Lesa's course directory

Previous version of this course (Fall 2023)

Instructor Contact Information:

Professor Lesa Hoffman
(she/her—you can call me Lesa)

Educational Measurement and Statistics
Email: Lesa-Hoffman@UIowa.edu (preferred mode of contact)
Office: 356 South LC (usually unattended)
Home Department Information: Psychological and Quantitative Foundations (PSQF)
Office: South 361 Lindquist Center
DEO: Dr. Martin Kivlighan
Course Room
and Time:

Instructor
Office Hours:


Zoom Link
for Instructor
Office Hours:
166 North Lindquist Center (LC)
Tuesdays and Thursdays 12:30–1:45 PM

Mondays 12:00-1:30 PM and Wednesdays 3:00–4:30 PM on zoom in a group format or individually by appointment

https://uiowa.zoom.us/my/lesahoffmaniowa
Meeting ID: 504 435 6512; Mobile Access: +13126266799
(please use your real name as your account name to be admitted)
Graduate Teaching Assistants' Contact Information and
Office Hours:

Erica Dorman (she/her)
PhD student in Educational Measurement and Statistics in PSQF
Email: Erica-Dorman@UIowa.edu
Office hours in a group format Mondays 9:30–11:00 AM and Tuesdays 10:00–11:30 AM via zoom: https://uiowa.zoom.us/my/ericadorman

Geraldo “Bladimir” Padilla (he/him)
PhD student in Educational Measurement and Statistics in PSQF
Email: Geraldo-Padilla@UIowa.edu
Office hours in a hybrid group format Thursdays and Fridays 9:00–10:30 AM in N476 LC or via zoom: https://uiowa.zoom.us/my/bladimirpadilla

Summary of
Office Hours:
Monday on zoom: Erica 9:30-11:00; Lesa 12:00-1:30
Tuesday on zoom: Erica 10:00-11:30
Wednesday on zoom: Lesa 3:00-4:30
Thursday hybrid: Bladimir 9:00-10:30 N476 or zoom
Friday hybrid: Bladimir 9:00-10:30 N476 or zoom
Software
Resources:
Video introduction to online homework (not yet available)

For help getting started with the U Iowa Virtual Desktop, STATA, or R, see the materials posted for 9/16 in PSQF 6243 from Fall 2024

Manuals and examples for SAS, SPSS, STATA, and Mplus at PilesOfVariance.com
Coursework
Access:
ICON for Formative Assessments

U Iowa Virtual Desktop Software

Online Homework System (now available!)
Software Documentation: SAS: PROC MIXED Online Manual

STATA: MIXED Online Manual

R: TeachingDemos package, READXL package, LM, MULTCOMP package, GLS and LME (within NLME package), ICC (within performance package), LMER (within LME4 package), LMERTEST package

Planned Schedule of Events (Printable Syllabus; last updated 8/25/2025)

Week Number

Weekday and Date

Topics and Course Materials

Readings for Each Unit
(ordered by priority)

1 M: 8/25 NO OFFICE HOURS
NO HOMEWORK (HW) OR FORMATIVE ASSESSMENT (FA) DUE
 
T: 8/26 Lecture 1: Introduction to this Course and to Multilevel Models (MLMs) for Clustered Data
Video Part 1: Lecture 1 slides 1-16
S & B (2012) ch. 1–2
Hoffman & Walters (2022)
R: 8/28 Lecture 1, continued
Video Part 2: Lecture 1 slides 15-24

Lecture 2 and Example 2: From Empty Models to Level-2 Predictors
Example 2 Files (.zip folder of data, syntax, and output)
Video Part 1: Lecture 2 slides 1-7
S & B (2012) ch. 3–4
McNeish (2017)
       
2 M: 9/1 NO OFFICE HOURS
HW0 (online, for 1 point extra credit over the syllabus) DUE BY 11:59 PM
Video: Demo of HW0
T: 9/2 Lecture 2 and Example 2, continued  
R: 9/4 Lecture 2 and Example 2, continued  
       
3 M: 9/8 FA1 (Quiz in ICON) DUE BY 11:59 PM  
T: 9/9 Discussion of FA1
Lecture 2 and Example 2, continued
 
R: 9/11 MEET ON ZOOM ONLY
Lecture 3 and Example 3: Fixed Slopes of Level-1 Predictors
Example 3 Files (.zip folder of data, syntax, and output)
Rights & Sterba (2019, 2020)
Yaremych et al. (2023)
       
4 M: 9/15 NOTHING DUE TODAY  
T: 9/16 Lecture 3 and Example 3, continued Hamaker & Muthén (2020)
McNeish (2023)
R: 9/18 Lecture 3 and Example 3, continued
 
       
5 M: 9/22 HW1 (online, based on Example 2) DUE BY 11:59 PM  
T: 9/23 Lecture 3 and Example 3, continued  
R: 9/25 Lecture 3 and Example 3, continued  
       
6 M: 9/29 FA2 (Quiz in ICON) DUE BY 11:59 PM  
T: 9/30 Discussion of FA2

Lecture 4 and Example 4: Random Slopes and Cross-Level Interactions
Example 4 Files (.zip folder of syntax and output -- no data)
S & B (2012) ch. 5–6
Rights & Sterba (2023)
Enders (2010) ch. 3–5
Stoel et al. (2006)
R: 10/2 Lecture 4 and Example 4, continued  
       
7 M: 10/6 HW2 (online, based on Example 3) DUE BY 11:59 PM  
T: 10/7 Lecture 4 and Example 4, continued  
R: 10/9 Lecture 4 and Example 4, continued  
       
8 M: 10/13 PROJECT OUTLINE (Assignment in ICON) DUE BY 11:59 PM  
T: 10/14 Lecture 4 and Example 4, continued  
R: 10/16 Lecture 4 and Example 4, continued  
       
9 M: 10/20 NO LESA OFFICE HOURS THIS WEEK
NOTHING DUE TODAY
 
T: 10/21 NO CLASS  
R: 10/23 NO CLASS  
       
10 M: 10/27 FA3 (Quiz in ICON) DUE BY 11:59 PM  
T: 10/28 Discussion of FA3
Lecture 4 and Example 4, continued

R: 10/30 Lecture 4 and Example 4, continued  
       
11 M: 11/3 FA4 (Quiz in ICON) DUE BY 11:59 PM
 
T: 11/4 Discussion of FA4

Lecture 5: Two-Level Cross-Classified Designs
Example 5a: Crossed Primary and Secondary Schools
Example 5a Files (.zip folder of data, syntax, and output)

S & B (2012) ch. 13
Guo et al. (2024)
O'Keefe & Rodgers (2017)
R: 11/6 Lecture 5 and Example 5a, continued
 
       
12 M: 11/10 HW3 (online, based on Example 4) DUE BY 11:59 PM  
T: 11/11 Lecture 5, continued
Example 5b: Subjects Crossed with Items
Example 5b Files (.zip folder of data, syntax, and output)
Hoffman (2015) ch. 12
ten Hove et al. (2022)
R: 11/13 Example 5b, continued  
       
13 M: 11/17 PROJECT CHECK-INS (Assignment in ICON) DUE BY 11:59 PM  
T: 11/18 Project Check-in Discussion
 
R: 11/20 NO CLASS  
       
14 M: 11/24 NO OFFICE HOURS; NOTHING DUE TODAY  
T: 11/25 NO CLASS OR OFFICE HOURS THIS WEEK  
R: 11/27 NO CLASS OR OFFICE HOURS THIS WEEK  
       
15 M: 12/1 FA5 (Quiz in ICON) DUE BY 11:59 PM  
T: 12/2 Discussion of FA5
TBD or Group 1 Presentations
 
R: 12/4 Group 1 Student Presentations
 
       
16 M: 12/8 NOTHING DUE TODAY  
T: 12/9 Group 2 Student Presentations
 
R: 12/11 GROUP 1 PEER REVIEWS (via email) AND
FA6 (Quiz in ICON) DUE BY 11:59 PM


Discussion of FA6 and Wrap-Up

Arend & Schäfer (2019)
Hoffman (2015) ch. 13
       
17 M: 12/15 LESA OFFICE HOURS 12:00–1:30 PM
GROUP 2 PEER REVIEWS (via email) DUE BY 11:59 PM
 
T: 12/16 NO CLASS; LESA OFFICE HOURS 12:30–2:30 PM  
W: 12/17 LESA OFFICE HOURS 3:00–4:30 PM
PRESENTATION REVISIONS DUE BY 11:59 PM
ALL OUTSTANDING WORK DUE BY 11:59 PM
 
R: 12/18 NO CLASS OR OFFICE HOURS  

Schedule of Topics and Events:

This course will meet in person except for specific sessions to be held on zoom when the classroom is unavailable or as needed otherwise. 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 course materials.

Course Objectives, Prerequisites, 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 clustered data (i.e., persons nested in natural groups). The course objective is for participants to be able to complete all the necessary steps in a multilevel analysis: deciding which type of model is appropriate, organizing the analysis data and creating predictor variables, testing fixed and random effects, predicting multiple sources of variation, and interpreting and presenting empirical findings. Participants should already be comfortable with general linear models (e.g., regression, ANOVA), which can be reviewed using the materials from PSQF 6243 from Fall 2024.

Class time will be devoted primarily to lectures, examples, and reviews, the materials for which will be available for download at the course website. Readings and other resources have been suggested for each unit and may be updated later. Regular attendance is strongly encouraged but is not required, and you do not need to notify the instructor of a single class absence. Video recordings of each class will be made 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 given 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 with the instructor or the TAs (first-come; first-served), in which multiple participants can receive assistance on course activities and content. Finally, the instructor will provide announcements and reminders via email, with larger changes also announced through ICON. Student emails will be answered within 1–2 business days barring unforseen circumstances.

Course Requirements:

Course participants will have the opportunity to earn up to 100 total points by completing course work as follows. Up to 48 points can be earned from submitting homework assignments (HW; 3 planned initially) through a custom online system—these will be graded for accuracy. Up to 34 points can be earned by conducting a project (either individually or in pairs), which will involve planning and conducting data analyses to be shared in a conference-style presentation to the class along with a peer review process. Presentations can be revised once to earn the maximum total points. More details about the project structure, allowable content, and presentation day assignments will be given later. Up to 18 points may be earned from formative assessments (FA; 6 planned initially) through ICON; for each you will receive 3 points for effort only—incorrect answers will not be penalized. Participants may earn up to 1 point of extra credit for completing 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. If that happens, this syllabus will be updated to reflect the new point totals.

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 HW assignments or project check-ins will each incur a 3-point penalty; late FAs, project outlines, or peer reviews will incur a 1-point penalty (overall, not per day). Presentations not given as scheduled will incur a 10-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 11:59 PM on Wednesday, December 17, 2025, 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—STATA or R+RStudio—that can estimate the models presented. Each of these programs are freely available to course participants in multiple ways:

Recommended Course Textbook
(to be purchased or accessed in person at the main library course reserves):

S & B: Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.

Other Course Readings (available via "Files" in ICON):

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

Enders, C. K. (2010; chapters 3–5). Applied missing data analysis (1st ed). Guilford.

Guo, Y., Dhaliwal, J., & Rights, J. D. (2024). Disaggregating level-specific effects in cross- classified multilevel models. Behavior Research Methods, 56, 3023–3057. https://doi.org/10.3758/s13428-023-02238-7

Hamaker, E. L., & Muthén, B. (2020). The fixed versus random effects debate and how it relates to centering in multilevel modeling. Psychological Methods, 25(3), 365–379. https://doi.org/10.1037/met0000239

Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge/Taylor & Francis.

Hoffman, L., & Walters, R. W. (2022). Catching up on multilevel modeling. Annual Review of Psychology, 73, 629-658. https://doi.org/10.1146/annurev-psych-020821-103525

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. (2023). A practical guide to selecting and blending approaches for clustered data: Clustered errors, multilevel models, and fixed-effect models. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000620

O'Keefe, P., & Rodgers, J. L. (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. https://doi.org/10.1080/00273171.2017.1354758

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

Rights, J, D., & Sterba, S. K. (2023). On the common but problematic specification of conflated random slopes in multilevel models. Multivariate Behavioral Research, 58(6), 1106–1133. https://doi.org/10.1080/00273171.2023.2174490

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

ten Hove, D., Jorgensen, T. D., & van der Ark, L. A. (2022). Interrater reliability for multilevel data: A generalizability theory approach. Psychological Methods, 27(4), 650–666. https://doi.org/10.1037/met0000391

Yaremych, H. E., Preacher, K. J., & Hedeker, D. (2023). Centering categorical predictors in multilevel models: Best practices and interpretation. Psychological Methods, 28(3), 613–630. http://dx.doi.org/10.1037/met0000434

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 course activities must ultimately be completed individually (or in pairs for the project). Please consult the instructor if you have any questions.

The use of ChatGPT or any other Artificial Intelligence (AI) should not be needed (or helpful), as the course materials will provide examples of all software code needed to complete homework assignments. Similarly, the use of AI in completing formative assessments (FAs) will defeat their purpose, as these structured reviews are designed to help participants recognize remaining sources of confusion or inexperience (and FA points will be given regardless, so long as there is some effort made in trying to answer each question). Any uncredited use of AI will be treated as academic misconduct. Acceptable uses of AI are limited to grammatical and proof-reading advice (and should be credited when applicable).

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, in office hours, over email, or in individual appointments with the instructor (available by request). Students with special needs are encouraged to contact the instructor for a confidential discussion of their individual considerations for academic accommodation.

If you are seriously ill, 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. The instructor intends to record every class session so that participants can catch up or review on their own as needed. Please note that class video recordings posted on YouTube will NOT include any video from course participants (only the class audio and screen share from the instructor will be captured).

The University of Iowa is committed to making the class environment 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 Civil Rights Compliance. Additional university guidelines about classroom behavior and other student resources are provided here and student complaint procedures are provided 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 in this course, 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!