Ross L. Matsueda
Structural equation models for the social sciences, including specification, estimation, and testing. Topics include path analysis, confirmatory factor analysis, linear models with latent variables, MIMIC models, non-recursive models, models for nested data. Emphasizes applications to substantive problems in the social sciences. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent; recommended: either CS&SS 505 and CS&SS 506, or equivalent. Offered: jointly with CS&SS 526.
This course introduces covariance structure analysis, focusing on Jöreskog and Sörbom's LISREL approach. It begins with the notion of a causal structure underlying a set of observable moments (covariances). This notion is illustrated briefly with path analysis applied to a multiple equation recursive model of observable variables. We will discuss the implications of random measurement error in linear regression models, discuss the concept of unobservable variables, and review some elementary principles of classical test theory. We then introduce the LISREL model, describing the model in matrix form, and then briefly present the estimation issue and use of maximum likelihood estimation and likelihood ratio testing. We will discuss the LISREL and PRELIS software programs, and briefly discuss Muthén’s Mplus Program. We will then examine the identification issue and examine specific classes of models, such as confirmatory factor models, MIMIC models, regression models with latent variables, and non-recursive models. We will also cover estimation when observed data are not multivariate normal, and observed variables are ordinal, dichotomous, or censored. Time permitting, we will survey other models used in recent research in the social sciences, such as models for panel data, growth curve models, models for nested data, mixture models for latent class and trajectory analysis, and models for data missing at random.
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