University of Nebraska-Lincoln |
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Instructor: |
Dr. Lesa Hoffman |
Email: |
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Rooms: |
77 (234, 227) Burnett Hall |
Phone: |
(402) 472-6930 |
Time: |
M W 10:30-11:45 |
Office: |
220 Burnett Hall (mailbox in 237) |
| Credits: | 3 | Office Hours: |
M W 1:30-2:30 and by appointment |
Extra Lab Hours: |
M F 8:30-10:00 and by appointment |
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.
This course will illustrate the uses of the General Linear Mixed Model (aka Multilevel Model, Hierarchical Linear Model) for longitudinal data analysis. The course is organized to take participants through each of the 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 will be available for download at the website above the day prior to class, or else copies will be provided in class. Because the course will have an applied focus, course sessions will also be held in the 233-234 Burnett computer lab (see syllabus for dates), in which participants will have opportunities for hands-on practice and to work on course assignments. Although SPSS syntax will be provided where applicable, SAS will be the main 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 (ANOVA, Regression) prior to enrolling in this course. Previous familiarity with the General Linear Mixed Model is helpful, but not required. Participants need to have access to SAS software, but no prior knowledge of SAS is necessary. Finally, participants are strongly encouraged to use a longitudinal data set within their substantive area for course assignments , but practice data sets can also be made available. A longitudinal data set with 3 or more time points is needed for all assignments, and must contain two continuous outcome variables and two predictor variables, at least one of which must be time-varying.
Course performance will be evaluated as follows. Details about each requirement will be presented throughout the semester prior to the due dates.
Final Project:
Participants will write a core of an empirical article describing their findings, due by 5:00 PM on Monday, 12/11/06 (30 points). Participants are welcome to submit prior drafts, but final papers cannot be revised. Participants will also present these results in a brief in-class talk at the end of the semester (10 points).
Intermediate Assignments:
Three assignments will be administered in order to give participants the opportunity to apply techniques discussed in class to their own data (and to serve as the foundation for the final project). Each assignment will be worth 12 or 24 points and will be due as listed on the syllabus unless otherwise stated. Because the course material will build on earlier concepts, late assignments will be docked .5 points per business day in order to encourage participants to keep up with the course. If you have a scheduling conflict that will prevent you from turning in an assignment on time, please contact me at least two weeks in advance to make arrangements. Assignments will be accepted via email as needed, but hard copy is preferred. Each assignment can be revised once and re-graded in order to earn the maximum possible points, but points lost to lateness cannot be returned. Please turn in the original assignment with the revised assignment.
Assignment #1 (due 9/18/06) Feedback on Assignment 1
Assignment #2 (due 11/01/06) Feedback on Assignment 2
Assignment #3 (due 11/20/06) Feedback on Assignment 3
Final grades for Psychology 930 will be determined according to the proportion earned of 100 possible points:
≥97=A+ 93-96=A 90–92=A- 87-89=B+ 83-86=B 80-82=B- 77-79=C+ 73-76=C 70-72=C- …
A s 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 analyses should be unique.
Any student in this course who has a disability that may prevent him or her from fully demonstrating his or her abilities should contact me personally as soon as possible, so that we can discuss accommodations necessary to ensure full participation and facilitate your educational opportunity.
Primary: S & B : Snijders, T. A. B., & Bosker, R. (1999). Multilevel analysis. Thousand Oaks, CA: Sage.
Other: R & B : Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Other: M & D : Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data . Mahwah, NJ: Erlbaum.
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.
Kreft, I. G. G., de Leeuw, J., & Aiken, L. S. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30(1), 1-21.
Rovine, M. J., & Molenaar, P. C. M. (1998). The covariance between level and shape in the latent growth curve model with estimated basis vector coefficients. Methods of Psychological Research Online, 3(2), 95-107.
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., Hofer, S. M., Hall, C. B., Buschke, H., & Lipton, R. B. (2003a). Modeling memory decline in older adults: The importance of preclinical dementia, attrition, and chronological age. Psychology and Aging, 18(4), 658-671.
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.
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.
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.
SPSS:
SAS:
Burlew, M.M. (1998). SAS macro programming made easy . Cary, NC: SAS Institute.