growth_schools: Reading growth of students nested in schools

In latents: Multilevel Latent Profile, Class and Transition Models

Reading growth of students nested in schools

Description

A simulated multilevel longitudinal dataset made for the multilevel growth mixture model. 600 students in 40 schools (15 per school) sit a reading test on five occasions (wave 0 to 4). Two kinds of student follow two trajectories: improving (starts near 48, gains 3 points a wave) and plateauing (starts near 56, gains 0.3). Students vary around their trajectory (random intercept SD 3, random slope SD 0.6, correlation -0.2; residual SD 2). Schools are of two types: in supportive schools 80% of students improve, in struggling schools 25% do, and a school's support programme (standardized, measured once) makes it more likely to be supportive. About 8% of tests were missed at random.

Usage

growth_schools

Format

A data frame with 2780 rows (600 students, 40 schools) and 7 columns:

school

Integer school identifier, 1 to 40. Pass it as cluster.

student

Integer student identifier, 1 to 600. Pass it as id.

wave

Integer test occasion, 0 to 4. The time variable.

score

Reading score.

programme

Standardized school support programme, constant within school.

school_type

Factor, "supportive" or "struggling": the school's generating type. Not for fitting; kept to check recovery.

trajectory

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

Details

The school types are what a multilevel growth mixture model's group classes recover: mixture_regression(..., cluster = "school", n_group_classes = 2), with group_membership = "programme".

Source

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

See Also

mixture_regression(), get_results.latents_growth_mixture().

Examples

descriptives(growth_schools, c("score", "programme"))

fit <- mixture_regression(score ~ wave, growth_schools, n_classes = 2,
                          id = "student", class_level = "group",
                          random = "wave", random_covariance = "equal",
                          cluster = "school", n_group_classes = 2,
                          group_membership = "programme",
                          n_starts = 2, seed = 1)
get_results(fit, "group_classes")
get_results(fit, "recovery", data = growth_schools, truth = "trajectory")

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