Fall 2018 CLDP 948 / EPSY 906: Latent Trait Measurement and Structural Equation Models

Tentative Schedule of Events (Printable Syllabus; last updated 9/10/2018)

Course Objectives, Materials, and Pre-Requisites:

This course will contemporary approaches to measurement, expanding from classical test theory into confirmatory factor models, item response models, and their use within structural equation models. In addition to the statistical models, the course will also focus on the measurement concepts behind these models and how they relate to each other with respect to scale construction and evaluation. Class time will be devoted primarily to lectures and examples. Lecture materials in .pdf format will be available for download at the website above the day prior to class, or else paper copies can be requested. Video recordings of the class lectures will also be available online, but are not intended to take the place of class attendance. Selected book chapters and journal articles will be assigned for each specific topic as needed; the initial list of readings below may be updated. Because the course will have an applied focus using Mplus software, instructor and GTA office hours will be held in the 3049 Dole computer lab, in which participants will have opportunities to work on assignments and receive immediate software assistance. Participants should be comfortable with the general linear model (analysis of variance, regression) prior to enrolling in this course.

Academic Honesty:

As a reminder, the University of Kansas has a formal policy on academic honesty. All assignments should be done individually without exception.

Accommodating Students with Disabilities:

Students with disabilities or who have other special needs are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.

Course Requirements:

Participants will have the opportunity to earn up to 100 total points in this course. Up to 82 points can be earned from submitting 6 homework assignments. Up to 18 points may be earned from submitting 6 outside-of-class quizzes. Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the online syllabus for more information). There may be other opportunities to earn extra credit at the instructor’s discretion.

Policy on Late Homework Assignments and Incompletes:

In order to be able to provide prompt feedback and class discussion of homework problems, late homework assignments will incur a 3-point penalty. However, extensions will be granted as needed for extenuating circumstances (e.g., conferences, family obligations) if requested at least two weeks in advance of the due date. Late or incomplete outside-of-class quizzes will incur a 1-point penalty when submitted. Finally, a final grade of “incomplete” will only be given in the event of dire circumstances and at the instructor's discretion. Homework assignments that involve individual writing will have the opportunity to be revised ONCE to earn the maximum total points. Written assignments must be at least ¾ complete to be accepted, and late revisions will incur a 1-point penalty. No late points will be returned through the revision process. Please use “track changes” and retain all original instructor comments (unless otherwise instructed) so that I can easily see how your revisions address the comments.

Final grades will be determined according to the proportion earned out of the total possible points:

>92 = A, 90–92 = A-, 87-89 = B+, 83-86 = B, 80-82 = B-, <80 = C or no pass

Course Software:

Participants will need to have access to Mplus software, which is available in 3049 Dole and in the GIS and Data Lab in 425 Watson Library. Individual student licenses can also be purchased from the statmodel.com ($350 each; no expiration date).

Course Textbook:

Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York, NY: Guilford.

Other Course Readings (all available via "Course Documents" on KU Blackboard):

Asparouhov, T., & Muthén, B. (2010). Plausible values for latent variables using Mplus. Retrieved from http://www.statmodel.com/download/Plausible.pdf.

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 befactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189-225.

Curran, P. J., Cole, V. T., Bauer, D. J., Rothenberg, A., & Hussong, A. M. (2018). Recovering predictor–criterion relations using covariate-informed factor score estimates. Structural Equation Modeling. Retrieved August 2018, https://doi.org/10.1080/10705511.2018.1473773.

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.

Davidson, C. A., Hoffman, L., & Spaulding, W. D. (2016). Schizotypal personality questionnaire – brief revised (updated): An update of norms, factor structure, and item content in a large non-clinical young adult sample. Psychiatry Research, 238, 345-355.

Edwards, M. C., & Wirth, R. J. (2009). Measurement and the study of change. Research in Human Development, 62(2-3), 74-96.

E & R: 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.

Ferrando, P. J. (2009). Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Applied Psychological Measurement, 33(1), 9-24.

Huggins-Manley, A. C., Algina, J. & Zhou, S. (2018). Models for semiordered data to address not applicable responses in scale measurement. Structural Equation Modeling, 25(2), 230-243.

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. 3473-503, 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.

McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000144

McNeish, D., An J., & Hancock, G. R. (2018). The thorny relation between measurement quality and fit index cutoffs in latent variable models. Journal of Personality Assessment.

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.

Paek, I., Cui, M., Gübes, N. O., & Yang, Y. (2018). Estimation of an IRT model by Mplus for dichotomously scored responses under different estimation methods. Educational and Psychological Measurement, 78(4), 569-588.

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.

Sheu, C.-F., Chen, C.-T., Su, Y.-H., & Wang, W.-C. (2005). Using SAS PROC NLMIXED to fit item response theory models. Behavior Research Methods, 37(2), 202-218.

Shi, D., Lee, T., & Maydeu-Olivares, A. (2018). Understanding the model size effect on SEM fit indices. Educational and Psychological Measurement.

Tay, L., & Jebb, A. T. (2018). Establishing construct continua in construct validation: The process of continuum specification. Advances in Methods and Practices in Psychological Science, retrieved July 2018. https://doi.org/10.1177/2515245918775707.

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.