Deciding on the number of classes in latent class analysis and growth mixture modeling:: A Monte Carlo simulation study

被引:8063
作者
Nylund, Karen L. [1 ]
Asparoutiov, Tihomir [1 ]
Muthen, Bengt O. [1 ]
机构
[1] Univ Calif Los Angeles, Grad Sch Educ & Informat Studies, Los Angeles, CA 90095 USA
关键词
D O I
10.1080/10705510701575396
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Mixture modeling is a widely applied data analysis technique used to identify unobserved heterogeneity in a population. Despite mixture models' usefulness in practice, one unresolved issue in the application of mixture models is that there is not one commonly accepted statistical indicator for deciding on the number of classes in a study population. This article presents the results of a simulation study that examines the performance of likelihood-based tests and the traditionally used Information Criterion (ICs) used for determining the number of classes in mixture modeling. We look at the performance of these tests and indexes for 3 types of mixture models: latent class analysis (LCA), a factor mixture model (FMA), and a growth mixture models (GMM). We evaluate the ability of the tests and indexes to correctly identify the number of classes at three different sample sizes (n = 200, 500, 1,000). Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.
引用
收藏
页码:535 / 569
页数:35
相关论文
共 35 条
[1]   FACTOR-ANALYSIS AND AIC [J].
AKAIKE, H .
PSYCHOMETRIKA, 1987, 52 (03) :317-332
[2]  
Bollen K. A., 1989, Structural equations with latent variables, DOI DOI 10.1002/9781118619179
[4]   A latent class analysis of antisocial personality disorder symptom data from a multi-centre family study of alcoholism [J].
Bucholz, KK ;
Hesselbrock, VM ;
Heath, AC ;
Kramer, JR ;
Schuckit, MA .
ADDICTION, 2000, 95 (04) :553-567
[5]   An entropy criterion for assessing the number of clusters in a mixture model [J].
Celeux, G ;
Soromenho, G .
JOURNAL OF CLASSIFICATION, 1996, 13 (02) :195-212
[6]   GOODNESS-OF-FIT TESTING FOR LATENT CLASS MODELS [J].
COLLINS, LM ;
FIDLER, PL ;
WUGALTER, SE ;
LONG, JD .
MULTIVARIATE BEHAVIORAL RESEARCH, 1993, 28 (03) :375-389
[7]   Latent class model diagnostics - a review and some proposals [J].
Formann, AK .
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2003, 41 (3-4) :549-559
[8]   Latent class model diagnosis [J].
Garrett, ES ;
Zeger, SL .
BIOMETRICS, 2000, 56 (04) :1055-1067
[9]   Variation in the drinking trajectories of freshmen college students [J].
Greenbaum, PE ;
Del Boca, FK ;
Darkes, J ;
Wang, CP ;
Goldman, MS .
JOURNAL OF CONSULTING AND CLINICAL PSYCHOLOGY, 2005, 73 (02) :229-238
[10]  
Hagenaars JA., 2002, Applied latent class analysis, DOI [DOI 10.1017/CBO9780511499531, 10.1017/cbo9780511499531]