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|Other Titles:||Multilevel LCA Mixture Analysis of Interpersonal Conflicts among Middle School Students|
multilevel latent class analysis;multilevel mixture model;interpersonal conflicts
|Issue Date:||2016-08-09 16:05:54 (UTC+8)|
Conventional latent class analysis (LCA) classifies subjects into various categories by analyzing their response patterns to observed variables. However, multilevel latent class analysis (MLCA) is methodologically more appropriate when data are collected from a nested structure sample. Primary strength of MLCA is to analyze student and classroom levels of data simultaneously while taking the nested structure of the data into account. This study utilized and compared two different MLCA approaches including nonparametric MLCA and MLCA mixture models. Gender and proportion of boys in class are level 1 and level 2 covariates, respectively. Data were collected from 85 junior high classes with a total of 2,783 7th to 9th grade students. The observed variables include five binary self-reported survey questions regarding experiences related to interpersonal conflicts at school. All analyses were carried out by using Mplus6.0. Results show that non-parametric MLCA and MLCA mixture models fit the data equally well. Students are clustered into three categories namely "peaceful", "vocal" and "behavioral" of interpersonal conflicts. Boys are more likely to be classified as "behavioral" conflict while girls are more likely to be classified as "vocal" conflict. Nonparametric model clusters level 2 random intercepts as one class, whereas mixture model extracts the covariance among five level 2 random indicators as single factor possibly due to small number of level 2 units. Results of nonparametric and mixture MLCA were also discussed. Empirical implications as well as methodological challenges applying MLCA nonparametric and mixture models, are discussed in the end of the study.
Journal of Education & Psychology
|Appears in Collections:||[教育與心理研究 TSSCI] 期刊論文|
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