Instructor:  Professor Lesa Hoffman (she, her, hers) Educational Measurement and Statistics Program  Department:  Psychological and Quantitative Foundations Office: 361 Lindquist Center (South); DEO: Dr. Saba Rashid Ali 
Instructor Office:  356 Lindquist Center (South)  Instructor Email:  LesaHoffman@UIowa.edu 
Course Room:  North 166 Lindquist Center  Course Time: Office Hours: 
Tuesdays and Thursdays 12:30–1:45 PM Tuesdays and Thursdays 1:453:00 PM in the course room 
Course Textbook:  Longitudinal Analysis: Modeling WithinPerson Fluctuation and Change 
SAS Resources:  Lesa's SAS guide from PilesOfVariance.com SAS MIXED Online Manual 
Online Homework:  coming soon! 
Week 
Date 
Course Materials 
Readings 

1  1/15  Course Introduction Lecture 1: Introduction to Multilevel Models for Longitudinal Data Course Introduction and Lecture 1 Part 1: Video NO OFFICE HOURS 
Hoffman ch. 1; Willett (1989) 
1/17  Introduction to SAS Files (for download only; do not print) Introduction to SAS Part 1: Video 

1/18  NO HOMEWORK DUE  
2  1/22  Introduction to SAS, continued Lecture 1, continued Lecture 2a: Review of General Linear Models 
Hoffman ch. 2 sec. 1 
1/24  Lecture 2a, continued Example 2a: Review of General Linear Models in SAS MIXED (SAS Files) 

1/25  HOMEWORK #0 DUE BY 11:59 PM ONLINE: 3 points extra credit for testing the online homework system 

3  1/29  Lecture 2a and Example 2a, continued  
1/31  QUIZ #1 DUE BY 12:15 PM VIA CANVAS Lecture 3: Introduction to WithinPerson Analysis and RM ANOVA Example 3a: Between vs. WithinPerson Models (SAS Files) 
Hoffman ch. 3 sec. 1  
2/1  HOMEWORK #1 DUE BY 11:59 PM VIA CANVAS: Make Friends with SAS 

4  2/5  Lecture 3 and Example 3a, continued Example 3b: Kinds of Analyses of Variance (SAS Files) (LRTs in Excel) 
Hoffman ch. 3 sec. 2+ 
2/7  MEET IN 108 LC (South) Lecture 3 and Example 3b, continued 

2/8  HOMEWORK #2 DUE BY 11:59 PM ONLINE: Review of General Linear Models 

5  2/12  QUIZ #2 DUE BY 12:15 PM VIA CANVAS Lecture 4: Describing WithinPerson Fluctuation over Time via ACS Models Example 4: Describing WithinPerson Fluctuation over Time (LRTs in Excel) 
Hoffman ch. 4 sec. 12 
2/14  Lecture 4 and Example 4, continued 
Hoffman ch. 4 sec. 3+  
2/15  NO HOMEWORK DUE  
6  2/19  Lecture 5: Introduction to Random Effects of Time and Model Estimation Example 5: Practice with Random Effects of Time (SAS and Excel Files) 
Hoffman ch. 5 sec. 12 
2/21  Lecture 5 and Example 5, continued 
Hoffman ch. 5 sec. 3+  
2/22  HOMEWORK #3 DUE BY 11:59 PM ONLINE: ACS Models 

7  2/26  QUIZ #3 DUE BY 12:15 PM VIA CANVAS Lecture 5 and Example 5, continued 
Enders ch. 35 
2/28  Lecture 5 and Example 5, continued  
3/1  REVISIONS TO HOMEWORK#1 DUE BY 11:59 PM VIA CANVAS  
8  3/5  Lecture 6: Describing WithinPerson Change (SAS and Excel Files) Example 6: Polynomial, Piecewise, and Exponential Models of Change 
Hoffman ch. 6 sec. 12A 
3/7  Lecture 6 and Example 6, continued  
3/8  HOMEWORK #4 DUE BY 11:59 PM ONLINE: Linear Time Random Effects Models 

9  3/12  QUIZ #4 DUE BY 12:15 PM VIA CANVAS Lecture 6 and Example 6, continued 
Hoffman ch. 6 sec. 2B 
3/14  Lecture 6 and Example 6, continued  
3/15  NO HOMEWORK DUE  
10  3/19  NO CLASS OR OFFICE HOURS  
3/21  NO CLASS OR OFFICE HOURS  
3/22  NO HOMEWORK DUE  
11  3/26  Lecture 6 and Example 6, continued 
Hoffman ch. 6 sec. 2C+ 
3/28  MEET IN 108 LC (South) Lecture 7a: Review of Unconditional Models of Time 

3/29  HOMEWORK #5 DUE BY 11:59 PM ONLINE: Quadratic Time Random Effects Models 

12  4/2  QUIZ #5 DUE BY 12:15 PM VIA CANVAS Lecture 2b: Interactions among Continuous Predictors Example 2b: Interactions among Continuous Predictors (SAS Files) 
Hoffman ch. 2 sec. 2 
4/4  Lecture 2b and Example 2b, continued  
4/5  NO HOMEWORK DUE  
13  4/9  Lecture 2c: Interactions among Categorical Predictors Example 2c: Interactions among Categorical Predictors (SAS Files) 
Hoffman ch. 2 sec. 3+ 
4/11  Lecture 2c and Example 2c, continued  
4/12  HOMEWORK #6 DUE BY 11:59 PM ONLINE: Piecewise Time Random Effects Models 

14  4/16  Lecture 7b: TimeInvariant Predictors in Longitudinal Models Example 7: TimeInvariant Predictors in Models of Change (SAS and Excel files) 
Hoffman ch. 7 
4/18  Lecture 7b and Example 7b, continued  
4/19  HOMEWORK #7 DUE BY 11:59 PM ONLINE: Interactions among Continuous Predictors 

15  4/23  QUIZ #6 DUE BY 12:15 PM VIA CANVAS Lecture 7b and Example 7b, continued 

4/25  Lecture 8: Analysis of Repeated Measures Designs not Involving Time Example 8a: Crossed Persons and Words 
Hoffman ch. 12 sec. 12  
4/26  NO HOMEWORK DUE  
16  4/30  Example 8b: Analysis of Eye Movements  Hoffman ch. 12 sec. 3+ 
5/2  Example 8b, continued Time for Course Evaluations as Needed 

5/3  HOMEWORK #8 DUE BY 11:59 PM ONLINE: TimeInvariant Predictors 

17  5/10  ALL OUTSTANDING WORK MUST BE COMPLETED BY 11:59 PM 
The planned schedule of topics and events may need to be adjusted throughout the course. The online syllabus above will always have the most current schedule and course materials.
This course will illustrate the uses of multilevel models (i.e., general linear mixedeffect models, hierarchical linear models) for the analysis of longitudinal (and other repeated measures) data. The course is organized to take participants through each of the cumulative steps in a longitudinal 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. Lecture materials will be available for download at the website above the day prior to class, or else paper copies can be requested. 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 as needed; the initial list of readings below may be updated. There will be no exams nor any required attendance outside class. However, because the course will have an applied focus requiring statistical software, instructor office hours will also be held in a groupbased format, in which multiple participants will have (optional) opportunities to work on course assignments and receive immediate assistance in turn. Participants should be comfortable with estimating and interpreting general linear models (i.e., analysis of variance, regression) prior to enrolling in this course.
Participants will have the opportunity to earn up to 100 total points in this course. Up to 88 points can be earned from homework assignments (approximately 8 in total). Up to 12 points may be earned from submitting outsideofclass quizzes (approximately 6 in total). Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the above syllabus). There may be other opportunities to earn extra credit at the instructor's discretion.
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. Late or incomplete outsideofclass quizzes will incur a 1point penalty when submitted. A final grade of “incomplete” will only be given in dire circumstances and entirely at the instructor's discretion.
>96 = 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−, 67–69 = D+, 63–66 = D, 60–62 = D−, <60 = F
Participants will also need to have access to software that can estimate the models presented. Although the course will feature SAS as its primary package, other software packages (e.g., SPSS, STATA, R) can also be used to complete homework assignments. These packages are freely available to University of Iowa members through the UIowa Virtual Desktop.
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.
As a reminder, the University of Iowa College of Education has a formal policy on academic misconduct, which all students in this course are expected to follow. Please consult the instructor if you have questions.
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.
It is my intent that students from ALL backgrounds and perspectives feel welcome and encouraged to participate in this course. There is no such thing as a “stupid” question. All course participants—enrolled students and auditors—should always feel welcome to ask whatever questions will be helpful in helping them understand and follow the course content. You may do so during class, in office hours, over email, or in individual appointments with the instructor (available by request).
The instructor realizes that this course is not your only obligation in your work or your life. If work or life events (expected or unexpected) may compromise your ability to succeed in this course, PLEASE contact the instructor for a confidential discussion (in person or over email, as you prefer) so that we can work together to make a plan for your success. Please do not wait to do so until you are too far behind to catch up!