Back to My HomePage

Back to Research Design and Analysis HomePage

Tentative Schedule of Events: Printable Course Syllabus (last updated 08/17/09)

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 the uses of multilevel models (aka general linear mixed models, hierarchical linear models) for longitudinal data analysis. The course is organized to take participants through each of the cumulative steps in a longitudinal (multilevel) analysis: deciding which type of model is appropriate, setting up the data file and coding variables, evaluating fixed and random effects and/or alternative covariance structures, predicting between- and within-person variation using covariates, interpreting and displaying empirical findings, and presenting results in both verbal and written form. 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 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 227/234 Burnett computer labs (see syllabus for dates), in which participants will have opportunities for hands-on practice and to work on course assignments. SAS will be the only program utilized, and lab time will also be used to orient participants to data manipulation, analysis, and graphing in SAS.

Participants should be familiar with the general linear model (analysis of variance, regression) prior to enrolling in this course, but no previous familiarity with mixed models (other than repeated measures ANOVA) is assumed. Participants will need to have access to SAS software, available in rooms 234, 227, and 230 Burnett. Student licenses can be purchased from the stats department (around $25; yearly renewal required). 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 a longitudinal data set 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 Psyc945: Advanced Multilevel Modeling (to be taught spring 2010).

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: 944_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 business day in order to encourage participants to keep up with the course. Points lost to lateness will not be returned.

Final grades for Psychology 944 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:

S & B: Snijders, T. A. B., & Bosker, R. (1999). Multilevel analysis. Thousand Oaks, CA: Sage.

S & W: Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.

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

Book Chapters:

Hoffman, L. (in preparation). Longitudinal analysis: Modeling within-person variation and change. NY, NY: Routledge Academic.

M & D: Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data. Mahwah, NJ: Erlbaum.

Wallace, D., & Green, S.B. (2002). Analysis of repeated measures designs with linear mixed models. In D.S. Moskowitz & S.L. Hershberger (Eds.), Modeling intraindividual variability with repeated measures data (pp. 103-134). Mahwah, NJ: Erlbaum.

Journal Articles:

Cudeck, R., & Harring, J. R. (2007). Analysis of nonlinear patterns of change with random coefficient models. Annual Review of Psychology, 58, 615-637.

Hernandez-Lloreda, M. V., Colmenares, F., & Martinez-Arias. (2004). Application of piecewise hierarchical linear growth modeling to the study of continuity in behavioral development of Baboons (Papio hamadryas). Journal of Comparative Psychology, 118(3), 316-324.

Hoffman, L. (2007). Multilevel models for examining individual differences in within-person variation and covariation over time. Multivariate Behavioral Research, 42 (4), 609-629.

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of state of the art. Psychological Methods, 7(2), 147-177.

Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24(4), 323-355.

Sliwinski, M. J., & Buschke, H. (2004). Modeling intraindividual cognitive change in aging adults: Results from the Einstein Aging Studies. Aging, Neuropsychology, and Cognition, 11(2-3), 196-211.

Willett, J.B. (1989). Some results on reliability for the longitudinal measurement of change: Implications for the design of studies of individual growth. Educational and Psychological Measurement, 49, 587-602.