Procedures for Analyzing Multidimensional Mixture Data

Autor/inn/en	Su, Hsu-Lin; Chen, Po-Hsi
Titel	Procedures for Analyzing Multidimensional Mixture Data
Quelle	In: Educational and Psychological Measurement, 83 (2023) 6, S.1173-1201 (29 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Su, Hsu-Lin) ORCID (Chen, Po-Hsi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644231151470
Schlagwörter	Data Analysis; Correlation; Classification; Factor Structure; Accuracy; Computation + Suchen Sie Ihr Suchwort? Auswertung; Korrelation; Classification system; Klassifikation; Klassifikationssystem; Faktorenstruktur
Abstract	The multidimensional mixture data structure exists in many test (or inventory) conditions. Heterogeneity also relatively exists in populations. Still, some researchers are interested in deciding to which subpopulation a participant belongs according to the participant's factor pattern. Thus, in this study, we proposed three analysis procedures based on the factor mixture model to analyze data in the multidimensional mixture context. Simulations were manipulated with different levels of factor numbers, factor correlations, numbers of latent classes, and class separation. Issues with regard to model selection were discussed at first. The results showed that in the two-class situations the procedures of "factor structure first then class number" (Procedure 1) and "factor structure and class number considered simultaneously" (Procedure 3) performed better than the "class number first then factor structure" (Procedure 2) and yielded precise parameter estimation and classification accuracy. It would be appropriate to choose Procedures 1 and 3 when strong measurement invariance is assumed while using an information criterion, but Procedure 1 saved more time than Procedure 3. In the three-class situations, the performance of all three procedures was limited. Implementations and suggestions have been addressed in this research. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01