growth_scores: Achievement growth of students with three kinds of trajectory

In latents: Multilevel Latent Profile, Class and Transition Models

Achievement growth of students with three kinds of trajectory

Description

A simulated longitudinal dataset made for growth models. 300 students sit an achievement test on up to seven occasions (wave 0 to 6). Three kinds of student follow three trajectories: improving (starts near 48, gains 3.5 points a wave), stable (starts near 62, gains 0.5) and declining (starts near 58, loses 2.5). Within each kind, students vary around the trajectory: each has their own start and rate of change (random intercept SD 4, random slope SD 0.8, correlation -0.3, the same spread in every kind), with residual SD 3. Motivation, measured once, makes a student more likely to be improving and less likely to be declining. About 12% of tests were missed at random, so the panel is unbalanced.

Usage

growth_scores

Format

A data frame with 1844 rows (300 students) and 5 columns:

student

Integer student identifier, 1 to 300. Pass it as id.

wave

Integer test occasion, 0 to 6. The time variable.

score

Achievement score.

motivation

Standardized motivation, constant within student.

trajectory

Factor, "improving", "stable" or "declining": the student's generating kind. Not for fitting; kept to check recovery.

Details

Because students vary within their kind, a trajectory model without random effects needs more classes than the three that generated the data; a growth mixture model with random intercepts and slopes recovers three.

Source

Simulated by data-raw/growth-scores.R in the source repository.

See Also

mixture_regression(), compare_models().

Examples

descriptives(growth_scores, c("score", "motivation"))

fit <- mixture_regression(score ~ wave, growth_scores, n_classes = 3,
                          id = "student", class_level = "group",
                          random = "wave", random_covariance = "equal",
                          n_starts = 4, seed = 1)
get_results(fit, "recovery", data = growth_scores, truth = "trajectory")

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