student_esm: Leisure activities of university students in daily life
In latents: Multilevel Latent Profile, Class and Transition Models

student_esm

R Documentation

Leisure activities of university students in daily life

Description

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.

Usage

student_esm

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.

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.

Source

openESM dataset 0062, deposited by Neubauer and Schmiedek at Zenodo, \Sexpr[results=rd]{tools:::Rd_expr_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.

References

Neubauer, A. B., & Schmiedek, F. (2024). Approaching academic adjustment on multiple time scales. Zeitschrift für Erziehungswissenschaft, 27, 147–168. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11618-023-01182-8")}

Examples

head(student_esm)

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")
get_results(lca, what = "profile_probabilities")

latents documentation built on Oct. 16, 2026, 5:09 p.m.