Lesa's course directory

Previous version of this course (Spring 2021)
Contact Information:
Professor Lesa Hoffman
(she/her—you can call me Lesa)

Educational Measurement and Statistics
Email: Lesa-Hoffman@UIowa.edu (preferred contact)
Office: 356 South LC (mostly unattended)
Phone: 319-384-0522 (mostly unattended)
Home Department Information: Psychological and Quantitative Foundations (PSQF)
Office: South 361 Lindquist Center
DEO: Professor Saba Ali
Course Location
and Time:
166 North Lindquist Center or via zoom
Tuesdays and Thursdays 12:30–1:45 PM
Office Hours:
Mondays and Wednesdays 3:30–4:30 PM as a group
or individually by appointment
Zoom Link for Class and Office Hours: https://uiowa.zoom.us/my/lesahoffmaniowa
Meeting ID: 5044356512; Mobile Access: +13126266799
Textbook and Supplemental Materials: - Longitudinal Analysis:
Modeling Within-Person Fluctuation and Change
- Full syntax examples at PilesOfVariance.com
ICON for Formative Assessments

U Iowa Virtual Desktop Software

Link to Homework Portal now available!

For help getting started with the online homework system, Virtual Desktop, SAS, STATA, or R, please see the videos and handouts posted 2/7/22 in this previous class
Program Documentation and Resources (to be updated throughout):
- Lesa's SAS guide from PilesOfVariance.com
- SAS PROC MIXED Online Manual

- Lesa's Stata guide from PilesOfVariance.com
- STATA MIXED Online Manual

- R: TeachingDemos package, HAVEN package, EXPSS package, READXL package, LM, MULTCOMP package, GLS (within NLME package), LMER (within LME4 package), LAVAAN package

Planned Schedule of Events (Printable Syllabus; last updated 8/24/2022)

Week Number

Weekday and Date

Topics and Course Materials

Readings for
Each Topic

T: 8/23 Lecture 1: Introduction to the Course and Multilevel Models for Longitudinal Data
Video: Lecture 1 Part 1 (slides 1-21)
Hoffman (2015) ch. 1
Willett (1989)
R: 8/25 Lecture 1, continued
Video: Demo of online homework and Lecture 1 Part 2 (slides 22-28)

Unit 2: Review of General Linear Models --
review Lecture 1 and Example 1 from this class on your own as needed

Hoffman (2015) ch. 2
2 M: 8/29 HW0 (2 points extra credit) DUE ONLINE BY 11:59 PM  
T: 8/30 Lecture 3: Introduction to Within-Person Analysis and RM ANOVA
Video: Lecture 3 Part 1 (slides 1-15)

Example 3a: Between vs. Within-Person Models
Example 3a Files (.zip folder of data, syntax, and output)
Video: Example 3a Part 1 (pages 1-2)
Hoffman (2015) ch. 3
Castro-Schilo & Grimm (2018)
R: 9/1 Example 3a, continued
Video: Example 3a Part 2 (pages 3-7)
3 M: 9/5 NO HW OR FA DUE  
Lecture 3 and Example 3a, continued

Example 3b: Repeated Measures Analysis of Variance (RM ANOVA)
Example 3b Files (.zip folder of data, syntax, and output)

Video: Example 3a (pages 5-6), Lecture 3 (slides 14-23), and Example 3b (pages 1-4)

Lecture 3 and Example 3b, continued
Video: Example 3b (pages 2-8); Lecture 3 (slides 24-31)
4 M: 9/12 FA1 DUE VIA ICON BY 11:59 PM  
T: 9/13 Lecture 4 (updated 9/20/22) and Example 4: Describing Within-Person Fluctuation over Time via ACS Models
Example 4 Files (.zip folder of syntax and output and excel LRTs)
Video Part 1: Lecture 4 (slides 1-14) and Example 4 (pages 1-7
Hoffman (2015) ch. 4
R: 9/15 Lecture 4 and Example 4, continued
Video Part 2: Lecture 4 (slides 15-28) and Example 4 (pages 8-17)
5 M: 9/19 FA2 DUE VIA ICON BY 11:59 PM  
T: 9/20 Lecture 5 and Example 5:
Introduction to Random Effects of Time and Model Estimation
Example 5 Files (.zip folder of data, syntax, and output)
Excel table for FA2
Video Part 1: Lecture 5 (slides 1-14)
Hoffman (2015) ch. 5
Enders (2010) ch. 3-5
McNeish (2017)
R: 9/22 Lecture 5 and Example 5, continued
Video Part 2: Lecture 5 (slides 11-22) and Example 5 (pages 1-9)
Stoel et al. (2006)
6 M: 9/26 HW1 (based on Lectures 3-4) DUE ONLINE BY 11:59 PM  
T: 9/27 Lecture 5 and Example 5, continued
Video Part 3: Lecture 5 (slides 21-25) and Example 5 (pages 6-11)
McNeish & Matta (2018)
R: 9/29 Lecture 5 and Example 5, continued
Video Part 4: Example 5 (pages 9-15) and Lecture 5 (slides 25-51)
Yuan et al. (2019)
7 M: 10/3 FA3 DUE VIA ICON UNDER ASSIGNMENTS BY 11:59 PM: Practice with MLM Notation  
T: 10/4 Lecture 5, continued (and end of Lecture 3 for review)  
R: 10/6 Lecture 6: Describing Within-Person Change
Example 6a: Modeling Change over Time with Polynomial Trends
Hoffman (2015) ch. 6
8 M: 10/10 HW2 (based on Example 5) DUE ONLINE BY 11:59 PM  
T: 10/11 Lecture 6 and Example 6a, continued  
R: 10/13 NO OFFICE HOURS 10/12 AND NO CLASS 10/13  
9 M: 10/17 NO HW OR FA DUE  
T: 10/18 Lecture 6 and Example 6a, continued Johnson & Hancock (2019)
Lecture 6 and Example 6a, continued

McNeish (2020)
Practice with MLM Notation
T: 10/25 Lecture 6, continued
Example 6b: Modeling Change over Time with Piecewise Trends

Tuliao et al. (2017)
R: 10/27 Example 6b, continued  
11 M: 10/31 HW3 (based on Example 6a Polynomial Models) DUE ONLINE BY 11:59 PM  
T: 11/1 Example 6c: Modeling Change over Time Using Log Time to Approximate Exponential Trends Preacher & Hancock (2015)
R: 11/3 Example 6d: Modeling Change over Time with Truly Exponential Models  
12 M: 11/7 FA5 DUE VIA ICON BY 11:59 PM  
T: 11/8 Lecture 7a: Review of Unconditional Models of Time Walters & Hoffman (2017)
R: 11/10 Lecture 7a, continued  
13 M: 11/14 FA6 DUE VIA ICON BY 11:59 PM  
T: 11/15 Lecture 7b and Example 7b:
Time-Invariant Predictors in Longitudinal Models
Hoffman (2015) ch. 7
R: 11/17 Lecture 7b and Example 7b, continued Rights & Sterba (2019, 2020)
15 M: 11/28 HW4 (based on Example 6b Piecewise Models) DUE ONLINE BY 11:59 PM  
T: 11/29 Lecture 7b and Example 7b, continued Arend & Schäfer (2019)
R: 12/1 Lecture 7b and Example 7b, continued  
16 M: 12/5 FA7 DUE VIA ICON BY 11:59 PM  
T: 12/6 Lecture 7b and Example 7b, continued  
R: 12/8 Lecture 7b and Example 7b, continued  
17 M: 12/12 Office hours from 3:30-4:30 PM  
T: 12/13 NO CLASS, but office hours from 12:30-3:30 PM  
W: 12/14 Office hours from 3:30-4:30 PM  
R: 12/15 NO CLASS, but office hours from 12:30-3:30 PM  
F: 12/16 HW5 (based on Example 7b) DUE BY 5:00 PM ONLINE

Schedule of Topics and Events:

This course will meet synchronously in person and on zoom. The planned schedule of topics and events may need to be adjusted throughout the course. The online syllabus above will always have the most current schedule and corresponding due dates (i.e., the printable syllabus will not be updated unless noted).

Course Objectives, Pre-Requisites, and Materials:

This course will illustrate the uses of multilevel models (i.e., general linear mixed-effect models, hierarchical linear models) for the analysis of longitudinal (repeated measures) data. The course objective is for participants to be able to complete all the necessary steps in a longitudinal analysis involving time-invariant predictors: deciding which type of model is appropriate, restructuring the data and creating predictor variables, evaluating fixed and random effects and/or alternative covariance structures, predicting multiple sources of variation, and interpreting and presenting empirical findings. Prior to enrolling, participants should be comfortable with general linear models (e.g., regression, ANOVA).

Class time will be devoted primarily to lectures, examples, and spontaneous review, the materials for which will be available for download above. Readings and other resources have been suggested for each topic and may be updated later. Synchronous attendance (in person or via zoom) is encouraged but not required, and you do not need to notify the instructor of a single class absence. Video recordings of each class will be available on YouTube so that closed captioning will be provided, and supplemental videos for specific topics (e.g., software demos) may be added as well. Auditors and visitors are always welcome to attend class. No required class sessions will be held outside the regular class time noted above (i.e., no additional midterm or final exam sessions). However, because the course will have an applied focus requiring the use of statistical software, participants are encouraged to attend group-based office hours (via zoom only), in which multiple participants can receive immediate assistance on homework assignments simultaneously.

Course Requirements:

Participants will have the opportunity to earn up to 100 total points in this course by completing work outside of class. Up to 86 points can be earned from submitting homework assignments (approximately 5 in total) through a custom online system—these will be graded for accuracy. Up to 14 points may be earned from submitting formative assessments (approximately 7 in total); these will be graded for effort only—incorrect answers will not be penalized. Please note there will also be an opportunity to earn up to 2 extra credit points (labeled as homework 0). There may be other opportunities to earn extra credit at the instructor's discretion. Finally, revisions to the planned course schedule and/or content may result in fewer homework assignments and formative assessments (and thus fewer total points) at the instructor's discretion.

Policy on Accepting Late Work and Grades of Incomplete:

Participants may submit work at any point during the semester to be counted towards their course grade. However, in order to encourage participants to keep up with the class, late homework assignments will incur a 2-point penalty, and late formative assessments will incur a 1-point penalty. 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. A final grade of "incomplete" will only be given in dire circumstances and entirely at the instructor's discretion. All work must be submitted by Friday, December 16, 2022, at 5:00 PM to be included in the course grade.

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

>96% = A+, 93–96% = A, 90–92% = A−, 87–89% = B+, 83–86% = B, 80–82% = B−, 77–79% = C+, 73–76% = C, 70–72% = C− (PASS), 67–69% = D+, 63–66% = D, 60–62% = D−, <60% = F

Course Software:

Participants will need to have access to statistical software—SAS, STATA, or R+Rstudio—that can estimate the models presented. Each of these programs are freely available to course participants in multiple ways:

Course Textbook:

Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic. Available at the University of Iowa library in electronic form

Other Course Readings (all available in ICON under "Files"):

Note—I know this is A LOT of readings, but we are covering a lot of material! I encourage you to prioritize reading the textbook, as it will map most closely onto what we cover in class. Then should come class participation and completing course work, followed by these extra readings as time permits (included to give you some additional background and/or exposure to current best-practices in each topic).

Arend, M. G., & Schäfer, T. (2019). Statistical power in two-level models: A tutorial based on Monte Carlo simulation. Psychological Methods, 24(1), 1–19. https://doi.org/10.1037/met0000195

Castro-Schilo, L., & Grimm, K. J. (2018). Using residualized change versus difference scores for longitudinal research. Journal of Social and Personal Relationships, 35(1), 32–58. https://doi.org/10.1177/0265407517718387

Enders, C. K. (2010; chapters 3–5). Applied missing data analysis. New York, NY: Guilford.

Johnson, T. L., & Hancock, G. R. (2019). Time to criterion latent growth models. Psychological Methods, 24(6), 690–707. https://doi.org/10.1037/met0000214

McNeish, D. (2017). Small sample methods for multilevel modeling: A colloquial elucidation of REML and the Kenward-Roger correction. Multivariate Behavioral Research, 52(5), 661–670. https://doi.org/10.1080/00273171.2017.1344538

McNeish, D. (2020). Relaxing the proportionality assumption in latent basis models for nonlinear growth. Structural Equation Modeling, 27(5), 817–824. https://doi.org/10.1080/10705511.2019.1696201

McNeish, D., & Matta, T. (2018) Differentiating between mixed-effects and latent-curve approaches to growth modeling. Behavior Research Methods, 50, 1398–1414. https://doi.org/10.3758/s13428-017-0976-5

Preacher, K. J., & Hancock, G. R. (2015). Meaningful aspects of change as novel random coefficients: A general method for reparameterizing longitudinal models. Psychological Methods, 20(1), 84–101. https://doi.org/10.1037/met0000028

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309–338. https://doi.org/10.1037/met0000184

Rights, J. D., & Sterba, S. K. (2020). New recommendations on the use of R-squared differences in multilevel model comparisons. Multivariate Behavioral Research, 55(4), 568–599. https://doi.org/10.1080/00273171.2019.1660605

Stoel, R. D., Garre, F. G., Dolan, C., & van den Wittenboer, G. (2006). On the likelihood ratio test in structural equation modeling when parameters are subject to boundary constraints. Psychological Methods, 11(4), 439–455. https://doi.org/10.1037/1082-989X.11.4.439

Tuliao, A. P., Hoffman, L. , & McChargue, D. E. (2017). Measuring individual differences in responses to date-rape vignettes using latent variable models. Aggressive Behavior, 43(1), 60-73. https://doi.org/10.1002/ab.21662

Walters, R. W., & Hoffman, L. (2017). Applying the hierarchical linear model to longitudinal data / La aplicación del modelo lineal jerárquico a datos longitudinales, Cultura y Educación, 29(3), 666–701. https://doi.org/10.1080/11356405.2017.1367168

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. https://doi.org/10.1177%2F001316448904900309

Yuan, K.-H., Zhang, Z., & Deng, L. (2019). Fit indices for mean structures with growth curve models. Psychological Methods, 24(1), 36–53. https://doi.org/10.1037/met0000186

Academic Misconduct:

As a reminder, the University of Iowa College of Education has a formal policy on academic misconduct, which all students in this course are expected to follow. While students can work with each other to understand the course content, all homework assignments must ultimately be completed individually using the student-specific datasets provided for each assignment. Please consult the instructor if you have questions.

Respect for Each Other:

The instructor wants ALL students to feel welcome and encouraged to participate in this course. There is no such thing as a “stupid” question (or answer). All course participants—enrolled students and auditing visitors—should always feel welcome to ask whatever questions will be helpful in helping them understand the course content. Questions or comments are welcome at any point during class (aloud or using the zoom chat window), in office hours, over email, or in individual appointments with the instructor (available by request). Students with disabilities or who have any special needs are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.

All participants are welcome to attend class via zoom instead of in person for any reason at any time. If you do attend class in person, the University of Iowa encourages everyone to be vaccinated against COVID-19 and to wear a face mask in all classroom settings and during in-person office hours. If it possible that you have been exposed to COVID-19 or any other illness, please DO NOT attend class in person! Similarly, if the instructor has been exposed to illness or the weather prohibits safe travel to class, the course will move to a temporary zoom-only format to protect all course participants. When using zoom, please provide the name you wish for us to call you inside your zoom account (i.e., so that it appears on your window while in use). Student use of cameras and microphones while on zoom is also encouraged but not required (out of respect for your privacy and/or limited bandwidth). Please note that class video recordings posted to YouTube will NOT include any video from course participants (only the class audio and screen share from the instructor will be captured). Participants who do not wish for their audio to be captured can use the zoom chat window (which also allows for private direct messages to the instructor), even while attending in person.

The University of Iowa is committed to making the class environment (in person or online) a respectful and inclusive space for people of all gender, sexual, racial, religious, and other identities. Toward this goal, students are invited to optionally share the names and pronouns they would like their instructors and advisors to use to address them. The University of Iowa prohibits discrimination and harassment against individuals on the basis of race, class, gender, sexual orientation, national origin, and other identity categories. For more information, contact the Office of Institutional Equity. Additional university guidelines about classroom behavior and other student resources are provided here, student complaint procedures are provided here, and the university acknowledgement of land and sovereignty is here.

Respect for The Rest of Your World:

The instructor realizes that this course is not your only obligation in your work or your life. While class attendance in real time is not mandatory, it is strongly encouraged because frequent review of the material will be your best strategy for success in this course. However, if work or life events may compromise your ability to succeed, please contact the instructor for a confidential discussion so that we can work together to make a plan for your success. Please do not wait until you are too far behind to try to catch up!