Latent Trait Measurement and Structural Equation Modeling (Fall 2017; EPSY 906; KU)

Course Information

Brief Course Description

EPSY 906, Latent Trait Measurement and Structural Equation Models, provides instruction on contemporary measurement theory and latent variable models for scale construction and evaluation, including confirmatory factor analysis, item response modeling, diagnostic classification models, and structural equation modeling. The course is designed to provide details of such models, from statistical underpinnings to how to run many various types of analyses, combining theoretical and practical perspectives.

As this course is cross-listed with CLDP 948 (taught by Lesa Hoffman of the Child Language Doctoral Program), in an attempt to provide more consistent content, the materials used in this course were created in large part by Lesa Hoffman. For versions of the course taught by Dr. Hoffman, please visit http://www.lesahoffman.com.

The course will use the R statistical program with R Studio and the Lavaan package (Rosseel, 2012) for all computational and data analysis work involved in the course. Additionally, examples will be provided in the Mplus package.

For all other specific information regarding general course policies, course evaluation rubrics, and grading systems, please see the course syllabus at the link below.

All readings will be made available via campus OneDrive shared folder. Please email me for an invitation to this folder.

Course Materials

Introduction to Latent Trait Measurement and Structural Equation Modeling

Exploratory Factor Analysis and Principal Components Analysis

Classical Test Theory for Scale Reliability and Validity

Confirmatory Factor Analysis

Additional Resources:

Latent Trait Measurement Models for Binary Responses

Additional Resources:

Latent Trait Measurement Models for Other Item Responses

Additional Resources:

Measurement Invariance in CFA and Differential Item Functioning in IRT/IFA

Higher-Order and Method Factor Models

Path Models, Mediation, and Structural Equation Modeling

Homework Schedule

References

Bauer, D. J., & Hussong, A. M. (2009). Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models. Psychological Methods, 14(2), 101-125.

Chen, F., F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189-225.

Curran, P. J., McGinley, J. S., Bauer, D. J., Hussong, A. M., Burns, A., Chassin, L., Sher, K., & Zucker, R. (2014). A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivariate Behavioral Research, 49(3), 214-231.

Embretson, S. E., & Reise, S. T. (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum.

Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.

John, O. P., & Benet-Martinez, V. (2014). Measurement: Reliability, construct validation, and scale construction. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 473-503, 2^(nd) ed.). New York, NY: Cambridge University Press.

MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Routledge Academic.

Maydeu-Olivares, A. (2015). Evaluating the fit of IRT models. In S. P. Reise & D. A. Revicki (Eds.), Handbook of item response theory modeling (pp. 111-127). New York, NY: Taylor & Francis.

Maydeu-Olivares, A., & Coffman, D. L. (2006). Random intercept item factor analysis. Psychological Methods, 11, 344-362.

McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.

Mungas, D., & Reed, B. R. (2000). Application of item response theory for development of a global functioning measure of dementia with linear measurement properties. Statistics in Medicine, 19, 1631-1644.

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13-43.

Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667-696.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4-69.

Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: Current approaches and future directions. Psychological Methods, 12(1), 58-79.