Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes
| Autor: | Sehee Hong, Unkyung No, Myungho Shin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Heteroscedasticity
Applied Mathematics 05 social sciences Monte Carlo method Robust statistics 050401 social sciences methods 01 natural sciences Latent class model Article Education 010104 statistics & probability 0504 sociology Sample size determination Homoscedasticity Statistics Developmental and Educational Psychology Probability distribution 0101 mathematics Applied Psychology BCH code Mathematics |
| Zdroj: | Educ Psychol Meas |
| ISSN: | 1552-3888 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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