Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data

Autor/inn/en	Clark, D. Angus; Nuttall, Amy K.; Bowles, Ryan P.
Titel	Study Length, Change Process Separability, Parameter Estimation, and Model Evaluation in Hybrid Autoregressive-Latent Growth Structural Equation Models for Longitudinal Data
Quelle	In: International Journal of Behavioral Development, 45 (2021) 5, S.440-452 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	ORCID (Clark, D. Angus) ORCID (Nuttall, Amy K.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/01650254211022862
Schlagwörter	Structural Equation Models; Longitudinal Studies; Risk; Accuracy; Goodness of Fit; Monte Carlo Methods; Simulation; Robustness (Statistics); Change; Developmental Stages + Suchen Sie Ihr Suchwort? Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Risiko; Monte-Carlo-Methode; Simulation program; Simulationsprogramm; Widerstandsfähigkeit; Wandel
Abstract	Hybrid autoregressive-latent growth structural equation models for longitudinal data represent a synthesis of the autoregressive and latent growth modeling frameworks. Although these models are conceptually powerful, in practice they may struggle to separate autoregressive and growth-related processes during estimation. This confounding of change processes may, in turn, increase the risk of the models producing deceptively compelling results (i.e., models that fit excellently by conventional standards despite highly biased parameter estimates). Including additional time points provides models with more raw information about change, which could help improve process separability and the accuracy of parameter estimates to a degree. This study thus used Monte Carlo simulation methods to examine associations between change process separability, the number of time points in a model, and the consequences of misspecification, across three prominent hybrid autoregressive-latent growth models: the Latent Change Score model (LCS), the Autoregressive Latent Trajectory Model (ALT), and the Latent Growth Model with Structured Residuals (LGM-SR). Results showed that including more time points increased process separability and robustness to misspecification in the LCS and ALT, but typically not at a rate that would be practically feasible for most developmental researchers. Alternatively, regardless of how many time points were in the model process separability was high in the LGM-SR, as was robustness to misspecification. Overall, results suggest that the LGM-SR is the most effective of the three hybrid autoregressive-latent growth models considered here. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01