Comparing the Robustness of Stepwise Mixture Modeling with Continuous Nonnormal Distal Outcomes

Autor/inn/en	Shin, Myungho; No, Unkyung; Hong, Sehee
Titel	Comparing the Robustness of Stepwise Mixture Modeling with Continuous Nonnormal Distal Outcomes
Quelle	In: Educational and Psychological Measurement, 79 (2019) 6, S.1156-1183 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (No, Unkyung)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419839770
Schlagwörter	Robustness (Statistics); Multivariate Analysis; Maximum Likelihood Statistics; Statistical Distributions; Sample Size; Classification + Suchen Sie Ihr Suchwort? Widerstandsfähigkeit; Multivariate Analyse; Wahrscheinlichkeitsverteilung; Classification system; Klassifikation; Klassifikationssystem
Abstract	The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01