A Comparison of Mixture Modeling Approaches in Latent Class Models with External Variables under Small Samples

Autor/inn/en	No, Unkyung; Hong, Sehee
Titel	A Comparison of Mixture Modeling Approaches in Latent Class Models with External Variables under Small Samples
Quelle	In: Educational and Psychological Measurement, 78 (2018) 6, S.925-951 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164417726828
Schlagwörter	Sample Size; Classification; Comparative Analysis; Statistical Analysis; Predictor Variables; Accuracy; Efficiency; Computation + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Statistische Analyse; Prädiktor; Effectiveness; Effektivität; Wirkungsgrad
Abstract	The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the three-step approaches even in poorly manipulated conditions indicate the advantage of these approaches. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01