Piecewise Growth Modeling Using SAS PROC MIXED

Autor/inn/en	Tomek, Sara; Robinson, Cecil
Titel	Piecewise Growth Modeling Using SAS PROC MIXED
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 19 (2021) 2, S.140-151 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Tomek, Sara) ORCID (Robinson, Cecil)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2020.1837565
Schlagwörter	Growth Models; Statistical Analysis; Hierarchical Linear Modeling; Computation; Data Interpretation; Computer Software + Suchen Sie Ihr Suchwort? Statistische Analyse; Data evaluation; Datenauswertung
Abstract	Typical longitudinal growth models assume constant functional growth over time. However, there are often conditions where trajectories may not be constant over time. For example, trajectories of psychological behaviors may vary based on a participant's age, or conversely, participants may experience an intervention that causes trajectories to change. Specifically, this article outlines how to build and estimate "piecewise growth models" (PGM) using SAS PROC MIXED in order to estimate discontinuous growth models over time in a hierarchical linear model (HLM) framework. Details of data coding schemes, model parameterization, analysis using SAS PROC MIXED, and interpretation of parameter estimates will be discussed using a simulated education intervention data set. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01