Semi-Nonparametric Methods for Detecting Latent Non-normality: A Fusion of Latent Trait and Ordered Latent Class Modeling

@article{Schmitt2006SemiNonparametricMF,
  title={Semi-Nonparametric Methods for Detecting Latent Non-normality: A Fusion of Latent Trait and Ordered Latent Class Modeling},
  author={J. Eric Schmitt and Paras D Mehta and Steven H. Aggen and Thomas S. Kubarych and Michael C. Neale},
  journal={Multivariate Behavioral Research},
  year={2006},
  volume={41},
  pages={427 - 443}
}
Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation, we compared a model in which probabilities of class membership were estimated to a restricted submodel in which class memberships were fixed to normal… 
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27 Citations
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5
Methods Citations
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27 Citations

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