A Monte Carlo Investigation of the Likelihood Ratio Test for Number of Classes in Latent Class Analysis

1988 ◽  
Vol 23 (4) ◽  
pp. 531-538 ◽  
Author(s):  
B.S. Everitt
Keyword(s):  
Monte Carlo ◽  
Latent Class Analysis ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Latent Class ◽  
Ratio Test ◽  
Class Analysis ◽  
Monte Carlo Investigation ◽  
Number Of Classes

scholarly journals Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes

2019 ◽  
Vol 79 (6) ◽  
pp. 1156-1183 ◽  
Author(s):  
Myungho Shin ◽  
Unkyung No ◽  
Sehee Hong
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Mixture Modeling ◽  
Simulation Studies ◽  
Class Analysis ◽  
Modeling Approaches ◽  
Unbiased Estimates ◽  
Distal Outcome

The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.

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