TOC: Struct Eqn Modeling


Structural Equation Modeling: A Multidisciplinary Journal, 24(2)

Novel Approaches in Mixture Modeling
Gitta Lubke & Kevin J. Grimm [Publisher] [Google Scholar]

An Empirical Assessment of the Sensitivity of Mixture Models to Changes in Measurement
Veronica T. Cole, Daniel J. Bauer, Andrea M. Hussong & Michael L. Giordano [Publisher] [Google Scholar]

Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling
Katherine E. Masyn [Publisher] [Google Scholar]

Using Bayesian Statistics to Model Uncertainty in Mixture Models: A Sensitivity Analysis of Priors
Sarah Depaoli, Yuzhu Yang & John Felt [Publisher] [Google Scholar]

Power and Type I Error of Local Fit Statistics in Multilevel Latent Class Analysis
Erwin Nagelkerke, Daniel L. Oberski & Jeroen K. Vermunt [Publisher] [Google Scholar]

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Gitta H. Lubke, Ian Campbell, Dan McArtor, Patrick Miller, Justin Luningham & Stéphanie M. van den Berg [Publisher] [Google Scholar]

Model Selection in Finite Mixture Models: A k-Fold Cross-Validation Approach
Kevin J. Grimm, Gina L. Mazza & Pega Davoudzadeh [Publisher] [Google Scholar]

Dynamic Latent Class Analysis
Tihomir Asparouhov, Ellen L. Hamaker & Bengt Muthén [Publisher] [Google Scholar]

A Comparison of Methods for Uncovering Sample Heterogeneity: Structural Equation Model Trees and Finite Mixture Models
Ross Jacobucci, Kevin J. Grimm & John J. McArdle [Publisher] [Google Scholar]

Pattern Mixture Models for Quantifying Missing Data Uncertainty in Longitudinal Invariance Testing
Sonya K. Sterba [Publisher] [Google Scholar]

Studying the Strength of Prediction Using Indirect Mixture Modeling: Nonlinear Latent Regression with Heteroskedastic Residuals | Open Access
Johanna M. de Kort, Conor V. Dolan, Gitta H. Lubke & Dylan Molenaar [Publisher] [Google Scholar]