Links under topics below are .pdf files for the lecture materials.
Versions of the .pdf files including the answers will be available after each class under answers.
MP4 files were recorded from the class lecture (right-click, use save target/link as). [no longer available as of 2017]
Week |
Date |
Course Materials |
Readings |
| 1 | 1/13 | Course Introduction Lecture 1: Introduction to Latent Trait Measurement Models, Items, and Scales Lecture 2: Exploratory Factor Analysis and Principal Components Analysis |
John & Benet-Martinez (2000) Preacher & McCollum (2003) Brown ch. 2 |
| 1/17 | HOMEWORK #0 DUE BY 11:59 PM | ||
| 2 | 1/20 | NO CLASS (or office hours this week) | |
| 1/24 | HOMEWORK #1 DUE BY 11:59 PM: Download HW1 | ||
| 3 | 1/27 | Lecture 3: Classical Test Theory for Assessing Scale Reliability and Validity Example 3: Classical Items Analysis in SPSS and SAS Lecture 4: Confirmatory Factor Analysis Example 4: Confirmatory Factor Models in Mplus (CFA spreadsheet) (Mplus output) |
McDonald ch. 5-7 McGraw & Wong (1996) Brown ch. 3 |
| 1/31 | By now you will need to have successfully registered for Tusker and submitted an Mplus script for analysis. Do so by getting your faculty advisor to sign up for a free HCC account, and then create a sub-account of your own. Follow the instructions given above to do so. Submit proof of your successful run (the output file with your Tusker directory path as a title) as an assignment in Blackboard (worth 3 HW points). | ||
| 4 | 2/3 | Lecture 4 and Example 4, continued |
Brown ch. 4-5 Enders (2010) ch. 3, 5 |
| 5 | 2/10 | Lecture 4 and Example 4, continued |
|
| 2/14 | HOMEWORK #2 ****NOW DUE BY 11:59 PM on 2/14***** | ||
| 6 | 2/17 | Example 4 addition: Confirmatory Single-Factor Models via SAS PROC MIXED Lecture 5: Introduction to Generalized Models (LL spreadsheet) |
DeMaris (2003) |
| 7 | 2/24 | Lecture 6: Latent Trait Measurement Models for Binary Responses Example 6: Binary Item Response Models in Mplus (Example 6 and 7a spreadsheet) (Mplus output) |
E & R (2000) ch. 3-4, 7-8 Mungas & Reed (2000) Wirth & Edwards (2007) |
| 2/28 | HOMEWORK #3 *****NOW DUE BY 11:59 PM on 2/28***** Download HW3 | ||
| 8 | 3/3 | Lecture 6 and example 6, continued |
|
| 9 | 3/10 | Lecture 7: Latent Trait Measurement Models for Other Item Responses Example 7a: Graded Response Models for Ordinal Responses in Mplus (Mplus output) Example 7b: Non-Normal Outcome Measurement Models in Mplus (spreadsheet) (Mplus output) |
E & R ch. 5 Bauer & Hussong (2009) |
| 10 | 3/17 | NO CLASS (or office hours this week) | |
| 3/21 | HOMEWORK #4 *****NOW DUE BY 11:59 PM ON FRIDAY 3/21***** | ||
| 11 | 3/24 | NO CLASS (or office hours this week) | |
| 12 | 3/31 | Example 7c: Item Response Models in SAS NLMIXED (GRM spreadsheet) (GPCM spreadsheet) Lecture 8: Explanatory Item Response Models Example 8: Explanatory Item Response Models in SAS GLIMMIX Lecture 9: Measurement Invariance in CFA and Differential Item Functioning in IRT/IFA |
Sheu et al. (2005) Rijmen et al. (2003) Embretson (1983) Brown ch. 7 Vandenberg & Lance (2000) |
| 4/4 | HOMEWORK #5 DUE BY 11:59 PM: Download HW5 | ||
| 13 | 4/7 | Lecture 9, continued Example 9a: Multiple-Group Measurement Invariance in CFA using Mplus (spreadsheet) (Mplus output) Example 9b: Longitudinal Measurement Invariance in CFA using Mplus (spreadsheet) (Mplus output) |
Edwards & Wirth (2009) |
| 14 | 4/14 | Lecture 9, continued Example 9c: Multiple-Group Measurement Invariance in IFA using Mplus (spreadsheet) (Mplus output) |
|
| 4/18 | HOMEWORK #6 DUE BY 11:59 PM | ||
| 15 | 4/21 | Lecture 10: Higher-Order and Method Factor Models Example 10: Higher-Order CFA and IFA Models in Mplus (spreadsheet) (Mplus output) |
Brown ch. 6, 8 Maydeu-Olivares & Coffman (2006) Chen et al. (2006) Reise (2012) |
| 16 | 4/28 | Lecture 11: Structural Equation Modeling Example 11: Structural Equation Modeling in Mplus (Mplus output) Course Evaluations |
Kline ch. 5, 6, 8 Boomsma (2009) |
| 5/2 | HOMEWORK #7 DUE BY 11:59 PM: Download HW7 | ||
| 17 | 5/11 | ALL HOMEWORK REVISIONS DUE BY ***SUNDAY 5/11*** AT 11:59 PM | |
This course will feature 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 electronically at the course website above prior to each class. Audio/Video recordings of the class lectures in .mp4 format will also be posted online, but are not intended to take the place of class attendance. In addition to the primary textbook (see below), supplemental book chapters and journal articles will be assigned for each topic; the list of readings below will be updated as needed. All supplemental readings will be available electronically within the online homework portal.
Because the course will have an applied focus using Mplus software, portions of the class and instructor office hours will be held in the 234 Burnett computer lab to provide opportunities for in-class practice and to work on homework assignments and receive immediate assistance. Participants will need access to Mplus software, which is available in rooms 234, 227, and 230 Burnett and online through the Tusker computing cluster (see the course webpage for access instructions). Finally, participants should be familiar with the general linear models and multivariate modeling using maximum likelihood (as covered in PSYC 941, 942, and 943 or the equivalent) prior to enrolling in this course.
As a reminder, the University has a policy on academic honesty (see the Graduate Studies Bulletin). All course assignments should be done individually.
Students with disabilities are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation. It is the policy of UNL to provide flexible and individualized accommodation to students with documented disabilities that may affect their ability to fully participate in course activities or to meet course requirements. To receive accommodation services, students must be registered with the Services for Students with Disabilities (SSD) office, 132 Canfield Administration, 472-3787 voice or TTY.
Course performance will be evaluated using 7 homework assignments designed to give participants hands-on practice applying the techniques discussed in class and will be due as listed on the online syllabus. Assignments will include data analysis, results interpretations, and other questions. Successfully running Mplus software through the HCC will also be required (3 of the homework points). In addition, “Homework 0” will be worth 3 bonus points to familiarize participants with the online homework system.
Homework assignments will fall into two categories: common data and individual data. Assignments using common datasets will be administered and submitted using the online homework portal above. Assignments using individual datasets will be submitted through UNL Blackboard and returned via the online homework portal. Assignments using individual datasets must be at least 3/4 complete in order to be accepted and may be revised ONCE to earn the maximum points (due by the end of semester via Blackboard). Assignments should be submitted as a Microsoft Word document using this naming convention: 948HW#_Firstname_Lastname (adding an “r” to the end of the # for a revision). Please use the track changes function in Word and leave in all previous instructor comments when revising assignments.
In order to be able to provide the entire class with prompt feedback, 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 three weeks in advance of the due date. Finally, a grade of “incomplete” will only be given in the event of extremely dire circumstances.
=97 = A+ 93-96 = A 90–92 = A- 87-89 = B+ 83-86 = B 80-82 = B- < 80 = C or no pass
Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
Kline (2005). Principles and practice of structural equation modeling (2nd Ed.). New York: Guilford.
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.
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.
Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling, 7, 461-483.
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.
DeMaris, A. (2003). Logistic regression. In J. A. Schinka & W. F. Velicer (Eds.), Research methods in psychology (Vol. 2, pp. 509-532). New York, NY: Wiley.
Edwards, M. C., & Wirth, R. J. (2009). Measurement and the study of change. Research in Human Development, 62(2-3), 74-96.
Embretson, S. E. (1983). Construct validity: Construct representation versus nomothetic span. Psychological Bulletin, 93(1), 179-197.
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.
John, O. P., & Benet-Martinez, V. (2000). 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. 339-369). New York, NY: Cambridge University Press.
Maydeu-Olivares, A., & Coffman, D. L. (2006). Random intercept item factor analysis. Psychological Methods, 11, 344-362.
McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30
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.
Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185-205.
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.
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.