Time Varying Covariates in Markov Latent Class Analysis: Some Problems and Solutions

• Activity Number: 186
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Marcus E. Berzofsky*+ and Paul Biemer and William D. Kalsbeek
• Companies: RTI International and RTI International and The University of North Carolina at Chapel Hill
• Keywords: Markov Latent Class Analysis ; measurement error ; classification error ; screener questions ; National Crime Victimization Survey ; time varying covariates

Abstract

Markov latent class analysis (MLCA) is a modeling technique for panel or longitudinal data that can be used to estimate the classification error rates for categorical outcomes with categorical predictors when gold standard measurements are not available. Because panel surveys track respondents over time, explanatory variables can be either time varying or time invariant. Time varying grouping variables can be useful in explaining differences in the latent construct over time. However, they generate a large number of model parameters that can make model results unreliable. This paper discusses alternative coding schemes for time varying grouping variables and proposes a set of procedures for determining the best coding scheme for a particular set of data. This process is then illustrated using data from the National Crime Victimization Survey (NCVS).