|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 Dole and by individual appointment
| Course Textbook (Mine!) is
Longitudinal Analysis: Modeling Within-Person Fluctuation and Change
Piles of Variance Book Website
CLDP 945 Online Homework Portal
Lesa's SAS guide from PilesOfVariance.com
Lesa's SAS MLM Macros
Link to SAS University Edition
|1||1/15||NO CLASS OR OFFICE HOURS|
Lecture 1: Review of CLDP 944
Lecture 1 Part 1: Video
|Hoffman ch. 1-6|
|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 Time-Invariant 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 1-2
O'Keefe & Rodgers (2017)
|2/2||HOMEWORK 1 DUE FRIDAY 2/2 BY 11:59 PM ONLINE:
Practice with Model-Predicted 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: Time-Varying Predictors of Within-Person Fluctuation
Example 3a: Predicting Weekly Psoriasis from Weekly Stress (Plot in Excel)
Lecture 3 Part 1 and Example 3a: Video
|Hoffman ch. 8 section 1-2|
|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 Within-Person 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 Pseudo-R2 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 Time-Varying Predictors
|8||3/5|| Lecture 6: Two-Level Models for Clustered Data
Example 6a: Cross-Sectional Models for Children Nested in Schools (updated 3-26-18)
(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: Cross-Classified 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
|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: Three-Level Random Effects Models
Example 8a: Three-Level Models for Longitudinal Twin Data (Excel calculations)
Lecture 8 and Example 8a Part 1: Video
|Hoffman ch. 11 section 1-2|
|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: Three-Level 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 Within-Person 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
|Hamaker et al. (2015)
Curran et al. (2012, 2014)
Berry & Willoughby (2017)
|5/4||NO HOMEWORK DUE|
|17||5/11|| HOMEWORK 7 DUE BY 11:59 PM ONLINE:
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.
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 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.
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.
> 92 = A, 90–92 = A-, 87-89 = B+, 83-86 = B, 80-82 = 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 within-person 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, 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.