68

-   sig predictors:

    -   DASS: A, t(143) = 3.20, p = .002
    -   NEO: E, t(143) = 1.94, p = .05 (almost sig, .055)
    -   SSEIT, t(143) = 2.24, p = .03
    -   SRS, t(143) = -4.66, p \< .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ age + age_first_phone + ST_weekly_tot_hours + ST_daily_pickups + ST_daily_not + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data %>% filter(ST == "had ST"), formula = SAD_dist_sum ~ NEO_N_sum + SSEIT_sum))
```

# Exploratory Analyses: Including Age of First Smartphone Predictor

## All Data

### Study 1: SONA

#### NMPQ

This regression used the following formula:
$NMPQ = age + BIS + DASS: D + DASS: A + DASS: S + NEO: O + NEO: C + NEO: E + NEO: A + NEO: N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 2.78, p \< .001, R2 = .15

-   sig predictors:

    -   NEO: O, t(225) = -2.19, p = .03
    -   NEO: N, t(225) = 3.10, p = .002
    -   SSEIT, t(225) = 2.52, p = .01
    -   SRS, t(225) = -2.27, p = .02

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = NMPQ_sum ~ age + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = NMPQ_sum ~ NEO_O_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Overall

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 3.74, p \< .001, R2 = .19

-   sig predictors:

    -   NEO: A, t(225) = -2.32, p = .02
    -   NEO: N, t(225) = 2.65, p = .009
    -   SSEIT, t(225) = 4.34, p \< .001
    -   SRS, t(225) = -2.27, p = .02

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = SAD_sum ~ NEO_A_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Dependency

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 2.39, p = .004, R2 = .13

-   sig predictors:

    -   NEO: O, t(225) = -2.93, p = .004
    -   NEO: N, t(225) = 1.94, p = .05 (almost sig)
    -   SSEIT, t(225) = 3.03, p = .003

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = SAD_dep_sum ~  NEO_O_sum + NEO_N_sum + SSEIT_sum))
```

#### SAD - Accessibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 3.25, p \< .001, R2 = .17

-   sig predictors:

    -   Age phone, t(225) = -2.13, p = .03
    -   NEO: N, t(225) = 3.00, p = .003
    -   SSEIT, t(225) = 3.47, p \< .001

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_access_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = SAD_access_sum ~ age_first_phone + NEO_N_sum + SSEIT_sum))
```

#### SAD - Emotional Attachement

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 3.32, p \< .001, R2 = .17

-   sig predictors:

    -   DASS: S, t(225) = 2.28, p = .02
    -   NEO: A, t(225) = -2.85, p = .005
    -   SSEIT, t(225) = 3.23, p = .001

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = SAD_ea_sum ~ DASS_S_sum + NEO_A_sum + SSEIT_sum))
```

#### SAD - Distractibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 225) = 3.18, p \< .001, R2 = .17

-   sig predictors:

    -   SSEIT, t(225) = 3.85, p \< .001
    -   SRS, t(225) = -2.23, p = .03

```{r}
# run lm + give summary
summary(lm(data = SONA_data, formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = SONA_data, formula = SAD_dist_sum ~ SSEIT_sum + SRS_sum))
```

### Study 2: Mturk

#### NMPQ

This regression used the following formula:
$NMPQ = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 29.67, p \< .001, R2 = .46

-   sig predictors: = Age, t(490) = -2.35, p = .02

    -   DASS: A, t(490) = 1.96, p = .05 (almost sig)
    -   NEO: C, t(490) = 3.61, p \< .001
    -   NEO: E, t(490) = 3.14, p = .002
    -   NEO: N, t(490) = 4.06, p \< .001
    -   RSES, t(490) = -1.94, p = .05 (almost sig)
    -   SSEIT, t(490) = 5.49, p \< .001
    -   SRS, t(490) = -3.30, p = .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = NMPQ_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = NMPQ_sum ~ age + DASS_A_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Overall

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 40.42, p \< .001, R2 = .54

-   sig predictors:

    -   Age, t(490) = -2.66, p = .008
    -   DASS: A, t(490) = 3.12, p = .002
    -   NEO: O, t(490) = -2.17, p = .03
    -   NEO: C, t(490) = 2.76, p = .006
    -   NEO: E, t(490) = 3.31, p = .001
    -   NEO: N, t(490) = 3.83, p \< .001
    -   SSEIT, t(490) = 7.03, p \< .001
    -   SRS, t(490) = -6.12, p = .02

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = SAD_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Dependency

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 22.36, p \< .001, R2 = .39

-   sig predictors:

    -   Age, t(490) = -2.18, p = .03
    -   DASS: A, t(490) = 2.53, p = .002
    -   NEO: O, t(490) = -2.13, p = .03
    -   NEO: C, t(490) = 2.71, p = .007
    -   NEO: E, t(490) = 2.64, p = .009
    -   NEO: N, t(490) = 3.06, p = .002
    -   SSEIT, t(490) = 3.99, p \< .001
    -   SRS, t(490) = -3.78, p \< .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_dep_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = SAD_dep_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Accessibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 14.25, p \< .001, R2 = .29

-   sig predictors:

    -   NEO: C, t(490) = 4.00, p \< .001
    -   NEO: E, t(490) = 2.13, p = .03
    -   NEO: N, t(490) = 3.55, p \< .001
    -   SSEIT, t(490) = 3.59, p \< .001
    -   SRS, t(490) = -2.24, p = .03

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_access_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = SAD_access_sum ~  NEO_C_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Emotional Attachement

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 39.93, p \< .001, R2 = .53

-   sig predictors:

    -   Age, t(490) = -2.76, p = .01
    -   DASS: A, t(490) = 3.76, p \< .001
    -   NEO: O, t(490) = -2.25, p = .03
    -   NEO: E, t(490) = 3.54, p \< .001
    -   NEO: N, t(490) = 2.82, p = .005
    -   SSEIT, t(490) = 8.44, p \< .001
    -   SRS, t(490) = -6.46, p \< .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_ea_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = SAD_ea_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_E_sum + NEO_N_sum + SSEIT_sum + SRS_sum))
```

#### SAD - Distractibility

This regression used the following formula:
$SAD = age + age first phone + BIS + D + A + S + O + C + E + A + N + RSES + SSEIT + SRS$

Results indicate that:

-   over all model was sig F(14, 490) = 56.37, p \< .001, R2 = .62

-   sig predictors:

    -   Age, t(490) = -2.03, p = .04
    -   DASS: A, t(490) = 3.37, p \< .001
    -   NEO: O, t(490) = -2.95, p = .003
    -   NEO: E, t(490) = 3.33, p \< .001
    -   SSEIT, t(490) = 6.68, p \< .001
    -   SRS, t(490) = -9.33, p \< .001

```{r}
# run lm + give summary
summary(lm(data = Mturk_data, formula = SAD_dist_sum ~ age + age_first_phone + BIS_sum + DASS_D_sum + DASS_A_sum + DASS_S_sum + NEO_O_sum + NEO_C_sum + NEO_E_sum + NEO_A_sum + NEO_N_sum + RSES_sum + SSEIT_sum + SRS_sum))

# summary(lm(data = Mturk_data, formula = SAD_dist_sum ~ age + DASS_A_sum + NEO_O_sum + NEO_E_sum + SSEIT_sum + SRS_sum))
```
