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Instructor: Dr. Lesa Hoffman Email: Lesa@ku.edu
Rooms: 3049 Dole Office: 3042 Dole
Time: 1:15-2:30 Mondays and Wednesdays Office Hours: 2:30-4:00 Mondays and Wednesdays in 3049 or 3042 Dole; also by individual appointment
Course Textbook (Mine!):
Longitudinal Analysis: Modeling Within-Person 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

Tentative Schedule of Events (Printable Syllabus; last updated 8/21/2017)

s

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 Within-Person Analysis and RM ANOVA
Example 3a: Between vs. Within-Person 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 Within-Person Fluctuation over Time via ACS Models
Example 4: Describing Within-Person Fluctuation over Time (LRTs in Excel)
Lecture 4 and Example 4 Part 1: Video
Hoffman ch. 4 sec. 1-2
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. 1-2
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. 3-5
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 Within-Person 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. 1-2A
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: Time-Invariant Predictors in Longitudinal Models
Example 7: Time-Invariant 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 1:00-4:00  
12/15 HOMEWORK #8 DUE FRIDAY 12/15 BY 11:59 PM ONLINE:
Time-Invariant Predictors
 

Course Objectives, Materials, and Pre-Requisites:

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 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; opportunities to earn participation points via in-class 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 pre-requisite 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.

Academic Honesty:

As a reminder, the University of Kansas has a formal policy on academic honesty. All course assignments should be done individually without exception.

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.

Course Requirements:

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 in-class 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).

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. As noted above, missed in-class 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.

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

> 92 = A, 90–92 = A-, 87-89 = B+, 83-86 = B, 80-82 = B-, < 80 = C or no pass

Course Software:

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).

Course Textbook:

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

Other Course Readings (all will be available via "Course Documents" on KU Blackboard):

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.