Analyzing data from experimental studies: A latent variable structural equation modeling approach

This article illustrates the use of structural equation modeling (SEM) procedures with latent variables to analyze data from experimental studies. These procedures allow the researcher to remove the biasing effects of random and correlated measurement error on the outcomes of the experiment and to examine processes that may account for changes in the outcome variables that are observed. Analyses of data from a Project Family study, an experimental intervention project with rural families that strives to improve parenting skills, are presented to illustrate the use of these modeling procedures. Issues that arise in applying SEM procedures, such as sample size and distributional characteristics of the measures, are discussed.