Procedures for Analyzing Multidimensional Mixture Data

Hsu Lin Su, Po Hsi Chen*


研究成果: 雜誌貢獻期刊論文同行評審


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.

頁(從 - 到)1173-1201
期刊Educational and Psychological Measurement
出版狀態已發佈 - 2023 12月

ASJC Scopus subject areas

  • 教育
  • 發展與教育心理學
  • 應用心理學
  • 應用數學


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