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en  Introduction to latent variable modeling with Structural Equation Models using Mplus

Kursleitung: Prof. Dr. Rolf Steyer

Sommersemester 2009, Workshop, Kurslänge: 11.25 Stunden, Sprache: Englisch, Thema: Introduction to structural equation modeling

This Workshop is a first introduction into structural equation modeling with latent variables. The first day, we introduce the basic concepts of Classical Test Theory (CTT) such as true-score and error variables and present the properties of these concepts. Next, we treat the models of CTT such as the models of parallel, tau-equivalent, and congeneric variables, and show how to analyze them with Mplus. This includes parameter estimation, testing hypotheses about these parameters, and testing the implications of the models, both for the total population and subpopulations via SEM multi-group analyses.

On the second day, we extend CTT to Latent State-Trait Theory (LST Theory), considering measurements at two or more occasions of measurement. On the theoretical side, we introduce the basic concepts such as latent state variables, latent trait variables, latent state residuals, and measurement error. Then we deal with the standard models of LST Theory: the singletrait model, the multistate model, and the singletrait-multistate model, thus introducing models with several latent variables. These models are then generalized to include method factors. Finally, we introduce a multiconstruct model, which combines two or more singletrait-multistate models with method factors.



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Videos


Session Video-presentation with slides Blackboard pictures
Thursday, April 02, 2009
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Session 01 Video with slides 01
Session 02 Video with slides 02
Session 03 Video with slides 03
Session 04 Video with slides 04
Session 05 Video with slides 05
Friday, April 03, 2009
Session 06 Video with slides 06
Session 07 Video with slides 07
Session 08 Video with slides 08
Session 09 Video with slides 09


Material

Slides
Readings on CTT and SEM
Readings on LST
Readings on Method Factors
Readings - Additional
Datasets