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 | 1/9 | Lecture 1: Introduction to Multilevel Models for Longitudinal and Repeated Measures Data | Hoffman ch. 1; Willett (1989) |
| 1/10 | HOMEWORK #0 (online portion) DUE MONDAY 1/14 BY 11:59 PM Please note -- at any point throughout the semester you can also receive 0.5 bonus points for setting up an HCC account (under group "lhoffman") and 0.5 bonus points by emailing me the resulting SAS log file that demonstrates that you successfully ran SAS on the computing cluster from your account. Written instructions for how to use the HCC to run SAS remotely |
||
| 1/11 | Lecture 2: Interpreting General Linear Models |
Hoffman ch. 2 | |
| 2 | 1/16 | Example 2: Practice with Interactions
(SAS data and syntax) |
|
| 1/17 | HOMEWORK #1 DUE THURSDAY 1/24 BY 11:59 PM | ||
| 1/18 | Lecture 3: Introduction to Within-Person Analysis and RM ANOVA Example 3a: Between vs. Within-Person Models (SAS data and syntax) Example 3b: Repeated Measures Analysis of Variance (SAS data and syntax) |
Hoffman ch. 3 | |
| 3 | 1/23 | Lecture 4: Describing Within-Person Fluctuation over Time via ACS Models Example 4: Within-Person Fluctuation in Symptom Severity over Time (spreadsheet) |
Hoffman ch. 4 sec. 1-2 |
| 1/24 | HOMEWORK #2 DUE THURSDAY 1/31 BY 11:59 PM | ||
| 1/25 | Lecture 4 and Example 4, continued Chapter 4 SAS data and syntax |
Hoffman ch. 4 sec. 3-6 | |
| 4 | 1/30 | Lecture 5: Introduction to Random Effects of Time and Model Estimation Example 5: Practice with Fixed and Random Effects of Time (SAS data and syntax) |
Hoffman ch. 5 sec. 1-2 |
| 1/31 | HOMEWORK #3 DUE THURSDAY 2/7 BY 11:59 PM | ||
| 2/1 | Lecture 5 and Example 5, continued (Part 2) |
Hoffman ch. 5 sec. 3-6 | |
| 5 | 2/6 | Lecture 5 and Example 5, continued (Part 3) |
|
| 2/7 | PROJECT PART 1 DUE FRIDAY 2/15 BY 11:59 PM | ||
| 2/8 | Lecture 6: Describing Within-Person Change Chapter 6 SAS data and syntax (spreadsheet) Example 6a: Polynomial Models of Change SAS Program to Simulate Polynomial Growth Data |
Hoffman ch. 6 sec. 1-2A | |
| 6 | 2/13 | Lecture 6, continued Example 6b: Piecewise Models of Change |
Hoffman ch. 6 sec. 2B |
| 2/14 | HOMEWORK #4 DUE THURSDAY 2/21 BY 11:59 PM | ||
| 2/15 | Lecture 6 and Example 6b, continued Example 6c: Exponential Models of Change via NLMIXED |
Hoffman ch. 6 sec. 2C-6 | |
| 7 | 2/20 | Chapter 7 SAS data and syntax Example 7a and 7b SAS data and syntax Lecture 7: Time-Invariant Predictors in Longitudinal Models Example 7a: Time-Invariant Predictors of Practice Effects (spreadsheet for plots) |
Hoffman ch. 7 sec. 1-2 |
| 2/21 | HOMEWORK #5 DUE MONDAY 3/4 BY 11:59 PM | ||
| 2/22 | NO CLASS OR OFFICE HOURS | ||
| 8 | 2/27 | Lecture 7 and Example 7a, continued |
Hoffman ch. 7 sec. 3-6 |
| 3/1 | HOMEWORK #6 DUE MONDAY 3/11 BY 11:59 PM | ||
| 3/1 | Lecture 7 and Example 7a, continued Example 7b: Decomposing Interactions of Time-Invariant Predictors in Models of Change (spreadsheet for plots) (answers) |
Hedeker & Mermelstein (2012) | |
| 9 | 3/6 | Chapter 15 SAS data and syntax Lecture 8: Analysis of Repeated Measures Designs not Involving Time Example 8a: Crossed Persons and Words (SAS data and syntax) |
Hoffman ch. 12 sec. 1-2 |
| 3/7 | HOMEWORK #7 DUE MONDAY 3/25 BY 11:59 PM | ||
| 3/8 | Lecture 8, continued Example 8b: Analysis of Eye Movements |
Hoffman ch. 12 sec. 3-6 | |
| 10 | 3/13 | Lecture 9: Time-Varying Predictors in Models of Within-Person Fluctuation Example 9a: Predicting Weekly Severity from Lagged Effects of Weekly Stress (spreadsheet for plot) Example 9b: Predicting Daily Glucose from Daily Negative Mood (spreadsheet for plot) (answers) (SAS syntax and data, including a macro syntax file) |
Hoffman ch. 8 sec. 1-2B |
| 3/14 | HOMEWORK #8 DUE FRIDAY 4/5 BY 11:59 PM | ||
| 3/15 | Lecture 9 and Examples 9a and 9b, continued | Hoffman ch. 8 sec. 2C-6 | |
| 11 | 3/20 | NO CLASS OR OFFICE HOURS | |
| 3/21 | NO NEW HOMEWORK | ||
| 3/22 | NO CLASS OR OFFICE HOURS | ||
| 12 | 3/27 | Lecture 10: Multilevel Models for Clustered Data Example 10: Modeling Children Nested within Schools (spreadsheet for plot) (answers) NO OFFICE HOURS |
Raudenbush & Bryk (2002) ch. 5 |
| 3/28 | PROJECT PART 2 DUE FRIDAY 4/5 BY 11:59 PM Help Generating Tables and Plots in SAS for Part 2 Method Section |
||
| 3/29 | Lecture 10 and Example 10, continued NO OFFICE HOURS |
Enders & Tofighi (2007); Hofmann & Gavin (1998) |
|
| 13 | 4/3 | Review Part 1 |
|
| 4/4 | PROJECT PARTS 2 (revised) and 3 (new) DUE THURSDAY 4/18 BY 11:59 PM | ||
| 4/5 | Review Part 2 |
||
| 14 | 4/10 | NO CLASS; VIRTUAL OFFICE HOURS (see email) |
|
| 4/11 | FEEDBACK AVAILABLE FOR PROJECT PART 2; NO NEW HOMEWORK | ||
| 4/12 | Lecture 10, Lecture 9, and Example 9b, continued | ||
| 15 | 4/17 | Lecture 9 and Example 9b, continued | |
| 4/18 | NO NEW HOMEWORK | ||
| 4/19 | NO CLASS | ||
| 16 | 4/24 | Course Evaluations Lecture 11: Evaluating Alternative Metrics of Time Example 11: Alternative Metrics of Time (spreadsheet) (answers) |
Hoffman ch. 10 sec. 1-2 Hoffman (2012); Sliwinski, Hoffman, & Hofer (2010) |
| 4/25 | FEEDBACK AVAILABLE FOR PROJECT PART 3; NO NEW HOMEWORK | ||
| 4/26 | NO CLASS; OFFICE HOURS BY INDIVIDUAL APPOINTMENT | ||
| 17 | 5/3 | COMPLETED PROJECT DUE FRIDAY 5/3 BY 11:59 PM |
This course will illustrate the uses of multilevel models (i.e., general linear mixed models, hierarchical linear models) for analysis of longitudinal and repeated measures data. The course is organized to take participants through each of the cumulative steps in a multilevel analysis: deciding which type of model is appropriate, organizing the data file and coding predictor variables, evaluating fixed and random effects and/or alternative covariance structures, predicting multiple sources of variation, and interpreting and presenting empirical findings. 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/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. Selected book chapters and journal articles will be assigned for each specific topic as needed; the initial list of readings below may be updated throughout the semester via email. Because the course will have an applied focus using SAS software, instructor office hours will be held in the 234 Burnett computer lab, in which participants will have opportunities to work on course assignments and receive immediate software assistance.
Participants should be familiar with the general linear model (analysis of variance, regression) and repeated measures ANOVA prior to enrolling in this course (i.e., through PSYC 941, 942, and 943). Participants will need to have access to SAS software, available in rooms 234, 227, and 230 Burnett. Student licenses can be purchased from the Psychology department ($25; yearly renewal required).
As a reminder, the University has a policy on academic honesty (see the Graduate Studies Bulletin). All course assignments should be done individually and all individual projects should be unique.
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 as follows.
Homework Assignments (74 possible points):
Throughout the semester, 8 online homework assignments will be administered in order to give participants practice applying techniques discussed in class and will be due as listed on the online syllabus. All homework assignments will be administered and submitted through the online system linked here. Each assignment will be worth between 8 and 10 points and will consist of data analysis, results interpretations, and questions about the topics assigned. There will also be a “homework 0” designed to familiarize participants with the online homework system that will be worth 3 bonus points.
In-Class Assessments (6 possible points):
Throughout the semester, short questions about the course material will be asked in class that participants will be expected to answer immediately via paper and pencil. Accordingly, please bring some form of scrap paper to each class. Make-up questions may be given only in the event of advanced notice of a class absence.
Individual Project (20 possible points):
Participants will get the opportunity to analyze data of their choosing to complete an individual project. Requirements are available for download within the online homework portal. Portions of the project will be due throughout the semester as noted in the online syllabus. Documents should be submitted electronically via Blackboard assignments as a Microsoft Word document using this naming convention: 944_Section_Lastname_Project#.docx (e.g., 944_1_Hoffman_Project1.docx for part 1). Please use track changes in Microsoft Word when revising parts of the project previously submitted.
Policy on Late Homework Assignments:
In order to be able to provide the entire class with prompt feedback, any late homework assignment or portion of the project will incur a 3-point penalty if submitted at any point past the due date. If extenuating obligations or circumstances will prevent you from completing any course requirements (e.g., conferences, family obligations), please contact the instructor at least three weeks advance so that we can create a modified deadline together.
Policy on Late Individual Projects:
The due date for submitting the completed projects falls shortly before course grades are due. Therefore, late projects will not be accepted.
Policy on Assigning Incompletes:
A grade of “incomplete” will be assigned ONLY in the case of extenuating circumstances that prevent participants from completing course requirements on time (e.g., a health emergency).
Final grades 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-, < 80 = C or no pass
Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583-619.
Curran, P. J., & Bauer, D. J. (2007). Building path diagrams for multilevel models. Psychological Methods, 12(3), 283-297.
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121-138.
Hedeker, D., & Mermelstein, R. J. (2012). Mood changes associated with smoking in adolescents: An application of a mixed-effects location scale model for longitudinal ecological momentary assessment (EMA) data. In J. Harring & G. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 59-79). Charlotte, NC: Information Age Publishing.
Hoffman, L. (in preparation). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.
Hoffman, L. (2012). Considering alternative metrics of time: Does anybody really know what “time” is? In J. Harring & G. Hancock (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 255-287). Charlotte, NC: Information Age Publishing.
Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623-641.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
Raudenbush, S.W., Brennan, R.T., & Barnett, R.C. (1995). A multivariate hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9(2), 161-174.
Sayer, A. G., & Klute, M. M. (2005). Analyzing couples and families. In V. L. Begtson, A. Acock, K. R. Allen, P. Dilworth-Anderson & D. M. Klein (Eds.), Sourcebook of family theory and research (pp. 289-313). Thousand Oaks, CA: Sage.
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.
Sliwinski, M. J., Hoffman, L., & Hofer, S. M. (2010). Evaluating convergence of within-person change and between-person age differences in age-heterogeneous longitudinal studies. Research in Human Development, 7(1), 45-60.
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.