330.666.11: Longitudinal Analysis with Latent Variables
Instructors: Hanno Petras, University of Maryland, College Park
Katherine Masyn, University of California, Davis
Wednesday-Friday, June 24-26, 2009
8:30 a.m. 5:30 p.m.
Fee: 3 credits
This course builds upon the two-quarter series on Statistics for Psychosocial Research. It is designed for doctoral students, postdoctoral fellows, and researchers with an interest in the use of latent variables in longitudinal data analysis as it is conceptualized in the modeling framework implemented in the Mplus V5.2 software. Analysis with latent variables is a common theme in mainstream statistics, although the term latent variable is typically not used to describe such analysis. The term latent variable is more typically encountered in psychometric analyses of social and behavioral science data, where latent variables are used to represent variables without measurement error or hypothetical constructs measured by multiple indicators. This course explores more general features of latent variable analyses as they are related to longitudinal modeling. Topics to be covered include latent growth analysis with a combination of continuous and categorical latent variables; continuous and categorical variables as predictors and distal outcomes; discrete- and continuous-time survival analysis. The examples for this course will be drawn from the public data set "Longitudinal Study of American Youth (LSAY)".
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