Utility of a slopes difference test for probing longitudinal multilevel aptitude treatment interactions: a simulation.

aptitude-treatment interaction longitudinal multilevel precision education simulation skill-by-treatment interaction slopes difference test

Journal

Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902

Informations de publication

Date de publication:
2023
Historique:
received: 02 02 2023
accepted: 01 06 2023
medline: 13 7 2023
pubmed: 13 7 2023
entrez: 13 7 2023
Statut: epublish

Résumé

To determine which interventions work best for which students, precision education researchers can examine aptitude-treatment interactions (ATI) or skill-by-treatment interactions (STI) using longitudinal multilevel modeling. Probing techniques like the slopes difference test fit an ATI or STI framework, but power for using slopes difference tests in longitudinal multilevel modeling is unknown. The current study used simulation to determine which design factors influence the power of slopes difference tests. Design factors included effect size, number of waves, number of clusters, participants per cluster, proportion of assignment to the treatment group, and intraclass correlation. Of these factors, effect size, number of waves, number of clusters, and participants per cluster were the strongest determinants of power, model convergence, and rates of singularity. Slopes difference tests had greater power in longitudinal multilevel modeling than where it is originally utilized: multiple regression.

Identifiants

pubmed: 37441330
doi: 10.3389/fpsyg.2023.1156962
pmc: PMC10335001
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1156962

Informations de copyright

Copyright © 2023 DeJong and Chen.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Trey L DeJong (TL)

Department of Mathematics and Statistics, Center for Interdisciplinary Statistical Education and Research, Washington State University, Pullman, WA, United States.

Qi Chen (Q)

Department of Educational Psychology, The College of Education, University of North Texas, Denton, TX, United States.

Classifications MeSH