Spring 2018 CLDP 945: Advanced Multilevel Models

Tentative Schedule of Events (Printable Syllabus; last updated 2/20/2018)

Course Objectives, Materials, and Pre-Requisites:

This course will continue to illustrate multilevel models (i.e., general linear mixed models, hierarchical linear models) for the analysis of longitudinal and repeated measures data, but will focus on more complex designs and advanced uses. After reviewing two-level longitudinal models, the course will cover multiple extensions, including models for accelerated time, cross-classification, multivariate models for time-varying predictors, three-level outcomes, and heterogeneity of variance. Class time will be devoted primarily to lectures and examples. Lecture materials in .pdf format 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. 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 assistance. CLDP 945 has a pre-requisite of CLDP 944: Multilevel Models for Longitudinal and Repeated Measures Data (last offered Fall 2017). Participants should be comfortable with CLDP 944 course material and SAS for mixed models prior to enrolling in this course.

Academic Honesty:

As a reminder, the University of Kansas has a formal policy on academic honesty. All 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 88 points can be earned from homework assignments (one due approximately every two weeks). Up to 12 points may be earned from submitting outside-of-class quizzes . Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the online syllabus for more information). There may be other opportunities to earn extra credit at the instructor's discretion.

Update posted 4/17/18: Due to the removal of one homework, the number of possible homework points will be 78 instead of 88.

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, 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 . Finally, a final grade of “incomplete” will only be given in the event of dire circumstances and at the instructor's discretion.

Any homework assignments that involve individual original writing will have the opportunity to be revised ONCE to earn the maximum total points. Written assignments must be at least ¾ complete to be accepted, and late revisions will incur a 1-point penalty. No late points will be returned through the revision process. Please use “track changes” and retain all original instructor comments (unless otherwise instructed) so that I can easily see how your revisions address the comments.

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):

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255-278.

Bauer, D. J. (2003). Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28(2), 135-167.

Bauer, D. (2009). A note on comparing the estimates of models for cluster-correlated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74(1), 97-105.

Berry, D., & Willoughby, M. (2017). On the practical interpretability of cross‐lagged panel models: Rethinking a developmental workhorse. Child Development, 88(4), 1186-1206.

Curran, P. J., Lee, T., Howard, A. L., Lane, S., & MacCallum, R. C. (2012). Disaggregating within-person and between-person effects in multilevel and structural equation growth models. In G. Hancock & J. Harring (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 217-253). Charlotte, NC: Information Age Publishing

Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82(5), 879-894.

Guo, G., & Wang, J. (2002). The mixed or multilevel model for behavior genetic analysis. Behavior Genetics, 32(1), 37-49.

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102-116.

Hedeker, D., & Mermelstein, R. J. (2012). Mood changes associated with smoking in adolescents: An application of a mixed-effects location scale model for longitudinal ecological momentary assessment (EMA) data. In G. Hancock & J. Harring (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 59-79). Charlotte, NC: Information Age Publishing.

Hoffman, L. (2012). Considering alternative metrics of time: Does anybody really know what “time” is? In J. Harring & G. Hancock (Eds.), Advances in Longitudinal Methods in the Social and Behavioral Sciences (pp. 255-287). Charlotte, NC: Information Age Publishing.

Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed). New York, NY: Routledge Academic.

Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13(3), 203-229.

Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing Type I error and power in linear mixed models. Journal of Memory and Language, 94, 305-315.

Nakagawa, S. & Schielzeth, S. (2010). Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biological Reviews, 85, 935-956.

O'Keefe, P., & Rodgers, J. (2017). Double decomposition of level-1 variables in multilevel models: An analysis of the Flynn Effect in the NSLY Data. Multivariate Behavioral Research, 52(5), 630-647.

Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011). Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling, 18, 161-182.

Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209-233.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

Walters, R. W., & Hoffman, L. (2017). Applying the hierarchical linear model to longitudinal data. Cultura y Educación, 29(3), 666-701.