Survey results

Loading Packages

if (!require(haven)){
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── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ lubridate 1.9.2     ✔ tidyr     1.3.0
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  install.packages("afex", dependencies = TRUE)
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Loading required package: afex
Loading required package: lme4
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Attaching package: 'Matrix'
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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':

    lmer

Import Data

dataset <- read_sav("Survey data(Slayyy Group).sav")

Clean Data

(dataset %>%
  filter(Duration__in_seconds_ > 100) -> dataset.clean)
# A tibble: 58 × 37
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 48 more rows
# ℹ 32 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …
(dataset.clean %>%
  filter(Progress > 99) -> dataset.clean)
# A tibble: 48 × 37
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 38 more rows
# ℹ 32 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …
(dataset.clean %>%
mutate(age_as_a_number = as.numeric(Age))-> dataset.clean)
# A tibble: 48 × 38
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 38 more rows
# ℹ 33 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …
(dataset.clean %>%
  filter(Age > 17) -> dataset.clean)
# A tibble: 47 × 38
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 33 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …

Create Study Guide IV

(dataset.clean %>%
  mutate(StudyGuideIV = case_when(FL_10_DO_Studyguide == 1 ~ "Study Guide", FL_10_DO_NoStudyGuide == 1 ~ "No Study Guide", TRUE ~ NA_character_)) -> dataset.clean)
# A tibble: 47 × 39
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 34 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …

Create Test Difficulty IV

(dataset.clean %>%
  mutate(TestdifficultyIV = case_when(FL_13_DO_Easiertest == 1 ~ "Easy Test", FL_13_DO_Hardertest == 1 ~ "Hard Test", TRUE ~ NA_character_)) -> dataset.clean)
# A tibble: 47 × 40
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 35 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …

Create DV

(dataset.clean %>%
   mutate(easy1 = case_when(Easy_1 == 2 ~ 1, TRUE ~ 0)) %>%
   mutate(easy2 = case_when(Easy_2 == 3 ~ 1, TRUE ~ 0)) %>%
   mutate(easy3 = case_when(Easy_3 == 1 ~ 1, TRUE ~ 0)) %>%
   mutate(easy4 = case_when(Easy_4 == 4 ~ 1, TRUE ~ 0)) %>%
   mutate(easy5 = case_when(Easy_5 == 1 ~ 1, TRUE ~ 0)) -> dataset.clean)
# A tibble: 47 × 45
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 40 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …
(dataset.clean %>%
   mutate(hard1 = case_when(Easy_1 == 3 ~ 1, TRUE ~ 0)) %>%
   mutate(hard2 = case_when(Easy_2 == 2 ~ 1, TRUE ~ 0)) %>%
   mutate(hard3 = case_when(Easy_3 == 2 ~ 1, TRUE ~ 0)) %>%
   mutate(hard4 = case_when(Easy_4 == 1 ~ 1, TRUE ~ 0)) %>%
   mutate(hard5 = case_when(Easy_5 == 1 ~ 1, TRUE ~ 0)) -> dataset.clean)
# A tibble: 47 × 50
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 45 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …
(dataset.clean %>%
   rowwise() %>%
   mutate(testscore = sum(easy1, easy2, easy3, easy4, easy5, hard1, hard2, hard3, hard4, hard5, na.rm = TRUE)) -> dataset.clean)
# A tibble: 47 × 51
# Rowwise: 
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2024-03-13 15:49:51 2024-03-13 15:53:13 0 [IP Address] 24.73.113.90       100
 2 2024-03-13 20:32:30 2024-03-13 20:34:51 0 [IP Address] 68.204.241.26      100
 3 2024-03-13 20:52:23 2024-03-13 20:57:15 0 [IP Address] 174.211.235.…      100
 4 2024-03-13 20:58:28 2024-03-13 21:02:02 0 [IP Address] 99.59.219.51       100
 5 2024-03-13 21:01:32 2024-03-13 21:03:24 0 [IP Address] 104.56.90.38       100
 6 2024-03-13 21:03:57 2024-03-13 21:06:17 0 [IP Address] 184.91.87.110      100
 7 2024-03-13 21:04:12 2024-03-13 21:07:59 0 [IP Address] 96.58.64.252       100
 8 2024-03-13 21:09:34 2024-03-13 21:12:34 0 [IP Address] 47.197.226.1…      100
 9 2024-03-13 21:20:56 2024-03-13 21:23:09 0 [IP Address] 73.192.7.69        100
10 2024-03-13 22:23:50 2024-03-13 22:29:50 0 [IP Address] 209.99.207.2…      100
# ℹ 37 more rows
# ℹ 46 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>, Informed_Consent <dbl+lbl>, Easy_1 <dbl+lbl>,
#   Easy_2 <dbl+lbl>, Easy_3 <dbl+lbl>, Easy_4 <dbl+lbl>, Easy_5 <dbl+lbl>, …

ANOVA

aov_ez(id = "ResponseId", 
       dv = "testscore", 
       data = dataset.clean, 
       between=c("StudyGuideIV","TestdifficultyIV"),
       anova_table = list(es = "pes"))
Converting to factor: StudyGuideIV, TestdifficultyIV
Contrasts set to contr.sum for the following variables: StudyGuideIV, TestdifficultyIV
Anova Table (Type 3 tests)

Response: testscore
                         Effect    df  MSE          F  pes p.value
1                  StudyGuideIV 1, 43 0.70     3.46 + .074    .070
2              TestdifficultyIV 1, 43 0.70 409.59 *** .905   <.001
3 StudyGuideIV:TestdifficultyIV 1, 43 0.70     3.46 + .074    .070
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Means

dataset.clean %>%
   group_by(StudyGuideIV) %>%
   summarise(mean = mean(testscore, na.rm = TRUE), 
             SD = SD(testscore, na.rm = TRUE))
# A tibble: 2 × 3
  StudyGuideIV    mean    SD
  <chr>          <dbl> <dbl>
1 No Study Guide  3.26  2.43
2 Study Guide     2.58  2.92
dataset.clean %>%
   group_by(TestdifficultyIV) %>%
   summarise(mean = mean(testscore, na.rm = TRUE), 
             SD = SD(testscore, na.rm = TRUE))
# A tibble: 2 × 3
  TestdifficultyIV  mean    SD
  <chr>            <dbl> <dbl>
1 Easy Test         5.07  1.17
2 Hard Test         0     0   
dataset.clean %>%
   group_by(StudyGuideIV, TestdifficultyIV) %>%
   summarise(mean = mean(testscore, na.rm = TRUE), 
             SD = SD(testscore, na.rm = TRUE))
`summarise()` has grouped output by 'StudyGuideIV'. You can override using the
`.groups` argument.
# A tibble: 4 × 4
# Groups:   StudyGuideIV [2]
  StudyGuideIV   TestdifficultyIV  mean    SD
  <chr>          <chr>            <dbl> <dbl>
1 No Study Guide Easy Test         4.69 1.25 
2 No Study Guide Hard Test         0    0    
3 Study Guide    Easy Test         5.64 0.809
4 Study Guide    Hard Test         0    0

Method section Numbers

print(dfSummary(dataset.clean, graph.magnif = .75), method = 'render')
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
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 *
graph.magnif, : unable to open connection to X11 display ''

Data Frame Summary

dataset.clean

Dimensions: 47 x 51
Duplicates: 0
No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
1 StartDate [POSIXct, POSIXt] Start Date
min : 2024-03-13 15:49:51
med : 2024-03-15 10:30:08
max : 2024-03-26 22:35:57
range : 13d 6H 46M 6S
47 distinct values 47 (100.0%) 0 (0.0%)
2 EndDate [POSIXct, POSIXt] End Date
min : 2024-03-13 15:53:13
med : 2024-03-15 10:33:57
max : 2024-03-26 22:37:48
range : 13d 6H 44M 35S
47 distinct values 47 (100.0%) 0 (0.0%)
3 Status [haven_labelled, vctrs_vctr, double] Response Type 1 distinct value
0 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
4 IPAddress [character] IP Address
1. 73.104.215.14
2. 104.160.57.17
3. 104.28.39.137
4. 104.56.90.38
5. 107.115.159.100
6. 107.115.159.93
7. 108.39.88.20
8. 128.12.123.99
9. 143.244.61.242
10. 166.198.157.57
[ 36 others ]
2 ( 4.3% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
36 ( 76.6% )
47 (100.0%) 0 (0.0%)
5 Progress [numeric] Progress 1 distinct value
100 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
6 Duration__in_seconds_ [numeric] Duration (in seconds)
Mean (sd) : 371.3 (1045)
min ≤ med ≤ max:
108 ≤ 189 ≤ 7348
IQR (CV) : 131 (2.8)
45 distinct values 47 (100.0%) 0 (0.0%)
7 Finished [haven_labelled, vctrs_vctr, double] Finished 1 distinct value
1 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
8 RecordedDate [POSIXct, POSIXt] Recorded Date
min : 2024-03-13 15:53:13
med : 2024-03-15 10:33:58
max : 2024-03-26 22:37:49
range : 13d 6H 44M 36S
47 distinct values 47 (100.0%) 0 (0.0%)
9 ResponseId [character] Response ID
1. R_1aiuBkmj1UGLQ7n
2. R_1dACIZIxEdq2EzB
3. R_1oMzsS4KrQQ3BXL
4. R_1UTHejI8JZ4Ksvv
5. R_1uzLoaSd6hhQUKJ
6. R_1wdAhWV1LFtBv9f
7. R_2Bxi0PXES5UgFmO
8. R_2iKBEs6w0Z83CQq
9. R_2m0QgSS0jX1pkPL
10. R_2OTEBlhu3lLfv7Y
[ 37 others ]
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
37 ( 78.7% )
47 (100.0%) 0 (0.0%)
10 RecipientLastName [character] Recipient Last Name
All empty strings
47 (100.0%) 0 (0.0%)
11 RecipientFirstName [character] Recipient First Name
All empty strings
47 (100.0%) 0 (0.0%)
12 RecipientEmail [character] Recipient Email
All empty strings
47 (100.0%) 0 (0.0%)
13 ExternalReference [character] External Data Reference
All empty strings
47 (100.0%) 0 (0.0%)
14 LocationLatitude [character] Location Latitude
1. 30.2712
2. 30.2903
3. 30.3511
4. 27.3407
5. 27.7538
6. 28.0406
7. 28.1822
8. 28.4224
9. 28.5981
10. 28.6344
[ 33 others ]
3 ( 6.4% )
2 ( 4.3% )
2 ( 4.3% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
33 ( 70.2% )
47 (100.0%) 0 (0.0%)
15 LocationLongitude [character] Location Longitude
1. -81.7529
2. -81.506
3. -81.5092
4. -110.915
5. -114.0234
6. -122.1639
7. -71.095
8. -72.0303
9. -72.5004
10. -72.56
[ 33 others ]
3 ( 6.4% )
2 ( 4.3% )
2 ( 4.3% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
33 ( 70.2% )
47 (100.0%) 0 (0.0%)
16 DistributionChannel [character] Distribution Channel 1. anonymous
47 ( 100.0% )
47 (100.0%) 0 (0.0%)
17 UserLanguage [character] User Language 1. EN
47 ( 100.0% )
47 (100.0%) 0 (0.0%)
18 Informed_Consent [haven_labelled, vctrs_vctr, double] Purpose of Research and Specific procedures to be used: In this study, you will be asked to participate in a research study on study habits. It should take approximately 10 minutes to complete. All answers will remain anonymous. Please answer the questions to the best of your ability. Duration of Participation: Your participation should take 10 minutes. Benefits to the Individual: Your participation in this research will contribute to the body of psychological knowledge about studying. You will have the opportunity to gain a deeper understanding of psychological research. In addition, you will receive information about study habits at the end of this study. Risks to the Individual: This study poses no risks greater than those encountered in daily social interactions. Anonymity: Strict anonymity of all data will be upheld. Your responses will remain anonymous and will not be associated with any identifying information. Your informed consent will be kept separate from your responses to the questionnaire 1 distinct value
1 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
19 Easy_1 [haven_labelled, vctrs_vctr, double] Where did the Olympic games originate?
Mean (sd) : 2 (0.4)
min ≤ med ≤ max:
1 ≤ 2 ≤ 4
IQR (CV) : 0 (0.2)
1 : 1 ( 3.7% )
2 : 25 ( 92.6% )
4 : 1 ( 3.7% )
27 (57.4%) 20 (42.6%)
20 Easy_2 [haven_labelled, vctrs_vctr, double] Who was the first American President?
Min : 2
Mean : 3
Max : 3
2 : 1 ( 3.7% )
3 : 26 ( 96.3% )
27 (57.4%) 20 (42.6%)
21 Easy_3 [haven_labelled, vctrs_vctr, double] Which country gifted the Statue of Liberty to the United States?
Min : 1
Mean : 1
Max : 2
1 : 26 ( 96.3% )
2 : 1 ( 3.7% )
27 (57.4%) 20 (42.6%)
22 Easy_4 [haven_labelled, vctrs_vctr, double] What year was the declaration of independence signed?
Min : 3
Mean : 4
Max : 4
3 : 1 ( 3.7% )
4 : 26 ( 96.3% )
27 (57.4%) 20 (42.6%)
23 Easy_5 [haven_labelled, vctrs_vctr, double] How many wives did King Henry VIII have?
Mean (sd) : 1.9 (1.3)
min ≤ med ≤ max:
1 ≤ 1 ≤ 4
IQR (CV) : 1.5 (0.7)
1 : 16 ( 59.3% )
2 : 4 ( 14.8% )
3 : 1 ( 3.7% )
4 : 6 ( 22.2% )
27 (57.4%) 20 (42.6%)
24 Hard_1 [haven_labelled, vctrs_vctr, double] What year did the first modern Olympics take place?
Mean (sd) : 2.7 (0.6)
min ≤ med ≤ max:
1 ≤ 3 ≤ 3
IQR (CV) : 0.2 (0.2)
1 : 1 ( 5.0% )
2 : 4 ( 20.0% )
3 : 15 ( 75.0% )
20 (42.6%) 27 (57.4%)
25 Hard_2 [haven_labelled, vctrs_vctr, double] Who was the 11th president of the United States?
Mean (sd) : 2.3 (0.9)
min ≤ med ≤ max:
1 ≤ 2 ≤ 4
IQR (CV) : 0 (0.4)
1 : 2 ( 10.0% )
2 : 14 ( 70.0% )
4 : 4 ( 20.0% )
20 (42.6%) 27 (57.4%)
26 Hard_3 [haven_labelled, vctrs_vctr, double] What year was the statue of liberty gifted to the United states?
Min : 1
Mean : 1.7
Max : 2
1 : 6 ( 30.0% )
2 : 14 ( 70.0% )
20 (42.6%) 27 (57.4%)
27 Hard_4 [haven_labelled, vctrs_vctr, double] Who was the first person to sign the Declaration of Independence?
Min : 1
Mean : 1.1
Max : 2
1 : 18 ( 90.0% )
2 : 2 ( 10.0% )
20 (42.6%) 27 (57.4%)
28 Hard_5 [haven_labelled, vctrs_vctr, double] Who was King Henry VIII's fourth wife?
Mean (sd) : 1.4 (0.8)
min ≤ med ≤ max:
1 ≤ 1 ≤ 4
IQR (CV) : 0 (0.6)
1 : 16 ( 80.0% )
2 : 2 ( 10.0% )
3 : 1 ( 5.0% )
4 : 1 ( 5.0% )
20 (42.6%) 27 (57.4%)
29 Race [character] What is your racial identity?
1. African American
2. Asian
3. Caucasian
4. hispanic
5. Human
6. Mixed
7. Mutt
8. Non hispanic or latino
9. Viking
10. white
11. White
1 ( 2.1% )
2 ( 4.3% )
9 ( 19.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
3 ( 6.4% )
26 ( 55.3% )
47 (100.0%) 0 (0.0%)
30 Gender [character] What gender do you identify with?
1. agender
2. F
3. female
4. Female
5. gender-fluid
6. He/they
7. Male
8. Non Binary
9. Nonbinary
10. Woman
11. Women
1 ( 2.1% )
1 ( 2.1% )
3 ( 6.4% )
17 ( 36.2% )
1 ( 2.1% )
1 ( 2.1% )
18 ( 38.3% )
1 ( 2.1% )
1 ( 2.1% )
2 ( 4.3% )
1 ( 2.1% )
47 (100.0%) 0 (0.0%)
31 Education [haven_labelled, vctrs_vctr, double] What is the highest degree or level of school you have completed? If currently enrolled, highest degree received.
Mean (sd) : 3.7 (1.3)
min ≤ med ≤ max:
1 ≤ 3 ≤ 5
IQR (CV) : 2 (0.3)
1 : 1 ( 2.1% )
2 : 9 ( 19.1% )
3 : 15 ( 31.9% )
4 : 2 ( 4.3% )
5 : 20 ( 42.6% )
47 (100.0%) 0 (0.0%)
32 Age [character] What is your age?
1. 21
2. 20
3. 22
4. 23
5. 28
6. 35
7. 19
8. 33
9. 39
10. 40
[ 14 others ]
7 ( 14.9% )
4 ( 8.5% )
3 ( 6.4% )
3 ( 6.4% )
3 ( 6.4% )
3 ( 6.4% )
2 ( 4.3% )
2 ( 4.3% )
2 ( 4.3% )
2 ( 4.3% )
16 ( 34.0% )
47 (100.0%) 0 (0.0%)
33 Participants_thought [character] what did you think the hypotheses were?
1. (Empty string)
2. 2 varibles, ideas
3. A study of study habits
4. A theory
5. Details learned over a pe
6. did people actually read
7. Do study guides assist in
8. Dunno
9. Dunno?
10. Either average knowledge
[ 33 others ]
5 ( 10.6% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
1 ( 2.1% )
33 ( 70.2% )
47 (100.0%) 0 (0.0%)
34 FL_10_DO_Studyguide [numeric] FL_10 - Block Randomizer - Display Order Studyguide 1 distinct value
1 : 24 ( 100.0% )
24 (51.1%) 23 (48.9%)
35 FL_10_DO_NoStudyGuide [numeric] FL_10 - Block Randomizer - Display Order NoStudyGuide 1 distinct value
1 : 23 ( 100.0% )
23 (48.9%) 24 (51.1%)
36 FL_13_DO_Easiertest [numeric] FL_13 - Block Randomizer - Display Order Easiertest 1 distinct value
1 : 27 ( 100.0% )
27 (57.4%) 20 (42.6%)
37 FL_13_DO_Hardertest [numeric] FL_13 - Block Randomizer - Display Order Hardertest 1 distinct value
1 : 20 ( 100.0% )
20 (42.6%) 27 (57.4%)
38 age_as_a_number [numeric]
Mean (sd) : 34.3 (14.6)
min ≤ med ≤ max:
19 ≤ 32 ≤ 69
IQR (CV) : 20.5 (0.4)
24 distinct values 47 (100.0%) 0 (0.0%)
39 StudyGuideIV [character]
1. No Study Guide
2. Study Guide
23 ( 48.9% )
24 ( 51.1% )
47 (100.0%) 0 (0.0%)
40 TestdifficultyIV [character]
1. Easy Test
2. Hard Test
27 ( 57.4% )
20 ( 42.6% )
47 (100.0%) 0 (0.0%)
41 easy1 [numeric]
Min : 0
Mean : 0.5
Max : 1
0 : 22 ( 46.8% )
1 : 25 ( 53.2% )
47 (100.0%) 0 (0.0%)
42 easy2 [numeric]
Min : 0
Mean : 0.6
Max : 1
0 : 21 ( 44.7% )
1 : 26 ( 55.3% )
47 (100.0%) 0 (0.0%)
43 easy3 [numeric]
Min : 0
Mean : 0.6
Max : 1
0 : 21 ( 44.7% )
1 : 26 ( 55.3% )
47 (100.0%) 0 (0.0%)
44 easy4 [numeric]
Min : 0
Mean : 0.6
Max : 1
0 : 21 ( 44.7% )
1 : 26 ( 55.3% )
47 (100.0%) 0 (0.0%)
45 easy5 [numeric]
Min : 0
Mean : 0.3
Max : 1
0 : 31 ( 66.0% )
1 : 16 ( 34.0% )
47 (100.0%) 0 (0.0%)
46 hard1 [numeric] 1 distinct value
0 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
47 hard2 [numeric]
Min : 0
Mean : 0
Max : 1
0 : 46 ( 97.9% )
1 : 1 ( 2.1% )
47 (100.0%) 0 (0.0%)
48 hard3 [numeric]
Min : 0
Mean : 0
Max : 1
0 : 46 ( 97.9% )
1 : 1 ( 2.1% )
47 (100.0%) 0 (0.0%)
49 hard4 [numeric] 1 distinct value
0 : 47 ( 100.0% )
47 (100.0%) 0 (0.0%)
50 hard5 [numeric]
Min : 0
Mean : 0.3
Max : 1
0 : 31 ( 66.0% )
1 : 16 ( 34.0% )
47 (100.0%) 0 (0.0%)
51 testscore [numeric]
Mean (sd) : 2.9 (2.7)
min ≤ med ≤ max:
0 ≤ 4 ≤ 6
IQR (CV) : 6 (0.9)
0 : 20 ( 42.6% )
3 : 3 ( 6.4% )
4 : 8 ( 17.0% )
6 : 16 ( 34.0% )
47 (100.0%) 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.3)
2024-04-10