ArticleEducational and psychological measurement2019
Comparing the Robustness of Stepwise Mixture Modeling With Continuous Nonnormal Distal Outcomes.
Article in Educational and psychological measurement, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Latent classes of self-management behaviors and a network model of influencing factors in community-dwelling older patients with chronic diseases: a healthy aging perspective.Frontiers in public health · 2026Article
- Identifying subtypes and determinants of fall risk perception in older adults: a latent profile analysis.Frontiers in public health · 2026Article
- Primary and Secondary Callous-Unemotional Childhood Variants in a Korean Community Sample.Child psychiatry and human development · 2025Article
- Ethnic enclaves, neighborhood socioeconomic status, and obesity among Hispanic women in Chicago: a latent profile analysis approach.Cancer causes & control : CCC · 2025Article
- A national cross-sectional study on latent profile analysis of occupational fatigue among Chinese nurses in the early post-COVID-19 era.Frontiers in public health · 2024Article
- Cardiovascular disease risk perception among community adults in South China: a latent profile analysis.Frontiers in public health · 2023Article
- Person-centered evaluation of positive body image among Chinese nursing students: Associations with body appreciation, self-concept clarity, and negative affect.Frontiers in psychology · 2022Article
- Relating latent class membership to external variables: An overview.The British journal of mathematical and statistical psychology · 2021Article
- Benefits of Psychological Detachment From Work: Does Autonomous Work Motivation Play a Role?Frontiers in psychology · 2020Article
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3 authors.
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Abstract
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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