A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics

Autor/inn/en	Edelsbrunner, Peter A.; Flaig, Maja; Schneider, Michael
Titel	A Simulation Study on Latent Transition Analysis for Examining Profiles and Trajectories in Education: Recommendations for Fit Statistics
Quelle	In: Journal of Research on Educational Effectiveness, 16 (2023) 2, S.350-375 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Edelsbrunner, Peter A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2022.2118197
Schlagwörter	Learning Processes; Profiles; Monte Carlo Methods; Bayesian Statistics; Evaluation Criteria; Sample Size; Computation; Educational Research; Robustness (Statistics) + Suchen Sie Ihr Suchwort? Learning process; Lernprozess; Charakterisierung; Profilanalyse; Monte-Carlo-Methode; Bildungsforschung; Pädagogische Forschung; Widerstandsfähigkeit
Abstract	Latent transition analysis is an informative statistical tool for depicting heterogeneity in learning as latent profiles. We present a Monte Carlo simulation study to guide researchers in selecting fit indices for identifying the correct number of profiles. We simulated data representing profiles of learners within a typical pre- post- follow up-design with continuous indicators, varying sample size (N from 50 to 1,000), attrition rate (none/10% per wave), and profile separation (entropy; from 0.73 to 0.87). Results indicate that the most commonly used fit index, the Bayesian information criterion ("BIC"), and the consistent Akaike information criterion ("CAIC") consistently underestimate the real number of profiles. A combination of the "AIC" or the "AIC3" with the adjusted Bayesian Information Criterion ("aBIC") provides the most precise choice for selecting the number of profiles and is accurate with sample sizes of at least N = 200. The "AIC3" excels starting from N = 500. Results were mostly robust toward differing numbers of time points, profiles, indicator variables, and alternative profiles. We provide an online tool for computing these fit indices and discuss implications for research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01