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Latent Variable Modeling

Course code
Course type
Doctoral Program Lecture
Weekly Hours
HS 2022
Prof. Dr. Walter Herzog
Please note that exchange students obtain a higher number of credits in the BSc-program at WHU than listed here. For further information please contact directly the International Relations Office.

The following topics are discussed:

- Observed and latent variables
- Classical latent variable theory and factor-analytic paradigm
- Exploratory and confirmatory factor analysis
- Observed and implied covariance structures
- Model identification
- Maximum likelihood estimation
- Model fit statistics (test of exact fit, test of close fit, noncentrality-based fit statistics, incremental fit indexes, residual-based fit indexes)
- Null hypothesis testing and confidence intervals for parameters
- Estimation of reliability and validity measures
- Mediation and moderation analyses
- Robustness against nonnormality and small sample sizes
- Trends: Reflective versus formative latent variables, common method bias, etc.
- Causality
- Latent variable models and experimental data
- Overview: Extensions of the basic latent variable model (e.g., mixture modeling)

Note: We currently plan to offer the course in person (room C-107, Campus Vallendar).

Date Time
Thursday, 29.09.2022 08:00 - 19:00
Friday, 30.09.2022 08:00 - 19:00
Participants understand the statistical foundations of the basic latent variable model, are able to estimate models using the Mplus software program, interpret software output, and defend their methodological approach against critical reviewer comments.
The content of this course is mainly based on research articles. Lecture slides and selected articles are provided; for examples, see "optional readings". Optional Readings (Examples):- Bagozzi, R. P., & Yi, Y. (1989). On the use of structural equation models in experimental designs. Journal of Marketing Research, 26, 271-284.- Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.- Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461-483.- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park: Sage.- Cheung, M. W. L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227-246.- Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25, 186-192.- Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26, 329-367.- Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12, 205-218.- Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314-336. - Jöreskog, K. G. (1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109-133.- Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.- Muthén, L. K., & Muthén, B. O. (2012). Mplus user's guide. Los Angeles: Muthén & Muthén.
60% lectures, 40% exercises (it is necessary to bring your laptop to the lecture!)
Participation and Course Paper
- Basic courses in statistics- It is necessary that you have already collected data for your dissertation thesis. The course is designed for doctoral students at any stage of the dissertation process.
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