Instructor:  Dr. Lesa Hoffman  Email:  Lesa@ku.edu 
Rooms:  3049 Dole  Office:  3042 Dole 
Time:  1:152:30 Mondays and Wednesdays  Office Hours:  2:304:00 Mondays and Wednesdays in 3049 or 3042 Dole; also by individual appointment 
Course Textbook (Mine!): Longitudinal Analysis: Modeling WithinPerson Fluctuation and Change CLDP 944 Online Homework Portal Lesa's SAS guide from PilesOfVariance.com 
Link to SAS University Edition Link to list of KU computer labs (filter list for those that have SAS) SAS MIXED Online Manual 
Week 
Date 
Course Materials 
Readings 

1  8/21  Course Introduction Lecture 1: Introduction to Multilevel Models for Longitudinal and Repeated Measures Data Course Introduction and Lecture 1 Part 1: Audio Only (sorry) 
Hoffman ch. 1; Willett (1989) 
8/23  Make Friends with SAS (download only; do not print) Make Friends with SAS Part 1: Video 

8/25  HOMEWORK #0 DUE FRIDAY 8/25 BY 11:59 PM ONLINE: 3 points extra credit for testing the online homework system 

2  8/28  Make Friends with SAS Part 2: Video 

8/30  Lecture 1 Part 2: No Video, sorry! Lecture 2a: Review of General Linear Models Lecture 2 Part 1: No Video, sorry! 
Hoffman ch. 2 sec. 1  
9/1  HOMEWORK #0B DUE FRIDAY 9/1 BY 11:59 PM VIA BLACKBOARD: 3 points extra credit for demonstrating home access to SAS 

3  9/4  NO CLASS OR OFFICE HOURS  
9/6  Example 2a: Review of General Linear Models in SAS MIXED (SAS Files) Lecture 2 Part 2: Video 

9/8  HOMEWORK #1 DUE FRIDAY 9/8 BY 11:59 PM VIA BLACKBOARD: Make Friends with SAS 

4  9/11  Lecture 2 Part 3: Video Lecture 3: Introduction to WithinPerson Analysis and RM ANOVA Example 3a: Between vs. WithinPerson Models (SAS Files) Lecture 3 Part 1: No Video, sorry! 
Hoffman ch. 3 sec. 1 
9/13  Lecture 3 and Example 3a, continued Lecture 3 and Example 3a Part 2: Video 

9/15  HOMEWORK #2 DUE FRIDAY 9/15 BY 11:59 PM ONLINE: Review of General Linear Models 

5  9/18  Lecture 3 and Example 3a, continued Example 3b: Kinds of Analyses of Variance (SAS Files) (LRTs in Excel) Lecture 3 and Example 3a/3b Part 3: Video 
Hoffman ch. 3 sec. 2+ 
9/20  Lecture 3 and Example 3a/3b Part 4: Video 

9/22  NO HOMEWORK DUE  
6  9/25  Lecture 4: Describing WithinPerson Fluctuation over Time via ACS Models Example 4: Describing WithinPerson Fluctuation over Time (LRTs in Excel) Lecture 4 and Example 4 Part 1: Video 
Hoffman ch. 4 sec. 12 
9/27  Lecture 4 and Example 4, continued Lecture 4 and Example 4 Part 2: Video 
Hoffman ch. 4 sec. 3+  
9/29  NO HOMEWORK DUE  
7  10/2  HOMEWORK #3 DUE MONDAY 10/2 BY 11:59 PM ONLINE: ACS Models Lecture 5: Introduction to Random Effects of Time and Model Estimation Lecture 5 Part 1: Video 
Hoffman ch. 5 sec. 12 
10/4  NO CLASS OR OFFICE HOURS  
10/6  REVISIONS TO HW#1 DUE FRIDAY 10/16 by 11:59 PM VIA BLACKBOARD  
8  10/9  Lecture 5, continued Example 5: Practice with Random Effects of Time (SAS and Excel Files) Lecture 5 and Example 5 Part 2: Video 
Hoffman ch. 5 sec. 3+ 
10/11  Lecture 5 and Example 5, continued Lecture 5 and Example 5 Part 3: Video 
Enders ch. 35  
10/13  NO HOMEWORK DUE 

9  10/16  NO CLASS OR OFFICE HOURS  
10/18  Lecture 5 and Example 5, continued Lecture 5 and Example 5 Part 4: Video Answers to Quiz 3 

10/20  HOMEWORK #4 DUE FRIDAY 10/20 BY 11:59 PM ONLINE: Linear Time Random Effects Models 

10  10/23  Lecture 6: Describing WithinPerson Change (SAS and Excel Files) Example 6: Polynomial, Piecewise, and Exponential Models of Change Lecture 6 and Example 6 Part 1: Video 
Hoffman ch. 6 sec. 12A 
10/25  Lecture 6 and Example 6, continued Lecture 6 and Example 6 Part 2: Video 

10/27  NO HOMEWORK DUE  
11  10/30  Lecture 6 and Example 6, continued Lecture 6 and Example 6 Part 3: Video 
Hoffman ch. 6 sec. 2B 
11/1  Lecture 6 and Example 6, continued Lecture 6 and Example 6 Part 4: Video 
Hoffman ch. 6 sec. 2C+  
11/3  HOMEWORK #5 DUE FRIDAY 11/3 BY 11:59 PM ONLINE: Quadratic Time Random Effects Models 

12  11/6  Lecture 6 and Example 6, continued Lecture 6 and Example 6 Part 5: Video 

11/8  Lecture 2b: Interactions among Continuous Predictors Example 2b: Interactions among Continuous Predictors (SAS Files) Lecture 2b: Video 
Hoffman ch. 2 sec. 2 

11/10  NO HOMEWORK DUE  
13  11/13  Lecture 2b, continued Example 2b: Video 

11/15  Lecture 2c: Interactions among Categorical Predictors Example 2c: Interactions among Categorical Predictors (SAS Files) Lecture 2c and Example 2c: Video 
Hoffman ch. 2 sec. 3+ 

11/17  HOMEWORK #6 DUE FRIDAY 11/17 BY 11:59 PM ONLINE: Piecewise Time Random Effects Models 

14  11/20  NO CLASS OR OFFICE HOURS  
11/22  NO CLASS OR OFFICE HOURS  
11/24  NO HOMEWORK DUE  
15  11/27  Lecture 7a: Review of Unconditional Models of Time Lecture 7a: Video 

11/29  Lecture 7b: TimeInvariant Predictors in Longitudinal Models Example 7: TimeInvariant Predictors in Models of Change (SAS and Excel files) Lecture 7b and Example 7: Video 
Hoffman ch. 7  
12/1  HOMEWORK #7 DUE FRIDAY 12/1 BY 11:59 PM ONLINE: Interactions among Continuous Predictors 

16  12/4  Example 7, continued Example 7 Part 1: Video 

12/6  Lecture 7b and Example 7, continued Example 7 Part 2: Video Course Evaluations 

12/8  STOP DAY; NO HOMEWORK DUE  
17  12/13  OPEN LAB DAY 1:004:00  
12/15  HOMEWORK #8 DUE FRIDAY 12/15 BY 11:59 PM ONLINE: TimeInvariant Predictors 
This course will illustrate the uses of multilevel models (i.e., general linear mixed models, hierarchical linear models) for the analysis of longitudinal and repeated measures data. The course is organized to take participants through each of the cumulative steps in a multilevel analysis involving timeinvariant predictors: deciding which type of model is appropriate, organizing the data 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; opportunities to earn participation points via inclass assessments will also occur throughout the semester. 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. Video recordings of the class lectures will also be available 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; the initial list of readings below may be updated if needed. Because the course will have an applied focus using SAS software, instructor office hours will also be held in the 3049 Dole computer lab, in which participants will have opportunities to work on course assignments and receive immediate software assistance. This course will be a prerequisite for CLDP 945, Advanced Multilevel Models, to be offered Spring 2018. Participants should be comfortable with the general linear model (analysis of variance, regression) prior to enrolling in this course.
As a reminder, the University of Kansas has a formal policy on academic honesty. All course assignments should be done individually without exception.
Students with disabilities or who have other special needs are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.
Participants will have the opportunity to earn up to 100 total points in this course. Up to 84 points can be earned from homework assignments (approximately 8 in total). Up to 16 points may be earned from participation in inclass quizzes on the course material, but you must be present on the day the quiz is administered to earn those points. Please note there will also be an opportunity to earn up to 6 points of extra credit (labeled as homework 0 and homework 0B; see the online syllabus for more information).
In order to be able to provide the entire class with prompt feedback, late homework assignments will incur a 3point penalty. However, extensions will be granted as needed for extenuating circumstances (e.g., conferences, comprehensive exams, family obligations) if requested at least two weeks in advance of the due date. As noted above, missed inclass quizzes cannot be made up. Finally, a final grade of “incomplete” will only be given in the event of extremely dire circumstances and at the instructor's discretion.
> 92 = A, 90–92 = A, 8789 = B+, 8386 = B, 8082 = B, < 80 = C or no pass
Participants will also need to have access to SAS software, which is freely available in 3049 Dole and in other computer labs across campus, as well as online through the KU Academic Computing Facility and by downloading the SAS University Edition. Individual licenses can also be purchased from the KU software store ($150 each; yearly renewal required).
Hoffman, L. (2015). Longitudinal analysis: Modeling withinperson fluctuation and change. New York, NY: Routledge Academic.
Enders, C. K. (2010; chapters 3–5). Applied missing data analysis. New York, NY: Guilford.
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, 587602.