Instructor:  Dr. Lesa Hoffman  Email:  Lesa@ku.edu 
Rooms:  3049 Dole  Office:  3042 Dole 
Time:  1:152:30 Mondays and Wednesdays  Office Hours:  2:304:00 Mondays and Wednesdays in 3049 Dole and by individual appointment 
Course Textbook (Mine!) is
Longitudinal Analysis: Modeling WithinPerson Fluctuation and Change Piles of Variance Book Website (CLDP 945 Online Homework Portal now longer active) Lesa's SAS guide from PilesOfVariance.com Lesa's SAS MLM Macros 
Link to SAS University Edition 
Week 
Date 
Course Materials 
Readings 

1  1/15  NO CLASS OR OFFICE HOURS  
1/17  Course Introduction Lecture 1: Review of CLDP 944 Lecture 1 Part 1: Video 
Hoffman ch. 16  
1/19  NO HOMEWORK DUE  
2  1/22  Lecture 1, continued Lecture 1 Part 2: Video 
Walters & Hoffman (2017) 
1/24  QUIZ 1 DUE BY 1:00 PM VIA BLACKBOARD Example 1: Story Time with TimeInvariant Predictors Example 1 Part 1: Video 
Hoffman ch. 2, 7  
1/26  HOMEWORK 0 DUE BY 11:59 PM ONLINE: 3 points extra credit for testing the online homework system 

3  1/29  Example 1, continued Example 1 Part 2: Video 

1/31  Lecture 2: Alternative Metrics of Time Lecture 2: Video 
Hoffman ch. 10 sections 12 Hoffman (2012) O'Keefe & Rodgers (2017) 

2/2  HOMEWORK 1 DUE FRIDAY 2/2 BY 11:59 PM ONLINE: Practice with ModelPredicted Fixed Effects 

4  2/5  Example 2: Comparing Accelerated Metrics of Time (Plots in Excel) Example 2: Video 

2/7  QUIZ 2 DUE BY 1:00 PM VIA BLACKBOARD Lecture 3: TimeVarying Predictors of WithinPerson Fluctuation Example 3a: Predicting Weekly Psoriasis from Weekly Stress (Plot in Excel) Lecture 3 Part 1 and Example 3a: Video 
Hoffman ch. 8 section 12  
2/9  NO HOMEWORK DUE  
5  2/12  Lecture 3, continued Example 3b: Predicting Daily Glucose from Daily Negative Mood (SAS Files) (Excel File) Lecture 3 and Example 3b Part 1: Video 
Hoffman ch. 8 section 3+ 
2/14  Lecture 3 and Example 3b, continued Lecture 3 and Example 3b Part 2: Video 

2/16  HOMEWORK 2 DUE BY 11:59 PM ONLINE: Growth Models for Accelerated Longitudinal Designs 

6  2/19  Lecture 4: Model Assumptions and Predicting Heterogeneity of Variance Lecture 4: Video (incomplete due to technical difficulties) 
Hoffman ch. 7 Appendix 
2/21  QUIZ 3 DUE BY 1:00 PM VIA BLACKBOARD Lecture 4, continued Example 4a: Models for Twin Analysis Example 4b: Predicting WithinPerson Fluctuation and Heterogeneity Quiz 3 Part 1: Video Quiz 3 Part 2: Video Example 4a and 4b: Video (incomplete due to technical difficulties) 
Guo & Wang (2002) Hoffman ch. 7 section 2C Hoffman ch. 8 section 3F Hedeker & Mermelstein (2012) 

2/23  NO HOMEWORK DUE  
7  2/26  Lecture 5: Analysis of Crossed Repeated Measures Designs not Involving Time Example 5a: Crossed Subjects and Items Lecture 5 and Example 5a Part 1: Video Lecture 5 Part 2: Sorry, No Video 
Hoffman ch. 12 
2/28  Lecture 5 and Example 5a, continued Example 5b: Analysis of Eye Movements Review of PseudoR2 Computation and Example 5b: Video 
Barr et al. (2013) Matuschek et al. (2017) 

3/2  HOMEWORK 3 DUE BY 11:59 PM ONLINE: Univariate Approach to TimeVarying Predictors 

8  3/5  Lecture 6: TwoLevel Models for Clustered Data Example 6a: CrossSectional Models for Children Nested in Schools (updated 32618) (Syntax, Output, and Excel Files) Lecture 6 and Example 6a Part 1: Video 
Raudenbush & Bryk (2002) ch. 5 
3/7  QUIZ 4 DUE BY 1:00 PM VIA BLACKBOARD Lecture 6 and Example 6a, continued Lecture 6 and Example 6a Part 2: Video (microphone dies after 53 minutes) 

3/9  NO HOMEWORK DUE  
9  3/12  Lecture 6 and Example 6a, continued Example 6b: CrossClassified Models for Clustered Data Example 6a Part 3 and Example 6b: Video 
Raudenbush & Bryk (2002) ch. 12 
3/14  Example 6c: Changes in Nesting over Time (sorry, no video for today) 
Hoffman ch. 11 section 1, 3  
3/16  HOMEWORK 4 DUE BY 11:59 PM ONLINE: Crossed Random Effects Models 

10  3/19  NO CLASS OR OFFICE HOURS  
3/21  NO CLASS OR OFFICE HOURS 

3/23  NO HOMEWORK DUE  
11  3/26  Lecture 7: Generalized Multilevel Models Lecture 7 Part 1: Video 
Hoffman ch. 13 sec. 2 
3/28  Example 7a: Clustered Models with Binary Outcomes (Plots in Excel) Lecture 7 and Example 7a Part 2: Video 
Bauer (2009)  
3/30  NO HOMEWORK DUE  
12  4/2  HOMEWORK 5 DUE BY 11:59 PM ONLINE: Models for Clustered Observations Lecture 7 and Example 7a, continued Lecture 7 and Example 7a Part 3: Video 

4/4  QUIZ 5 DUE BY 1:00 PM VIA BLACKBOARD Lecture 7, continued Example 7b: Longitudinal Models with Ordinal Outcomes (Plots in Excel) (sorry, no video) 
Hox (2010) ch. 6  
4/6  NO HOMEWORK DUE  
13  4/9  Example 7b, continued Example 7c: Longitudinal Binomial Models for Percent Correct Example 7b and Example 7c Part 1: Video 
Hox (2010) ch. 7 Nakagawa & Schielzeth (2010) 
4/11  Example 7c, continued Example 7d: Explanatory IRT Models as Crossed Random Effects Models in SAS GLIMMIX Lecture 7, continued All today's material in one video: Video 

4/13  NO HOMEWORK DUE  
14  4/16  Lecture 8: ThreeLevel Random Effects Models Example 8a: ThreeLevel Models for Longitudinal Twin Data (Excel calculations) Lecture 8 and Example 8a Part 1: Video 
Hoffman ch. 11 section 12 
4/18  Lecture 8 and Example 8a, continued Lecture 8 and Example 8a Part 2: Video 

4/20  HOMEWORK 6 DUE BY 11:59 PM ONLINE: Generalized Multilevel Models 

15  4/23  Lecture 8, continued Example 8b: ThreeLevel Models for Intensive Longitudinal (EMA) Designs (Excel calculations) Lecture 8 and Example 8b Part 1: Video 
Hoffman ch. 10 section 3 
4/25  QUIZ 6 DUE BY 1:00 PM VIA BLACKBOARD Lecture 8 and Example 8b, continued Example 8b Part 2: Video 

4/27  NO HOMEWORK DUE  
16  4/30  Lecture 9: Multivariate Multilevel Models for Longitudinal Data Example 9a: Multivariate Models of Change (SAS and Mplus files) Lecture 9 and Example 9a Part 1: Video 
Hoffman ch. 9 Lüdtke et al. (2008) Preacher et al. (2010; 2011) 
5/2  Lecture 9 and Example 9b, continued Example 9b: Mediation of WithinPerson Fluctuation (SAS and Mplus files) Example 9c: Three Ways of Estimating Multivariate Models of Change in MLM and SEM (SAS and Mplus files) Lecture 9, Example 9a, Example 9b, Example 9c Part 2: Video Course Evaluations 
Hamaker et al. (2015) Bauer (2003) Curran et al. (2012, 2014) Berry & Willoughby (2017) 

5/4  NO HOMEWORK DUE  
17  5/11  HOMEWORK 7 DUE BY 11:59 PM ONLINE: ThreeLevel Models 
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 twolevel longitudinal models, the course will cover multiple extensions, including models for accelerated time, crossclassification, multivariate models for timevarying predictors, threelevel 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 prerequisite 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.
As a reminder, the University of Kansas has a formal policy on academic honesty. All assignments should be done individually without exception.
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.
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 outsideofclass 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.
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, 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 . 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 1point 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.
> 92 = A, 90–92 = A, 8789 = B+, 8386 = B, 8082 = B, < 80 = C or no pass
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).
Hoffman, L. (2015). Longitudinal analysis: Modeling withinperson fluctuation and change. New York, NY: Routledge Academic.
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, 255278.
Bauer, D. J. (2003). Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28(2), 135167.
Bauer, D. (2009). A note on comparing the estimates of models for clustercorrelated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74(1), 97105.
Berry, D., & Willoughby, M. (2017). On the practical interpretability of cross‐lagged panel models: Rethinking a developmental workhorse. Child Development, 88(4), 11861206.
Curran, P. J., Lee, T., Howard, A. L., Lane, S., & MacCallum, R. C. (2012). Disaggregating withinperson and betweenperson 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. 217253). Charlotte, NC: Information Age Publishing
Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of betweenperson and withinperson components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82(5), 879894.
Guo, G., & Wang, J. (2002). The mixed or multilevel model for behavior genetic analysis. Behavior Genetics, 32(1), 3749.
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the crosslagged panel model. Psychological Methods, 20(1), 102116.
Hedeker, D., & Mermelstein, R. J. (2012). Mood changes associated with smoking in adolescents: An application of a mixedeffects 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. 5979). 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. 255287). 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 grouplevel effects in contextual studies. Psychological Methods, 13(3), 203229.
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, 305315.
Nakagawa, S. & Schielzeth, S. (2010). Repeatability for Gaussian and nonGaussian data: a practical guide for biologists. Biological Reviews, 85, 935956.
O'Keefe, P., & Rodgers, J. (2017). Double decomposition of level1 variables in multilevel models: An analysis of the Flynn Effect in the NSLY Data. Multivariate Behavioral Research, 52(5), 630647.
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, 161182.
Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209233.
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), 666701.