Schreiber-Gregory, Deanna (2014). How to use latent analyses of survey data in a logistic regression model. 25th Annual Midwest SAS Users Group Conference.
The current study looks at several ways to investigate latent variables in longitudinal sirveys and their use in logistic regression models. Three different analyses for latent variable discovery will be briefly reviewed and explored. The procedures explored in this paper are PROC LCA, PROC LTA, PROC CA TMOD, PROC FACTOR, PROC TRAJ, and PROC SURVEYLOGISTIC. The analyses defined through these procedures are latent profile analyses, latent class analyses, and latent transition analyses. The latent variables ill then be included in a logistic regression model. The observed data will be reviewed. The data used for this study was obtained via the National Longitudinal Study of Adolescent Health, a study distributed and collected by Add Health. Data was analyzed using SAS 9.3. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in behavioral science or statistics.
25th Annual Midwest SAS Users Group Conference
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