An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures

May 19, 2011

Levy, R., & Hancock, G. (2011). An Extended Model Comparison Framework for Covariance and Mean Structure Models, Accommodating Multiple Groups and Latent Mixtures Sociological Methods & Research, 40 (2), 256-278 DOI: 10.1177/0049124111404819 

Abstract 

Roy Levy, Arizona State University, Tempe, AZ, USA, roy.levy@asu.edu


A New Mixture Model for Misclassification With Applications for Survey Data

February 7, 2010

Simon Cheng, Yingmei Xi, and Ming-Hui Chen A New Mixture Model for Misclassification With Applications for Survey Data  Sociological Methods & Research 2008 37: 75-104.

Social scientists often rely on survey data to examine group differences. A problem with survey data is the potential misclassification of group membership due to poorly trained interviewers, inconsistent responses, or errors in marking questions. In data containing unequal subsample sizes, Read the rest of this entry »


Jeroen K. Vermunt and Jay Magidson Latent Class Analysis With Sampling Weights: A Maximum-Likelihood Approach Sociological Methods & Research 2007 36: 87-111.

February 7, 2010

The authors illustrate how to perform maximum-likelihood estimation in latent class (LC) analysis when there are sampling weights. The methods are natural extensions of the approaches proposedby Clogg and Eliason (1987) and Magidson (1987) for dealing with sampling weights in the log-linear analysis of frequency tables. For the log-linear form of the LC model, the approachcorresponds to a special case of Haberman’s (1979) log-linear LC model with cell weights. This approach can also be applied to the probability formulation of the LC model with cell weights,which can accommodate many indicators. The authors propose an efficient estimation-maximization algorithm for estimating the parameters for this formulation. A small simulation study shows that the probability estimates obtained by this approach compare favorably to other weighting approaches. Several empirical examples are provided to illustrate various possible weighting methods in LC analysis.

Key Words: latent class analysis • mixture model • complex sampling • log-linear analysis • EM algorithm