The Impact of Ignoring a Level of Nesting Structure in Multilevel Mixture Model: A Monte Carlo Study

SAGE Open

Qi Chen¹ 

关键词: multilevel mixture model;    finite mixture model;    multilevel modeling;    intraclass correlation;   

DOI  :  10.1177/2158244012442518

学科分类：社会科学、人文和艺术（综合）

来源: Sage Journals

【 摘 要 】

Mixture modeling has gained more attention among practitioners and statisticians in recent years. However, when researchers analyze their data using finite mixture model (FMM), some may assume that the units are independent of each other even though it may not always be the case. This article used simulation studies to examine the impact of ignoring a higher nesting structure in multilevel mixture models. Results indicate that the misspecification results in lower classification accuracy of individuals, less accurate fixed effect estimates, inflation of lower level variance estimates, and less accurate standard error estimates in each subpopulation, the latter result of which in turn affects the accuracy of tests of significance for the fixed effects. The magnitude of the intraclass correlation (ICC) coefficient has a substantial impact. The implication for applied researchers is that it is important to model the multilevel data structure in mixture modeling.

【 授权许可】

CC BY