ABSTRACT

Factor analysis (FA) is very widely used across many research disciplines. FA models link a set of observed variables (e.g., items on a scale and subscales from an assessment battery) to one or more latent traits that the observed variables are believed to measure. This chapter explores the modeling of indicators in the exploratory factor analysis (EFA) framework by using Mplus. It shows how to interpret the results from such an analysis. There exists a second type of factor analysis, confirmatory factor analysis (CFA), which does place constraints on the factor model that are not present in EFA. When interpreting the results from an latent class analysis (LCA) model, the chapter focuses on several different pieces of information related to the parameters of interest, the proportion of individuals in each latent class, and the class-specific means of the observed indicators.