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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: Lesa-Hoffman@UIowa.edu
Course Room: North 166 Lindquist Center Course Time:
Office Hours:
Tuesdays and Thursdays 12:30–1:45 PM
Tuesdays and Thursdays 1:45-3:00 PM in the course room
Course Textbook: Longitudinal Analysis:
Modeling Within-Person Fluctuation and Change
SAS Resources: Lesa's SAS guide from PilesOfVariance.com
SAS MIXED Online Manual
Online Homework: coming soon!

Planned Schedule of Events (Printable Syllabus; last updated 1/11/2019)

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 Within-Person Analysis and RM ANOVA
Example 3a: Between vs. Within-Person 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 Within-Person Fluctuation over Time via ACS Models
Example 4: Describing Within-Person Fluctuation over Time (LRTs in Excel)
Hoffman ch. 4 sec. 1-2
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. 1-2
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. 3-5
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 Within-Person Change (SAS and Excel Files)
Example 6: Polynomial, Piecewise, and Exponential Models of Change
Hoffman ch. 6 sec. 1-2A
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: Time-Invariant Predictors in Longitudinal Models
Example 7: Time-Invariant 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. 1-2
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:
Time-Invariant Predictors
 
       
17 5/10 ALL OUTSTANDING WORK MUST BE COMPLETED BY 11:59 PM  

Schedule of Topics and Events:

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.

Course Objectives, Materials, and Pre-Requisites:

This course will illustrate the uses of multilevel models (i.e., general linear mixed-effect 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 time-invariant 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 group-based 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.

Course Requirements:

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 outside-of-class 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.

Policy on Late Homework Assignments and Incompletes:

In order to be able to provide the entire class with prompt feedback, late homework assignments will incur a 3-point 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 outside-of-class quizzes will incur a 1-point penalty when submitted. A final grade of “incomplete” will only be given in dire circumstances and entirely at the instructor's discretion.

Final grades will be determined according to the proportion earned of the total possible points:

>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

Course Software:

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.

Course Textbook:

Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.

Other Course Readings (available via "Files" on Canvas):

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, 587-602.

Academic Misconduct:

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.

Accommodating Students with Disabilities:

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.

Respect for Diversity:

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).

Respect for The Rest of Your World:

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!