|Instructor:||Dr. Jonathan Templin|
|Office:||614 Joseph R. Pearson Hall|
|Office Hours:||Vary by week. See schedule here: https://calendar.google.com/calendar/embed?src=jtemplin%40ku.edu&ctz=America%2FChicago|
|Graduate Teaching Assistant||Jihong Zhang|
|GTA Office Hours:||Fridays 9am-11am|
|Course Meeting Time:||1:30pm-4:20pm Tuesdays|
|Course Meeting Location:||150 Joseph R. Pearson Hall|
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.
Helpful R Links and Resources
|Books:||R for Data Science (Grolemund and Wickham, 2015)|
|R for SAS and SPSS Users by Muenchen (2007)|
|An introduction to R by Venables et al. (2013)|
|aRrgh: a newcomer's (angry) guide to R|
|DataCamp Introduction to R|
|Cookbook for R|
|Quick References:||Reference Card #1 (various authors, noted on card)|
|Reference Card #2 (various authors, noted on card)|
Course Topics and Materials
Lecture from Spring 2016 Multivariate:
- Lecture 4 Slides: EPSY906_Lecture04_CFA
- Lecture 4 Examples:
- Lecture 4 Videos:
- Lecture 5 Slides: EPSY906_Lecture05_Binary_Responses
- Lecture 5 Examples:
- Lecture 5 Videos:
- Lecture 6 Slides: EPSY906_Lecture06_Other_Responses
- Lecture 6 Example A:
- Lecture 6 Example B:
- Lecture 6 Videos:
- 2 Nov 2017: https://youtu.be/txIEpF-rXJE
- Lecture 7 Slides: EPSY906_Lecture07_Invariance
- Lecture 7 Examples:
- Example 7A (CFA Multiple Group Invariance)
- Example 7B (CFA Longitudinal Invariance)
- Example 7C (IRT/IFA Limited Information Multiple Group Invariance)
- Example 7D (IRT/IFA Maximum Likelihood Multiple Group Invariance)
- Lecture 7 Videos:
- Lecture 9 Slides: EPSY906_Lecture09_SEM
- Lecture 9 Examples:
- Example 9A:
- Example 9C:
- Lecture 9 Videos:
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, 2nd 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.