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Tentative Schedule of Events: Printable Course Syllabus (last updated 08/18/09)

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".
Audio links are .mp3 files taped from the class lecture (right-click, use "save target/link as").

Course Objectives and Pre-Requisites:

This course will illustrate contemporary approaches to measurement, expanding from classical test theory into confirmatory factor models and item response models. In addition to the statistical models, however, the course will also focus on the measurement concepts behind these models and how they compare and contrast with 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 each class, or else paper copies will be provided in class. Audio recordings of the class lectures in .mp3 format will also be posted online, but are not intended to take the place of class attendance. Because the course will be applied, course sessions will also be held in the 227 and 234 Burnett computer labs (see syllabus for dates), in which participants will have opportunities for hands-on practice and to work on course assignments. The primary program utilized will be Mplus, although additional programs may be brought in as needed. Lab time will be used to provide instruction with each program used; no prior experience with any program (other than SPSS or SAS) is assumed.

Participants should be familiar with the general linear model (analysis of variance, regression) prior to enrolling in this course, but no previous familiarity with measurement or structural equation modeling (other than exploratory factor analysis) is assumed. Participants will need access to Mplus software. Mplus is available in rooms 234, 227, and 230 Burnett. Student licenses are expensive ($200 for the base program), but may be worth the cost if these models are something you're likely to need frequently (but they do not expire yearly as do other programs). Course assignments will include both essay questions and application of techniques discussed in class, and will utilize data sets provided by the instructor. Participants are strongly encouraged to use an item-level dataset within their substantive area for the final project, but example data sets can also be made available. Requirements for the final project dataset will be discussed in more detail throughout the semester. Finally, this course is intended to serve as a precursor to a course in Structural Equation Modeling (to be taught spring 2010 in EDPS).

Course Requirements:

Course performance will be evaluated as follows. Details about each requirement will be presented throughout the semester prior to the due dates.

Course Assignments:

Three assignments (65 points) will be administered in order to give participants the practice applying techniques discussed in class and will be due as listed on the online syllabus. Each assignment must be at least 3/4 complete in order to be accepted and may be revised ONCE to earn the maximum possible points. Assignments should be submitted electronically via email as a Microsoft Word document using this naming convention: 948_FirstLast_HW# (adding an “r” for a revision). Please use the ‘track changes' function in Microsoft word when revising assignments.

Final Project and Presentation:

Participants will complete an independent project utilizing ideas and techniques discussed through the semester (25 points) and will be due as noted on the online syllabus. The results of the project will also be presented in a 12-minute talk (10 points), to be given at the end of the semester. A project outline (5 points) will be due prior to the end of the semester as noted on the syllabus.

Policy on Assigning Incompletes:

A grade of “incomplete” will be assignment ONLY in the case of extenuating circumstances that prevent participants from completing course requirements in a timely manner. If an incomplete is assigned, then all course requirements must be completed within ONE MONTH of the end of the course or else the incomplete will turn into whatever grade has been earned at that point.

Policy on Late Assignments:

If other obligations or circumstances will prevent you from completing any course requirements, please contact me so that we can create a solution . Don't wait until you are behind! If you contact me at least two weeks prior to a due date we may be able to extend the deadline to accommodate any extenuating circumstances. Otherwise, late assignments will be docked .5 points per day in order to encourage participants to keep up with the course. Points lost to lateness will not be returned.

Final grades for Psychology 948 will be determined according to the proportion earned of 105 possible points:

=97 = A+ 93-96 = A 90–92 = A- 87-89 = B+ 83-86 = B 80-82 = B- < 80 = C

Academic Honesty:

As a reminder, the University has a policy on academic honesty (see the Graduate Studies Bulletin). Although data sets may be shared, all course assignments should be done individually and all final projects should be unique.

Accommodating Disabilities:

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 the University of Nebraska-Lincoln 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 Texts:

Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.    

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

All supplementary readings will be available online through course documents on UNL Blackboard.

Book Chapters:

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.

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

Rupp, A., Templin, J., & Henson, R. (2010). Diagnostic Measurement: Theory, Methods, and Applications. Forthcoming from Guilford Press.

Journal Articles:

Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21 (4), 726-735.

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.

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.

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.

Kamata, A., & Bauer, D. J. (2008). A note on the relation between factor analytic and item response theory models. Structural Equation Modeling, 15, 136-153.

McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30-46.

Meade, A. W., & Bauer, D. J. (2007). Power and precision in confirmatory factor analytic tests of measurement invariance. Structural Equation Modeling, 14 (4), 611-635.

Millsap, R. E., & Yun-Tein, J. (2004). Assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research, 39(3), 479-515.

Mungas, D., & Reed, B. R. (2000). Appliction of item response theory for development of a global functioning measure of dementia with linear measurement propoerties. 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.

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.

Smith, E. V., Jr. (2001). Evidence for the reliability of measures and validity of measure interpretation: A Rasch measurement perspective. Journal of Applied Measurement, 2(3), 281-311.

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.

Mplus Website (filled with examples, documentation, and other resources)

Mplus Online Manual