Experience-sampling data on what university students did between prompts when they were not studying. Prompts are nested within students, so the data suit a two-level latent class analysis: each prompt belongs to an activity profile, and students differ in how often their prompts fall in each profile.
Format
A data frame with 2582 rows (100 students) and 15 columns:
- student
Integer student identifier, 1 to 100. The nesting unit; pass it as
id.- day
Integer study day, 0 to 13.
- beep
Integer prompt of the day, 1 to 5 among the prompts kept.
- time_with_friends
Factor,
"no"or"yes": spent time with friends since the previous prompt.- on_social_media
Factor,
"no"or"yes": used social media.- tv_video_games
Factor,
"no"or"yes": watched TV or played video games.- listened_music
Factor,
"no"or"yes": listened to music.- sports
Factor,
"no"or"yes": did sports.- walking
Factor,
"no"or"yes": went for a walk.- reading
Factor,
"no"or"yes": read.- part_time_job
Factor,
"no"or"yes": worked in a part-time job.- happy
Integer rating of feeling happy, 1 to 7.
- relaxed
Integer rating of feeling relaxed, 1 to 7.
- worried
Integer rating of feeling worried, 1 to 7.
- exhausted
Integer rating of feeling exhausted, 1 to 7.
Source
openESM dataset 0062, deposited by Neubauer and Schmiedek at
Zenodo, doi:10.5281/zenodo.17347974
, under the Creative Commons
Attribution 4.0 licence (CC-BY 4.0), and retrieved through the openESM
database (https://openesmdata.org). The subset is produced by
data-raw/student-esm.R in the source repository; identifiers are
renumbered and the values are otherwise unchanged.
Details
The students answered six prompts a day for fourteen days. At a prompt where the student had not studied since the previous prompt, the questionnaire asked which of eight leisure activities they had done. The study asked these items only at non-study prompts, so the dataset contains those prompts alone. Each row is one answered non-study prompt with all eight activity items and all four affect ratings observed.
The dataset is a subset of the original study: 100 of its students, drawn
with a fixed seed from the students with at least 15 such prompts, with every
such prompt they answered. Each student contributes 15 to 51 prompts (median
24). The eight activity items are categorical indicators; the four affect
ratings are continuous-scored and can be combined with them in a mixed
measurement model. worried has a floor: 49.7% of its ratings are 1, so a
profile of low-worry prompts can have zero variance on it and reach the
min_variance bound.
References
Neubauer, A. B., & Schmiedek, F. (2024). Approaching academic adjustment on multiple time scales. Zeitschrift für Erziehungswissenschaft, 27, 147–168. doi:10.1007/s11618-023-01182-8
See also
multilpa() with categorical = for the two-level latent class
model.
Examples
head(student_esm)
#> student day beep time_with_friends on_social_media tv_video_games
#> 1 1 0 3 yes yes yes
#> 2 1 0 5 no no yes
#> 3 1 1 1 no no no
#> 4 1 1 3 no no yes
#> 5 1 1 4 no no yes
#> 6 1 2 3 no no no
#> listened_music sports walking reading part_time_job happy relaxed worried
#> 1 no yes no yes no 5 6 5
#> 2 no no no no no 5 6 2
#> 3 no no no no no 3 3 2
#> 4 no no no no no 6 5 2
#> 5 no no no no no 4 5 1
#> 6 no no no no no 5 4 5
#> exhausted
#> 1 3
#> 2 5
#> 3 7
#> 4 4
#> 5 6
#> 6 7
# \donttest{
activities <- c("time_with_friends", "on_social_media", "tv_video_games",
"listened_music", "sports", "walking", "reading",
"part_time_job")
first_week <- subset(student_esm, day <= 6)
lca <- multilpa(first_week, vars = activities, id = "student",
n_profiles = 2, n_group_classes = 2,
categorical = activities, n_starts = 3, seed = 1)
get_results(lca, what = "responses")
#> profile indicator category probability threshold
#> 1 1 time_with_friends no 0.83242068 1.6028812
#> 2 2 time_with_friends no 0.93678704 2.6959466
#> 3 1 time_with_friends yes 0.16757932 NA
#> 4 2 time_with_friends yes 0.06321296 NA
#> 5 1 on_social_media no 0.92355812 2.4917031
#> 6 2 on_social_media no 0.33649814 -0.6789390
#> 7 1 on_social_media yes 0.07644188 NA
#> 8 2 on_social_media yes 0.66350186 NA
#> 9 1 tv_video_games no 0.86644478 1.8698834
#> 10 2 tv_video_games no 0.63132094 0.5378878
#> 11 1 tv_video_games yes 0.13355522 NA
#> 12 2 tv_video_games yes 0.36867906 NA
#> 13 1 listened_music no 0.94058177 2.7618976
#> 14 2 listened_music no 0.64640069 0.6032552
#> 15 1 listened_music yes 0.05941823 NA
#> 16 2 listened_music yes 0.35359931 NA
#> 17 1 sports no 0.91593266 2.3883247
#> 18 2 sports no 0.94060224 2.7622638
#> 19 1 sports yes 0.08406734 NA
#> 20 2 sports yes 0.05939776 NA
#> 21 1 walking no 0.83653079 1.6326387
#> 22 2 walking no 0.88653607 2.0558368
#> 23 1 walking yes 0.16346921 NA
#> 24 2 walking yes 0.11346393 NA
#> 25 1 reading no 0.89169019 2.1081230
#> 26 2 reading no 0.87897002 1.9827125
#> 27 1 reading yes 0.10830981 NA
#> 28 2 reading yes 0.12102998 NA
#> 29 1 part_time_job no 0.95867090 3.1439809
#> 30 2 part_time_job no 0.98375254 4.1034381
#> 31 1 part_time_job yes 0.04132910 NA
#> 32 2 part_time_job yes 0.01624746 NA
#> probability_standard_error
#> 1 0.015245913
#> 2 0.012813326
#> 3 0.015245913
#> 4 0.012813326
#> 5 0.026626917
#> 6 0.045890541
#> 7 0.026626917
#> 8 0.045890541
#> 9 0.016048843
#> 10 0.028907924
#> 11 0.016048843
#> 12 0.028907924
#> 13 0.014189478
#> 14 0.031664343
#> 15 0.014189478
#> 16 0.031664343
#> 17 0.010533576
#> 18 0.012075931
#> 19 0.010533576
#> 20 0.012075931
#> 21 0.014230522
#> 22 0.016565859
#> 23 0.014230522
#> 24 0.016565859
#> 25 0.011899678
#> 26 0.016844680
#> 27 0.011899678
#> 28 0.016844680
#> 29 0.007300081
#> 30 0.006253449
#> 31 0.007300081
#> 32 0.006253449
get_results(lca, what = "profile_probabilities")
#> group_class profile probability group_class_probability
#> 1 1 1 0.29188874 0.4828372
#> 2 1 2 0.70811126 0.4828372
#> 3 2 1 0.92482149 0.5171628
#> 4 2 2 0.07517851 0.5171628
# }