Analysis Assignment

Load Packages

if (!require(haven)){
  install.packages("haven", dependencies = TRUE)
  require(haven)
}
Loading required package: haven
if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  require(tidyverse)
}
Loading required package: tidyverse
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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)

Attaching package: 'summarytools'
The following object is masked from 'package:tibble':

    view
if (!require(psych)){
  install.packages("psych", dependencies = TRUE)
  require(psych)
}
Loading required package: psych

Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':

    %+%, alpha
if (!require(afex)){
  install.packages("afex", dependencies = TRUE)
  require(afex)
}
Loading required package: afex
Loading required package: lme4
Loading required package: Matrix

Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':

    expand, pack, unpack
************
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("Music_Exposure_Data.sav")

Clean Data

(dataset %>%
  filter(Duration__in_seconds_ > 90) %>%
  mutate(age_as_a_number = as.numeric(Age)) -> dataset.clean)
# A tibble: 44 × 34
   StartDate           EndDate             Status             IPAddress Progress
   <dttm>              <dttm>              <dbl+lbl>          <chr>        <dbl>
 1 2024-07-14 19:37:50 2024-07-14 19:53:25 1 [Survey Preview] ""             100
 2 2024-07-16 20:35:21 2024-07-16 20:38:46 1 [Survey Preview] ""             100
 3 2024-07-24 13:58:48 2024-07-24 14:03:11 0 [IP Address]     "50.234.…      100
 4 2024-07-24 14:04:07 2024-07-24 14:05:48 0 [IP Address]     "208.189…      100
 5 2024-07-24 14:10:15 2024-07-24 14:12:12 0 [IP Address]     "104.28.…      100
 6 2024-07-24 14:04:37 2024-07-24 14:14:45 0 [IP Address]     "208.189…      100
 7 2024-07-24 14:21:14 2024-07-24 14:25:15 0 [IP Address]     "108.147…      100
 8 2024-07-24 14:28:34 2024-07-24 14:30:18 0 [IP Address]     "174.212…      100
 9 2024-07-24 14:28:07 2024-07-24 14:32:06 0 [IP Address]     "73.104.…      100
10 2024-07-24 14:36:11 2024-07-24 14:40:21 0 [IP Address]     "97.178.…      100
# ℹ 34 more rows
# ℹ 29 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>, QID1 <dbl+lbl>, Extroversion_1_1 <dbl+lbl>,
#   Extroversion_2_1 <dbl+lbl>, Extroversion_3_1 <dbl+lbl>, …

Create Exposure IV

(dataset.clean %>%
  mutate(exposureIV = case_when(FL_20_DO_MusicExposure_1song__Genre_somber_ == 1 ~ "1song",
                                FL_20_DO_MusicExposure_1song__Genre_pop_upbeat_ == 1 ~ "1song",
                                FL_20_DO_MusicExposure_1minute__Genre_pop_upbeat_ == 1 ~ "1minute",
                                FL_20_DO_MusicExposure_1minute__Genre_somber_ == 1 ~ "1minute")) -> dataset.clean)
# A tibble: 44 × 35
   StartDate           EndDate             Status             IPAddress Progress
   <dttm>              <dttm>              <dbl+lbl>          <chr>        <dbl>
 1 2024-07-14 19:37:50 2024-07-14 19:53:25 1 [Survey Preview] ""             100
 2 2024-07-16 20:35:21 2024-07-16 20:38:46 1 [Survey Preview] ""             100
 3 2024-07-24 13:58:48 2024-07-24 14:03:11 0 [IP Address]     "50.234.…      100
 4 2024-07-24 14:04:07 2024-07-24 14:05:48 0 [IP Address]     "208.189…      100
 5 2024-07-24 14:10:15 2024-07-24 14:12:12 0 [IP Address]     "104.28.…      100
 6 2024-07-24 14:04:37 2024-07-24 14:14:45 0 [IP Address]     "208.189…      100
 7 2024-07-24 14:21:14 2024-07-24 14:25:15 0 [IP Address]     "108.147…      100
 8 2024-07-24 14:28:34 2024-07-24 14:30:18 0 [IP Address]     "174.212…      100
 9 2024-07-24 14:28:07 2024-07-24 14:32:06 0 [IP Address]     "73.104.…      100
10 2024-07-24 14:36:11 2024-07-24 14:40:21 0 [IP Address]     "97.178.…      100
# ℹ 34 more rows
# ℹ 30 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>, QID1 <dbl+lbl>, Extroversion_1_1 <dbl+lbl>,
#   Extroversion_2_1 <dbl+lbl>, Extroversion_3_1 <dbl+lbl>, …

Create Genre IV

(dataset.clean %>%
  mutate(genreIV = case_when(FL_20_DO_MusicExposure_1song__Genre_somber_ == 1 ~ "somber",
                                FL_20_DO_MusicExposure_1song__Genre_pop_upbeat_ == 1 ~ "upbeat",
                                FL_20_DO_MusicExposure_1minute__Genre_pop_upbeat_ == 1 ~ "upbeat",
                                FL_20_DO_MusicExposure_1minute__Genre_somber_ == 1 ~ "somber")) -> dataset.clean)
# A tibble: 44 × 36
   StartDate           EndDate             Status             IPAddress Progress
   <dttm>              <dttm>              <dbl+lbl>          <chr>        <dbl>
 1 2024-07-14 19:37:50 2024-07-14 19:53:25 1 [Survey Preview] ""             100
 2 2024-07-16 20:35:21 2024-07-16 20:38:46 1 [Survey Preview] ""             100
 3 2024-07-24 13:58:48 2024-07-24 14:03:11 0 [IP Address]     "50.234.…      100
 4 2024-07-24 14:04:07 2024-07-24 14:05:48 0 [IP Address]     "208.189…      100
 5 2024-07-24 14:10:15 2024-07-24 14:12:12 0 [IP Address]     "104.28.…      100
 6 2024-07-24 14:04:37 2024-07-24 14:14:45 0 [IP Address]     "208.189…      100
 7 2024-07-24 14:21:14 2024-07-24 14:25:15 0 [IP Address]     "108.147…      100
 8 2024-07-24 14:28:34 2024-07-24 14:30:18 0 [IP Address]     "174.212…      100
 9 2024-07-24 14:28:07 2024-07-24 14:32:06 0 [IP Address]     "73.104.…      100
10 2024-07-24 14:36:11 2024-07-24 14:40:21 0 [IP Address]     "97.178.…      100
# ℹ 34 more rows
# ℹ 31 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>, QID1 <dbl+lbl>, Extroversion_1_1 <dbl+lbl>,
#   Extroversion_2_1 <dbl+lbl>, Extroversion_3_1 <dbl+lbl>, …

Create DV

#create dataframe with only relevant variables to work with
Extroversion <- data.frame (dataset.clean$Extroversion_1_1,dataset.clean$Extroversion_2_1, dataset.clean$Extroversion_3_1, dataset.clean$Extroversion_4_1, dataset.clean$Extroversion_5_1)

#create list of 'keys'. The  numbers just refer to the order of the question in the data.frame() you just made. The most important thing is to mark the questions that should be reversed scored with a '-'. 
Extroversion.keys <- make.keys(Extroversion, list(Extroversion=c(1,2,3,4,5)))

#score the scale
Extroversion.scales <- scoreItems (Extroversion.keys, Extroversion)

#save the scores
Extroversion.scores <- Extroversion.scales$scores

#save the scores back in 'dataset'
dataset.clean$Extroversion <- Extroversion.scores[,]

#print the cronbach alpha
Extroversion.scales$alpha
      Extroversion
alpha    0.2267214

ANOVA

aov_ez(id = "ResponseId", 
       dv = "Extroversion", 
       data = dataset.clean, 
       between=c("exposureIV", "genreIV"),
       anova_table = list(es = "pes"))
Converting to factor: exposureIV, genreIV
Warning: Missing values for 2 ID(s), which were removed before analysis:
R_538NE4EGCzwEbFF, R_6PLfzifu0emS0zD
Below the first few rows (in wide format) of the removed cases with missing data.
            ResponseId exposureIV genreIV   .
# 20 R_538NE4EGCzwEbFF       <NA>    <NA> 3.6
# 35 R_6PLfzifu0emS0zD       <NA>    <NA> 3.0
Contrasts set to contr.sum for the following variables: exposureIV, genreIV
Anova Table (Type 3 tests)

Response: Extroversion
              Effect    df  MSE    F  pes p.value
1         exposureIV 1, 38 0.47 1.23 .031    .273
2            genreIV 1, 38 0.47 0.92 .024    .343
3 exposureIV:genreIV 1, 38 0.47 0.72 .019    .402
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Numbers

Marginal means for Exposure

dataset.clean %>%
  group_by(exposureIV) %>%
  summarise(m = mean(Extroversion),
            sd = sd(Extroversion))
# A tibble: 3 × 3
  exposureIV     m    sd
  <chr>      <dbl> <dbl>
1 1minute     3.48 0.697
2 1song       3.68 0.672
3 <NA>        3.3  0.424

Marginal means for Genre

dataset.clean %>%
  group_by(genreIV) %>%
  summarise(m = mean(Extroversion),
            sd = sd(Extroversion))
# A tibble: 3 × 3
  genreIV     m    sd
  <chr>   <dbl> <dbl>
1 somber   3.5  0.667
2 upbeat   3.68 0.706
3 <NA>     3.3  0.424

Cell means (inside of the study design table)

(dataset.clean %>%
  group_by(genreIV, exposureIV) %>%
  summarise(m = mean(Extroversion),
            sd = sd(Extroversion),
            .groups = 'drop'))
# A tibble: 5 × 4
  genreIV exposureIV     m    sd
  <chr>   <chr>      <dbl> <dbl>
1 somber  1minute     3.47 0.6  
2 somber  1song       3.52 0.733
3 upbeat  1minute     3.49 0.797
4 upbeat  1song       3.91 0.530
5 <NA>    <NA>        3.3  0.424

Method Section Numbers

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

Data Frame Summary

dataset.clean

Dimensions: 44 x 37
Duplicates: 0
No Variable Label Stats / Values Freqs (% of Valid) Valid Missing
1 StartDate [POSIXct, POSIXt] Start Date
min : 2024-07-14 19:37:50
med : 2024-07-24 16:30:11
max : 2024-07-25 12:19:41
range : 10d 16H 41M 51S
44 distinct values 44 (100.0%) 0 (0.0%)
2 EndDate [POSIXct, POSIXt] End Date
min : 2024-07-14 19:53:25
med : 2024-07-24 16:36:26
max : 2024-07-25 12:25:34
range : 10d 16H 32M 9S
44 distinct values 44 (100.0%) 0 (0.0%)
3 Status [haven_labelled, vctrs_vctr, double] Response Type
Min : 0
Mean : 0
Max : 1
0 : 42 ( 95.5% )
1 : 2 ( 4.5% )
44 (100.0%) 0 (0.0%)
4 IPAddress [character] IP Address
1. 4.7.33.130
2. 151.124.105.58
3. 23.121.35.136
4. (Empty string)
5. 174.212.37.118
6. 208.189.216.118
7. 104.28.32.245
8. 104.28.57.242
9. 104.28.57.245
10. 108.147.177.124
[ 23 others ]
5 ( 11.4% )
3 ( 6.8% )
3 ( 6.8% )
2 ( 4.5% )
2 ( 4.5% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
23 ( 52.3% )
44 (100.0%) 0 (0.0%)
5 Progress [numeric] Progress
Mean (sd) : 98.8 (5.2)
min ≤ med ≤ max:
67 ≤ 100 ≤ 100
IQR (CV) : 0 (0.1)
67 : 1 ( 2.3% )
93 : 3 ( 6.8% )
100 : 40 ( 90.9% )
44 (100.0%) 0 (0.0%)
6 Duration__in_seconds_ [numeric] Duration (in seconds)
Mean (sd) : 389.8 (478.6)
min ≤ med ≤ max:
91 ≤ 239.5 ≤ 2438
IQR (CV) : 234.8 (1.2)
43 distinct values 44 (100.0%) 0 (0.0%)
7 Finished [haven_labelled, vctrs_vctr, double] Finished
Min : 0
Mean : 0.9
Max : 1
0 : 4 ( 9.1% )
1 : 40 ( 90.9% )
44 (100.0%) 0 (0.0%)
8 RecordedDate [POSIXct, POSIXt] Recorded Date
min : 2024-07-14 19:53:26
med : 2024-07-24 19:39:16
max : 2024-07-25 16:42:47
range : 10d 20H 49M 21S
44 distinct values 44 (100.0%) 0 (0.0%)
9 ResponseId [character] Response ID
1. R_1MQmRexm5FUva1l
2. R_1SyHNTKIRt6guHL
3. R_1tmVGOaNybLq2MK
4. R_2bAhoOvBFyDiv2V
5. R_2BjIcZTmo7Gc0tH
6. R_2i3ruILnZQoYDs0
7. R_2ypFt6FHfniYpOs
8. R_3ANFUBbcKNku5Bv
9. R_3efrwTmSqGPnEGd
10. R_3FQHuoffz2qfIeV
[ 34 others ]
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
34 ( 77.3% )
44 (100.0%) 0 (0.0%)
10 RecipientLastName [character] Recipient Last Name
All empty strings
44 (100.0%) 0 (0.0%)
11 RecipientFirstName [character] Recipient First Name
All empty strings
44 (100.0%) 0 (0.0%)
12 RecipientEmail [character] Recipient Email
All empty strings
44 (100.0%) 0 (0.0%)
13 ExternalReference [character] External Data Reference
All empty strings
44 (100.0%) 0 (0.0%)
14 LocationLatitude [character] Location Latitude
1. 30.2712
2. 30.0822
3. (Empty string)
4. 28.3961
5. 30.0843
6. 30.0955
7. 30.1918
8. 28.53
9. 28.5436
10. 28.5588
[ 14 others ]
7 ( 15.9% )
6 ( 13.6% )
4 ( 9.1% )
3 ( 6.8% )
3 ( 6.8% )
2 ( 4.5% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
14 ( 31.8% )
44 (100.0%) 0 (0.0%)
15 LocationLongitude [character] Location Longitude
1. -81.7529
2. -81.5498
3. (Empty string)
4. -81.4681
5. -81.8629
6. -81.6028
7. -81.7198
8. -111.9429
9. -73.8624
10. -74.2688
[ 14 others ]
7 ( 15.9% )
6 ( 13.6% )
4 ( 9.1% )
3 ( 6.8% )
3 ( 6.8% )
2 ( 4.5% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
14 ( 31.8% )
44 (100.0%) 0 (0.0%)
16 DistributionChannel [character] Distribution Channel
1. anonymous
2. preview
42 ( 95.5% )
2 ( 4.5% )
44 (100.0%) 0 (0.0%)
17 UserLanguage [character] User Language 1. EN
44 ( 100.0% )
44 (100.0%) 0 (0.0%)
18 QID1 [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 listen to a song and then will be asked to complete a test. 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 personality. You will have the opportunity to gain a deeper understanding of psychological research. In addition, you will receive information about personality 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 1 distinct value
4 : 44 ( 100.0% )
44 (100.0%) 0 (0.0%)
19 Extroversion_1_1 [haven_labelled, vctrs_vctr, double] When a stranger talks to me, I consider it an opportunity to make a connection. - Select one of the following:
Mean (sd) : 3.2 (1.4)
min ≤ med ≤ max:
1 ≤ 3.5 ≤ 5
IQR (CV) : 2 (0.4)
1 : 8 ( 18.2% )
2 : 5 ( 11.4% )
3 : 9 ( 20.5% )
4 : 14 ( 31.8% )
5 : 8 ( 18.2% )
44 (100.0%) 0 (0.0%)
20 Extroversion_2_1 [haven_labelled, vctrs_vctr, double] I consider myself to be an assertive person. - Select one of the following:
Mean (sd) : 3.8 (1.2)
min ≤ med ≤ max:
1 ≤ 4 ≤ 5
IQR (CV) : 1.2 (0.3)
1 : 2 ( 4.5% )
2 : 7 ( 15.9% )
3 : 2 ( 4.5% )
4 : 19 ( 43.2% )
5 : 14 ( 31.8% )
44 (100.0%) 0 (0.0%)
21 Extroversion_3_1 [haven_labelled, vctrs_vctr, double] I think that being on a reality show would be a nightmare. - Select one of the following:
Mean (sd) : 3.7 (1.5)
min ≤ med ≤ max:
1 ≤ 5 ≤ 5
IQR (CV) : 3 (0.4)
1 : 4 ( 9.1% )
2 : 8 ( 18.2% )
3 : 8 ( 18.2% )
4 : 1 ( 2.3% )
5 : 23 ( 52.3% )
44 (100.0%) 0 (0.0%)
22 Extroversion_4_1 [haven_labelled, vctrs_vctr, double] I'd rather spend one-on-one time with a close friend than get together with a friend group. - Select one of the following:
Mean (sd) : 3.7 (1.4)
min ≤ med ≤ max:
1 ≤ 4 ≤ 5
IQR (CV) : 2.2 (0.4)
1 : 4 ( 9.1% )
2 : 7 ( 15.9% )
3 : 6 ( 13.6% )
4 : 8 ( 18.2% )
5 : 19 ( 43.2% )
44 (100.0%) 0 (0.0%)
23 Extroversion_5_1 [haven_labelled, vctrs_vctr, double] At this moment, I feel like I could go out and make new friends. - Select one of the following:
Mean (sd) : 3.4 (1.3)
min ≤ med ≤ max:
1 ≤ 4 ≤ 5
IQR (CV) : 2 (0.4)
1 : 6 ( 13.6% )
2 : 6 ( 13.6% )
3 : 4 ( 9.1% )
4 : 19 ( 43.2% )
5 : 9 ( 20.5% )
44 (100.0%) 0 (0.0%)
24 Gender [character] Write what gender you identify as:
1. Female
2. Male
3. female
4. Man
5. (Empty string)
6. boy
7. Demi-girl
8. F
9. Femal
10. girl
[ 5 others ]
17 ( 38.6% )
11 ( 25.0% )
3 ( 6.8% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
5 ( 11.4% )
44 (100.0%) 0 (0.0%)
25 Ethnicity [character] Write down your ethnicity:
1. White
2. Caucasian
3. white
4. (Empty string)
5. Middle eastern
6. African American
7. American
8. Asian
9. Black
10. Native American White Hun
[ 3 others ]
24 ( 54.5% )
4 ( 9.1% )
4 ( 9.1% )
2 ( 4.5% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
3 ( 6.8% )
44 (100.0%) 0 (0.0%)
26 Age [character] Write down your age in numerical form:
1. 25
2. 18
3. 20
4. 51
5. 19
6. 21
7. 28
8. 30
9. 40
10. 43
[ 16 others ]
4 ( 9.1% )
3 ( 6.8% )
3 ( 6.8% )
3 ( 6.8% )
2 ( 4.5% )
2 ( 4.5% )
2 ( 4.5% )
2 ( 4.5% )
2 ( 4.5% )
2 ( 4.5% )
19 ( 43.2% )
44 (100.0%) 0 (0.0%)
27 Prediction_ [character] What do you think the hypothesis was?
1. (Empty string)
2. Not sure
3. Are you introverted or ex
4. Does age, race, and ethni
5. Does listening to an upbe
6. Does music affect your mo
7. Does music influence the
8. Does sad music make you s
9. Does the music have any i
10. Effect of music on our mo
[ 30 others ]
4 ( 9.1% )
2 ( 4.5% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
1 ( 2.3% )
30 ( 68.2% )
44 (100.0%) 0 (0.0%)
28 Q_UnansweredPercentage [numeric] Q_UnansweredPercentage
Mean (sd) : 0 (0)
min ≤ med ≤ max:
0 ≤ 0 ≤ 0.2
IQR (CV) : 0 (4)
0.00 : 41 ( 93.2% )
0.10 : 2 ( 4.5% )
0.20 : 1 ( 2.3% )
44 (100.0%) 0 (0.0%)
29 Q_UnansweredQuestions [character] Q_UnansweredQuestions
1. (Empty string)
2. QID13
3. QID13, QID6
41 ( 93.2% )
2 ( 4.5% )
1 ( 2.3% )
44 (100.0%) 0 (0.0%)
30 FL_20_DO_MusicExposure_1song__Genre_somber_ [numeric] FL_20 - Block Randomizer - Display Order MusicExposure(1song),Genre(somber) 1 distinct value
1 : 13 ( 100.0% )
13 (29.5%) 31 (70.5%)
31 FL_20_DO_MusicExposure_1song__Genre_pop_upbeat_ [numeric] FL_20 - Block Randomizer - Display Order MusicExposure(1song),Genre(pop/upbeat) 1 distinct value
1 : 9 ( 100.0% )
9 (20.5%) 35 (79.5%)
32 FL_20_DO_MusicExposure_1minute__Genre_pop_upbeat_ [numeric] FL_20 - Block Randomizer - Display Order MusicExposure(1minute),Genre(pop/upbeat) 1 distinct value
1 : 11 ( 100.0% )
11 (25.0%) 33 (75.0%)
33 FL_20_DO_MusicExposure_1minute__Genre_somber_ [numeric] FL_20 - Block Randomizer - Display Order MusicExposure(1minute),Genre(somber) 1 distinct value
1 : 9 ( 100.0% )
9 (20.5%) 35 (79.5%)
34 age_as_a_number [numeric]
Mean (sd) : 35.7 (15.4)
min ≤ med ≤ max:
18 ≤ 30 ≤ 71
IQR (CV) : 23 (0.4)
25 distinct values 43 (97.7%) 1 (2.3%)
35 exposureIV [character]
1. 1minute
2. 1song
20 ( 47.6% )
22 ( 52.4% )
42 (95.5%) 2 (4.5%)
36 genreIV [character]
1. somber
2. upbeat
22 ( 52.4% )
20 ( 47.6% )
42 (95.5%) 2 (4.5%)
37 Extroversion [numeric]
Mean (sd) : 3.6 (0.7)
min ≤ med ≤ max:
1.6 ≤ 3.6 ≤ 4.8
IQR (CV) : 0.8 (0.2)
14 distinct values 44 (100.0%) 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.4.1)
2024-07-26