Fall 2017 CLDP 944: Multilevel Models for Longitudinal and Repeated Measures Data

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

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.