Latent Class Analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical and/or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this paper, a new methodology, multilevel latent class analysis (MLCA), is described and an applied example is presented. Latent classes of cigarette smoking among 10,772 European American females in 9th grade who live in one of 206 rural communities across the U.S. are considered. A parametric and non-parametric approach for estimating a MLCA are presented and both individual and contextual predictors of the smoking typologies are assessed. Both latent class and indicator-specific random effects models are explored. The best model was comprised of three Level 1 latent smoking classes (heavy smokers, moderate smokers, non-smokers), two random effects to account for variation in the probability of Level 1 latent class membership across communities, and a random factor for the indicator-specific Level 2 variances. Several covariates at the individual and contextual level were useful in predicting latent classes of cigarette smoking as well as the individual indicators of the latent class model. This paper will assist researchers in estimating similar models with their own data.Latent Class Analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical and/or continuous observed variables. These subtypes are referred to as latent classes. The classes are latent in that the subtypes are not directly observed; rather they are inferred from the multiple observed indicators. This method has been used to answer many interesting research questions in the behavioral sciences. For example, recent applications of LCA have assessed alcohol dependence subtypes (Moss, Chen, & Yi, 2007), peer victimization subtypes (Nylund, Bellmore, Nishina, & Graham, 2007), and gambling subtypes (Cunningham-Williams & Hong, 2007).Traditional LCA assumes that observations are independent of one another. However, in many data structures this assumption is not fulfilled. For example, observations are not independent when the data structure includes students nested in schools, children nested in families, or employees nested in companies. These nested data structures require multilevel techniques. In response to these needs, Vermunt (2003Vermunt ( , 2008 and presented a framework for assessing latent class models with nested data.Consider a Level 1 latent class solution for cigarette smoking typologies that describes three types of smokers: heavy smokers, moderate smokers, and non-smokers. If these individuals were randomly selected from the population, then a traditional, fixed effects latent class NIH Public Access
Author ManuscriptStruct Equ Modeling. Author manuscript; available in PMC 2011 April 1.
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