Social Osprey Analysis

Loading Packages

haven

if (!require(haven)){
  install.packages("haven", dependencies = TRUE)
  require(haven)
}

Loading required package: haven

summarytools

if (!require(summarytools)){
  install.packages("summarytools", dependencies = TRUE)
  require(summarytools)
}

Loading required package: summarytools

Warning in fun(libname, pkgname): couldn't connect to display ":0"

system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)

tidyverse

if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
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psych

if (!require(psych)){
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}

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afex

if (!require(afex)){
  install.packages("afex", dependencies = TRUE)
  library(afex)
}

Loading required package: afex

Loading required package: lme4

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************
Welcome to afex. For support visit: http://afex.singmann.science/

- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- Get and set global package options with: afex_options()
- Set sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
************


Attaching package: 'afex'

The following object is masked from 'package:lme4':

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##Import Data

dataset <- read_sav("Social+Ospreys+Survey_April+12,+2024_10.37.sav")

dataset %>%
  filter(Duration__in_seconds_ > 120) %>%
  mutate(age_as_a_number = as.numeric(Age)) -> dataset.clean

Create Activity IV

(dataset.clean %>%
  mutate(activityIV = case_when(FL_11_DO_ExerciseLessThanaMinute == 1 ~ "Exercise",
                               FL_11_DO_ReadingLessThanaMinute == 1 ~ "Reading",
                               FL_11_DO_ExerciseMoreThanaMinute == 1 ~ "Exercise",
                               FL_11_DO_ReadingMoreThanaMinute == 1 ~ "Reading")) -> dataset.clean)

# A tibble: 31 × 32
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-04-03 12:24:34 2024-04-03 12:28:03 0 [IP Address] 38.91.106.87       100
 2 2024-04-03 13:17:39 2024-04-03 13:20:13 0 [IP Address] 72.209.71.16       100
 3 2024-04-03 13:18:46 2024-04-03 13:21:05 0 [IP Address] 72.196.114.4       100
 4 2024-04-03 13:17:43 2024-04-03 13:22:18 0 [IP Address] 139.62.222.65      100
 5 2024-04-03 13:40:51 2024-04-03 13:43:14 0 [IP Address] 172.59.66.164      100
 6 2024-04-03 13:21:13 2024-04-03 13:48:35 0 [IP Address] 85.76.116.117      100
 7 2024-04-03 12:58:01 2024-04-03 13:50:23 0 [IP Address] 24.7.113.89        100
 8 2024-04-03 13:58:25 2024-04-03 14:00:55 0 [IP Address] 172.59.67.240      100
 9 2024-04-03 14:06:05 2024-04-03 14:08:42 0 [IP Address] 199.44.92.17       100
10 2024-04-03 15:11:12 2024-04-03 15:14:53 0 [IP Address] 162.229.230.…      100
# ℹ 21 more rows
# ℹ 27 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Q_RecaptchaScore <dbl>, Informed_consent <dbl+lbl>,
#   Anxiety1 <dbl+lbl>, Anxiety2 <dbl+lbl>, Anxiety3 <dbl+lbl>, Gender <chr>, …

Create Duration IV

(dataset.clean %>%
  mutate(durationIV = case_when(FL_11_DO_ExerciseLessThanaMinute == 1 ~ "short",
                               FL_11_DO_ReadingLessThanaMinute == 1 ~ "short",
                               FL_11_DO_ExerciseMoreThanaMinute == 1 ~ "long",
                               FL_11_DO_ReadingMoreThanaMinute == 1 ~ "long")) -> dataset.clean)

# A tibble: 31 × 33
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-04-03 12:24:34 2024-04-03 12:28:03 0 [IP Address] 38.91.106.87       100
 2 2024-04-03 13:17:39 2024-04-03 13:20:13 0 [IP Address] 72.209.71.16       100
 3 2024-04-03 13:18:46 2024-04-03 13:21:05 0 [IP Address] 72.196.114.4       100
 4 2024-04-03 13:17:43 2024-04-03 13:22:18 0 [IP Address] 139.62.222.65      100
 5 2024-04-03 13:40:51 2024-04-03 13:43:14 0 [IP Address] 172.59.66.164      100
 6 2024-04-03 13:21:13 2024-04-03 13:48:35 0 [IP Address] 85.76.116.117      100
 7 2024-04-03 12:58:01 2024-04-03 13:50:23 0 [IP Address] 24.7.113.89        100
 8 2024-04-03 13:58:25 2024-04-03 14:00:55 0 [IP Address] 172.59.67.240      100
 9 2024-04-03 14:06:05 2024-04-03 14:08:42 0 [IP Address] 199.44.92.17       100
10 2024-04-03 15:11:12 2024-04-03 15:14:53 0 [IP Address] 162.229.230.…      100
# ℹ 21 more rows
# ℹ 28 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Q_RecaptchaScore <dbl>, Informed_consent <dbl+lbl>,
#   Anxiety1 <dbl+lbl>, Anxiety2 <dbl+lbl>, Anxiety3 <dbl+lbl>, Gender <chr>, …

Create DV

Anxiety <- data.frame (dataset.clean$Anxiety1, dataset.clean$Anxiety2, dataset.clean$Anxiety3)
#create dataframe with only relevant variables to work with

Anxiety.keys <- make.keys(Anxiety, list(Anxiety=c(1,2,3)))
#score the scale
Anxiety.scales <- scoreItems (Anxiety.keys, Anxiety)
#save the scores
Anxiety.scores <- Anxiety.scales$scores
#save the scores back in 'dataset'
dataset.clean$Anxiety <- Anxiety.scores[,]
#print the cronbach alpha
Anxiety.scales$alpha

        Anxiety
alpha 0.9067193

ANOVA

aov_ez(id = "ResponseId", 
       dv = "Anxiety", 
       data = dataset.clean, 
       between=c("durationIV", "activityIV"),
       anova_table = list(es = "pes"))

Converting to factor: durationIV, activityIV

Contrasts set to contr.sum for the following variables: durationIV, activityIV

Anova Table (Type 3 tests)

Response: Anxiety
                 Effect    df  MSE    F  pes p.value
1            durationIV 1, 27 1.77 0.30 .011    .586
2            activityIV 1, 27 1.77 2.30 .079    .141
3 durationIV:activityIV 1, 27 1.77 2.08 .072    .160
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Numbers

Marginal means for Duration

dataset.clean %>%
  group_by(durationIV) %>%
  summarise(m = mean(Anxiety),
            sd = sd(Anxiety))

# A tibble: 2 × 3
  durationIV     m    sd
  <chr>      <dbl> <dbl>
1 long        2.63  1.61
2 short       2.69  1.07

Marginal means for Activity

dataset.clean %>%
  group_by(activityIV) %>%
  summarise(m = mean(Anxiety),
            sd = sd(Anxiety))

# A tibble: 2 × 3
  activityIV     m    sd
  <chr>      <dbl> <dbl>
1 Exercise    2.12  1.10
2 Reading     2.95  1.44

Cell Means (inside of the study design table)

dataset.clean %>%
  group_by(durationIV, activityIV) %>%
  summarise(m = mean(Anxiety),
            sd = sd(Anxiety))

`summarise()` has grouped output by 'durationIV'. You can override using the
`.groups` argument.

# A tibble: 4 × 4
# Groups:   durationIV [2]
  durationIV activityIV     m    sd
  <chr>      <chr>      <dbl> <dbl>
1 long       Exercise    1.67 0.760
2 long       Reading     3.15 1.73 
3 short      Exercise    2.67 1.27 
4 short      Reading     2.70 1.02

Method section numbers

print(dfSummary(dataset.clean, graph.magnif = .75), method = 'render')

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Data Frame Summary

dataset.clean

Dimensions: 31 x 34
Duplicates: 0

StartDate [POSIXct, POSIXt]

Start Date

min : 2024-04-03 12:24:34
med : 2024-04-03 19:06:40
max : 2024-04-06 16:10:09
range : 3d 3H 45M 35S

31 distinct values

31 (100.0%)

0 (0.0%)

EndDate [POSIXct, POSIXt]

End Date

min : 2024-04-03 12:28:03
med : 2024-04-03 19:58:34
max : 2024-04-06 16:13:37
range : 3d 3H 45M 34S

31 distinct values

31 (100.0%)

0 (0.0%)

Status [haven_labelled, vctrs_vctr, double]

Response Type

1 distinct value

0	:	31	(	100.0%	)

31 (100.0%)

0 (0.0%)

IPAddress [character]

IP Address

1. 24.7.113.89
2. 104.3.179.1
3. 107.222.32.72
4. 108.254.221.89
5. 108.64.64.57
6. 139.62.222.65
7. 139.62.223.232
8. 139.62.223.245
9. 139.62.223.57
10. 162.229.230.176
[ 20 others ]

2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
20	(	64.5%	)

31 (100.0%)

0 (0.0%)

Progress [numeric]

Progress

Min : 90
Mean : 99.7
Max : 100

90	:	1	(	3.2%	)
100	:	30	(	96.8%	)

31 (100.0%)

0 (0.0%)

Duration__in_seconds_ [numeric]

Duration (in seconds)

Mean (sd) : 1623 (7039)
min ≤ med ≤ max:
122 ≤ 207 ≤ 39421
IQR (CV) : 135.5 (4.3)

30 distinct values

31 (100.0%)

0 (0.0%)

Finished [haven_labelled, vctrs_vctr, double]

Finished

Min : 0
Mean : 1
Max : 1

0	:	1	(	3.2%	)
1	:	30	(	96.8%	)

31 (100.0%)

0 (0.0%)

RecordedDate [POSIXct, POSIXt]

Recorded Date

min : 2024-04-03 12:28:04
med : 2024-04-03 19:58:35
max : 2024-04-10 20:29:53
range : 7d 8H 1M 49S

31 distinct values

31 (100.0%)

0 (0.0%)

ResponseId [character]

Response ID

1. R_10Whl7WJicp5KZr
2. R_12hXYSwULhE3IVg
3. R_1B5IhZLegTmRBZG
4. R_1cpSddO01mevcfP
5. R_1Ftz8IyEOJVh125
6. R_1q9urgnevwrr72A
7. R_1TtPjqnBVbvWI7E
8. R_29qANJ98VaHugqR
9. R_2CHgokM1tWPZN62
10. R_2DOM02ebmNICslJ
[ 21 others ]

1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
21	(	67.7%	)

31 (100.0%)

0 (0.0%)

RecipientLastName [character]

Recipient Last Name

All empty strings

31 (100.0%)

0 (0.0%)

RecipientFirstName [character]

Recipient First Name

All empty strings

31 (100.0%)

0 (0.0%)

RecipientEmail [character]

Recipient Email

All empty strings

31 (100.0%)

0 (0.0%)

ExternalReference [character]

External Data Reference

All empty strings

31 (100.0%)

0 (0.0%)

LocationLatitude [character]

Location Latitude

1. 30.3007
2. 30.2183
3. 30.132
4. 30.2903
5. 30.3958
6. 37.9985
7. (Empty string)
8. 25.7689
9. 29.6138
10. 29.6475
[ 12 others ]

4	(	12.9%	)
3	(	9.7%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
12	(	38.7%	)

31 (100.0%)

0 (0.0%)

LocationLongitude [character]

Location Longitude

1. -81.4421
2. -81.5621
3. -121.8878
4. -81.5092
5. -81.6893
6. -81.7726
7. (Empty string)
8. -76.7942
9. -80.1946
10. -81.506
[ 12 others ]

4	(	12.9%	)
3	(	9.7%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
12	(	38.7%	)

31 (100.0%)

0 (0.0%)

DistributionChannel [character]

Distribution Channel

1. anonymous

31	(	100.0%	)

31 (100.0%)

0 (0.0%)

UserLanguage [character]

User Language

1. EN

31	(	100.0%	)

31 (100.0%)

0 (0.0%)

Q_RecaptchaScore [numeric]

Q_RecaptchaScore

Mean (sd) : 0.9 (0.1)
min ≤ med ≤ max:
0.4 ≤ 0.9 ≤ 1
IQR (CV) : 0.2 (0.1)

0.40 !	:	1	(	3.2%	)
0.70 !	:	2	(	6.5%	)
0.80 !	:	5	(	16.1%	)
0.90 !	:	12	(	38.7%	)
1.00	:	11	(	35.5%	)
! rounded

31 (100.0%)

0 (0.0%)

Informed_consent [haven_labelled, vctrs_vctr, double]

Informed Consent University of North Florida Department of Psychological Sciences Purpose of Research and Specific procedures to be used: In this study, you will be asked to complete a short activity and answer some questions afterwards. It should take approximately 5 minutes to complete. All answers will remain anonymous. Please answer the questions to the best of your ability. There are no right or wrong answers. Duration of Participation: Your participation should take 5 minutes. Benefits to the Individual: Your participation in this research will contribute to the body of psychological knowledge about anxiety. You will have the opportunity to gain a deeper understanding of psychological research. In addition, you will receive information about anxiety at the end of this study. Risks to the Individual: This study poses no risks greater than those encountered in daily life. Anonymity: Strict anonymity of all data will be upheld. Your responses will remain anonymous and will not be associated with

1 distinct value

1	:	31	(	100.0%	)

31 (100.0%)

0 (0.0%)

Anxiety1 [haven_labelled, vctrs_vctr, double]

This activity gave me anxiety.

Mean (sd) : 3.1 (1.7)
min ≤ med ≤ max:
1 ≤ 2 ≤ 6
IQR (CV) : 3 (0.5)

1	:	5	(	16.1%	)
2	:	11	(	35.5%	)
3	:	2	(	6.5%	)
4	:	4	(	12.9%	)
5	:	6	(	19.4%	)
6	:	3	(	9.7%	)

31 (100.0%)

0 (0.0%)

Anxiety2 [haven_labelled, vctrs_vctr, double]

I felt nervous during this activity.

Mean (sd) : 2.5 (1.4)
min ≤ med ≤ max:
1 ≤ 2 ≤ 6
IQR (CV) : 1.5 (0.6)

1	:	8	(	25.8%	)
2	:	12	(	38.7%	)
3	:	4	(	12.9%	)
4	:	4	(	12.9%	)
5	:	2	(	6.5%	)
6	:	1	(	3.2%	)

31 (100.0%)

0 (0.0%)

Anxiety3 [haven_labelled, vctrs_vctr, double]

During this experience, I had feelings of worry.

Mean (sd) : 2.4 (1.4)
min ≤ med ≤ max:
1 ≤ 2 ≤ 6
IQR (CV) : 1.5 (0.6)

1	:	8	(	25.8%	)
2	:	14	(	45.2%	)
3	:	3	(	9.7%	)
4	:	3	(	9.7%	)
5	:	1	(	3.2%	)
6	:	2	(	6.5%	)

31 (100.0%)

0 (0.0%)

Gender [character]

What is your gender?

1. 34
2. female
3. Female
4. FEMALE
5. male
6. Male
7. Woman

1	(	3.2%	)
2	(	6.5%	)
18	(	58.1%	)
1	(	3.2%	)
1	(	3.2%	)
6	(	19.4%	)
2	(	6.5%	)

31 (100.0%)

0 (0.0%)

Race [character]

What is your race?

1. White
2. African American
3. Asian
4. Black
5. Hispanic
6. white
7. Asian pacific
8. black
9. Black & White
10. Black/white
[ 2 others ]

15	(	48.4%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
2	(	6.5%	)

31 (100.0%)

0 (0.0%)

Age [character]

What is your age? (18+)

1. 20
2. 21
3. 23
4. 24
5. 38
6. 52
7. 18
8. 19
9. 22
10. 26
[ 9 others ]

4	(	12.9%	)
4	(	12.9%	)
4	(	12.9%	)
2	(	6.5%	)
2	(	6.5%	)
2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
9	(	29.0%	)

31 (100.0%)

0 (0.0%)

Hypothesis [character]

What do you think the hypothesis of our study is?

1. (Empty string)
2. Academic reading is assoc
3. Anxiety doing a short tim
4. As timed pressures increa
5. Does an increased heart r
6. Does exercise affect your
7. Exercise helps reduce anx
8. Fungi
9. Fungi is every where
10. Hopefully that a quick bu
[ 20 others ]

2	(	6.5%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
1	(	3.2%	)
20	(	64.5%	)

31 (100.0%)

0 (0.0%)

FL_11_DO_ExerciseLessThanaMinute [numeric]

FL_11 - Block Randomizer - Display Order ExerciseLessThanaMinute

1 distinct value

1	:	5	(	100.0%	)

5 (16.1%)

26 (83.9%)

FL_11_DO_ReadingLessThanaMinute [numeric]

FL_11 - Block Randomizer - Display Order ReadingLessThanaMinute

1 distinct value

1	:	9	(	100.0%	)

9 (29.0%)

22 (71.0%)

FL_11_DO_ExerciseMoreThanaMinute [numeric]

FL_11 - Block Randomizer - Display Order ExerciseMoreThanaMinute

1 distinct value

1	:	6	(	100.0%	)

6 (19.4%)

25 (80.6%)

FL_11_DO_ReadingMoreThanaMinute [numeric]

FL_11 - Block Randomizer - Display Order ReadingMoreThanaMinute

1 distinct value

1	:	11	(	100.0%	)

11 (35.5%)

20 (64.5%)

age_as_a_number [numeric]

Mean (sd) : 30.8 (12.7)
min ≤ med ≤ max:
18 ≤ 24 ≤ 59
IQR (CV) : 17 (0.4)

19 distinct values

31 (100.0%)

0 (0.0%)

activityIV [character]

1. Exercise
2. Reading

11	(	35.5%	)
20	(	64.5%	)

31 (100.0%)

0 (0.0%)

durationIV [character]

1. long
2. short

17	(	54.8%	)
14	(	45.2%	)

31 (100.0%)

0 (0.0%)

Anxiety [numeric]

Mean (sd) : 2.7 (1.4)
min ≤ med ≤ max:
1 ≤ 2 ≤ 6
IQR (CV) : 1.8 (0.5)

14 distinct values

31 (100.0%)

0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.3)
2024-04-15