Instructor Contact Information: 
Professor Lesa Hoffman (she/her—you can call me Lesa) Educational Measurement and Statistics Email: LesaHoffman@UIowa.edu (preferred contact) Office: 356 South LC (mostly unattended) Phone: 3193840522 (mostly unattended)  Home Department Information:  Psychological and Quantitative Foundations (PSQF) Office: South 361 Lindquist Center DEO: Dr. Megan Foley Nicpon 
Course Location and Time: 
166 North Lindquist Center or via zoom Tuesdays and Thursdays 12:30–1:45 PM 
ZoomOnly Office Hours:  Tuesdays and Thursdays 3:30–4:30 PM as a group or individually by appointment 
Zoom Link for Class and Office Hours:  no longer available  Lesa's resources:   Make Friends with SAS class (at UNL)  Manuals for SAS, SPSS, STATA, and Mplus at PilesOfVariance.com  For help using Virtual Desktop, SAS, STATA, or R, see the handout and videos posted at 2/7 for PSQF 6243 
Coursework Access: 
ICON for Formative Assessments UIowa Virtual Desktop Software Online Homework System no longer available For help getting started, see video for 1/24 
Program Documentation:   SAS: PROC GLM,
PROC GLIMMIX,
PROC LOGISTIC,
PROC GENMOD,
PROC FMM,
PROC MIXED,
PROC CALIS  STATA: REGRESS, OLOGIT, GOLOGIT2, GLM, NBREG, ZIP, ZINB, MEGLM, (BETABIN, ZIB, and ZIBBIN), MIXED, (SEM and GSEM)  R: TeachingDemos package, HAVEN package, EXPSS package, READXL package, LM, MULTCOMP package, GLM, PREDICTION package, VGLM (within VGAM package), GLM.NB (within MASS package), ZEROINFL (within PSCL package), GLMER (within LME4 package), GLS (within NLME package), RQ (within QUANTREG package), LAVAAN package 
Week 
Weekday 
Topics and Course Materials 
Readings and Other Resources for 

1  M: 1/17  NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
T: 1/18  MEET ON ZOOM ONLY Lecture 0: Introduction to this Course Lecture 0 Part 1 (slides 115): Video 

R: 1/20  MEET ON ZOOM ONLY Lecture 0, continued Lecture 0 Part 2 (slides 1427 and 31): Video Loglikelihood examples: (Excel) (SAS) 

2  M: 1/24  HW0 (for 2 points extra credit) DUE ONLINE BY 11:59 PM  Video: Intro to Homework 
T: 1/25  MEET ON ZOOM ONLY Lecture 0, continued Lecture0 Part 3 (slides 2738): Video Lecture 1 (updated 1/26) and Example 1: Review of General Linear Models Example 1 Files (.zip folder of data, syntax, and output, R updated 2/8/22) Lecture 1 Part 1 (slides 17): Video 
Agresti (2015) ch. 13 Hoffman (2015) ch. 2 Enders (2010) ch. 3 Darlington & Hayes (2016) ch. 10 Finsaas & Goldstein (2021) Johfre & Freese (2021) 

R: 1/27  OFFICE HOURS END AT 4 PM Lecture 1 and Example 1, continued Lecture 1 Part 2 (slides 721): Video 

3  M: 1/31  FA1 DUE VIA ICON BY 11:59 PM  
T: 2/1  Lecture 1 and Example 1, continued Lecture 1 and Example 1 (pages 14 predictor coding + maineffects model) Part 3: Video 

R: 2/3  MEET ON ZOOM ONLY Lecture 1 and Example 1, continued Lecture 1 and Example 1 (pages 49 maineffects model) Part 4: Video (recorded offline) Lecture 1 and Example 1 (pages 1016 age*grip model) Part 5: Video (recorded offline) 

4  M: 2/7  NO HW OR HA DUE  
T: 2/8  Lecture 1 and Example 1, continued Lecture 1 (slides 2127) and Example 1 (pages 1622 sex*demgroup model) Part 6: Video 

R: 2/10  Lecture 2: Models for Categorical Outcomes (updated 2/22/22) Example 2a: Models for Binary Outcomes Example 2ab Files (.zip folder of data, syntax, and output; R updated 2/22/22) Lecture 2 (slides 117) Part 1: Video 
Agresti (2015) ch. 4–5 H & H ch. 2, 9 Hsieh (1989) Mize (2019) Rohrer & Arslan (2021) 

5  M: 2/14  HW1 (based on Example 1) DUE ONLINE BY 11:59 PM  
T: 2/15  Lecture 2 and Example 2a, continued Lecture 2 (slides 2021) and Example 2a (pages 110) Part 2: Video 

R: 2/17  Lecture 2 and Example 2a, continued Lecture 2 (slides 1823) and Example 2a (pages 1118) Part 3: Video 

6  M: 2/21  FA2 DUE VIA ICON BY 11:59 PM  
T: 2/22  MEET ON ZOOM ONLY Lecture 2, continued (IRT slides) Lecture 2 (slides 3135 for IRT) Part 4: Video Lecture 2 (slides 2428 for categorical outcomes) Part 5: Video Example 2b: Models for Ordinal and Nominal Outcomes Example 2ab Files (.zip folder of data, syntax, and output) 
Agresti (2015) ch. 6 H & H ch. 15, 16 Bürkner & Vuorre (2019) Williams (2016) 

R: 2/24  Lecture 2 and Example 2b, continued Example 2b (pages 19) Part 1: Video 

7  M: 2/28  HW2 (based on Example 2a) DUE ONLINE BY 11:59 PM  
T: 3/1  Lecture 2 and Example 2b, continued Example 2b (pages 1017) Part 2: Video 

R: 3/3  Lecture 2 and Example 2b, continued Example 2b (pages 1826) Part 3: Video Lecture 3 (notation updated 3/15/22) and Example 3: Models for Count Outcomes Example 3 Files (.zip folder of syntax and output) Lecture 3 (slides 19) Part 1: Video 
Agresti (2015) ch. 7 H & H ch. 12–14 Green (2021) McGinley et al. (2015) 

8  M: 3/7  FA3 DUE VIA ICON BY 11:59 PM  
T: 3/8  Lecture 3 and Example 3, continued Lecture 3 (slides 112) and Example 3 (pages 17) Part 2: Video 

R: 3/10  Lecture 3 and Example 3, continued Lecture 3 (slides 89) and Example 3 (pages 414) Part 3: Video 

9  M: 3/14  NO HW OR HA DUE  
T: 3/15  NO CLASS OR OFFICE HOURS  
R: 3/17  NO CLASS OR OFFICE HOURS  
10  M: 3/21  NO HW OR HA DUE  
T: 3/22  Lecture 3 and Example 3, continued Lecture 3 (slides 517) and Example 3 (pages 1522) Part 4: Video 

R: 3/24  MEET ON ZOOM ONLY Lecture 4: Models for Other NonNormal Outcomes Example 4a: Models for Proportion (Binomial) Outcomes Example 4a Files (.zip folder of syntax and output) Lecture 4 (slides 19) and Example 4a (pages 17) Part 1: Video 
Agresti (2015) ch. 8 H & H ch. 10–11 Hardin & Hilbe (2014) 

11  M: 3/28  HW3 (based on Example 2b) DUE ONLINE BY 11:59 PM  
T: 3/29  Lecture 4 and Example 4a, continued Lecture 4 (review of slides 49) and Example 4a (pages 111) Part 2: Video 

R: 3/31  Lecture 4 and Example 4a, continued Example 4b: Models for Skewed Continuous Outcomes Example 4b Files (.zip folder of data, syntax, and output) Example 4a (pages 917), Lecture 4 (slides 1016), and Example 4b (pages 110) Part 3: Video 
H & H ch. 6 Knief & Forstmeier (2021) Konstantopoulos et al. (2019) 

12  M: 4/4  FA4 DUE VIA ICON BY 11:59 PM  
T: 4/5  Lecture 4, continued Discussion of HW4, FA4, and Lecture 4 (slides 1720) Part 4: Video 

R: 4/7  Example 4b, continued Example 4b (pages 1117) Part 5: Video Lecture 5: Multivariate Models via Univariate Software Lecture 5 (slides 14) Part 1: Video 
Agresti (2015) ch. 9 H & H ch. 18–19 Kumle et al. (2021) 

13  M: 4/11  HW4 (based on Example 3) DUE ONLINE BY 11:59 PM  
T: 4/12  Lecture 5, continued Lecture 5 (slides 120) Part 2: Video 

R: 4/14  Lecture 5, continued Example 5a Part 1: Models for Triadic (Family) Outcomes Example 5a Files (.zip folder of syntax and output for Part 1 and Part 2, Stata updated 4/27/22) Lecture 5 (slides 16, 2739) and Example 5a (pages 14) Part 3: Video 

14  M: 4/18  FA5 DUE VIA ICON BY 11:59 PM  
T: 4/19  Lecture 5 and Example 5a Part 1, continued Example 5a (pages 19) Part 4: Video 

R: 4/21  Lecture 5 and Example 5a Part 1, continued Example 5a (pages 814) and Lecture 5 (slides 1526) Part 5: Video Example 5b Part 1: Models for Difference Score Outcomes  see Example 5a from previous class version Example 5c: Models for Repeated Measures Outcomes  see Example 4a from previous class version 
Hoffman (2015) ch. 3 

15  M: 4/25  HW5 (based on Example 5a Part 1) DUE ONLINE !!!! WED 4/27 !!!!! BY 11:59 PM  
T: 4/26  Lecture 6: Multivariate Models via Path Analysis Lecture 6 (slides 117) Part 1: Video 
Enders (2010) ch. 4–5 MacKinnon (2008) ch. 6 

R: 4/28  Lecture 6, continued Example 5a Part 2 (using materials posted 4/14/22) Lecture 6 (slides 1330) and Example 5a (pages 1518) Part 2: Video 

16  M: 5/2  FA6 DUE VIA ICON BY 11:59 PM  
T: 5/3  Lecture 6 and Example 5a Part 2, continued Example 5a (pages 1526) and Lecture 6 (slide 34) Part 3: Video 

R: 5/5  Lecture 6, continued Example 6a: Path Models for Mediation with Normal Outcomes Example 6a Files (.zip folder of syntax and output) Example 6b: Path Models for Mediation with Binary Outcomes Example 6b Files (.zip folder of syntax and output) Lecture 6 (slides 4043), Example 6a (all), Example 6b (all): Video 

17  T: 5/10  NO CLASS, but office hours from 12:304:30 PM  
R: 5/12  NO CLASS, but office hours from 12:304:30 PM 

F: 5/13  HW6 (based on Example 6a) DUE BY 5:00 PM ONLINE ALL OUTSTANDING WORK MUST BE COMPLETED BY 5:00 PM 
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).
This course will focus on the uses of generalized linear models for predicting univariate and multivariate outcomes. The course objective is for participants to be able to complete all the necessary steps in a generalized linear model analysis: deciding which type of model is appropriate, creating predictor variables, building models to evaluate unique effects of predictors, 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 groupbased office hours (via zoom only), in which multiple participants can receive immediate assistance on homework assignments simultaneously.
Participants will have the opportunity to earn up to 100 total points in this course by completing work outside of class. Up to 88 points can be earned from submitting homework assignments (approximately 6 in total) through a custom online system—these will be graded for accuracy. Up to 12 points may be earned from submitting formative assessments (approximately 6 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.
Participants may submit work at any point during the semester to be counted towards their course grade. However, in order to provide participants with prompt feedback, late homework assignments will incur a 1point penalty, and late formative assessments will incur a 0.5point 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, May 13, 2021 at 5:00 PM to be included in the course grade.
>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
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:
 You can connect to the U Iowa Virtual Desktop (connect to the U Iowa VPN first) for free
 You can connect to the U Iowa Research Remote Desktop (connect to the U Iowa VPN first) for free
 You can install R software for free on your local machine, along with the free graphical Rstudio interface that makes R easier to use (install second after R software)
 You can connect to the webbased SAS OnDemand platform for free on your local machine
 You could also pay $48 to install a 6month student copy of STATA on your local machine
(H & H): Hardin, J. W. & Hilbe, J. M. (2018). Generalized linear models and extensions (4th ed.). STATA Press. Available from the U of Iowa library as an ebook (for one user at a time).
Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. Guilford.
Available from the U Iowa library as an ebook (for multiple users at the same time).
Note—I know this is A LOT of readings, but we are covering a lot of material! I have included these sources to give you some additional tutorials and examples that will be easier to read than the two textbooks. I encourage you to read as much as possible, but your priority should be to participate in class and complete course work first!
Agresti, A. (2015). Foundations of linear and generalized linear models (1st ed.). Wiley & Sons.
Bürkner, P.C., & Vuorre, M. (2019). Ordinal regression models in psychology: A tutorial. Advances in Methods and Practices in Psychological Science, 2(1), 77–101.
Enders, C. K. (2010; chapters 3–5). Applied missing data analysis. Guilford.
Finsaas, M. G., & Goldstein, B. L. (2021). Do simple slopes followup tests lead us astray? Advancements in the visualization and reporting of interactions. Psychological Methods, 26(1), 38–60.
Green, J. A. (2021). Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression. Health Psychology and Behavioral Medicine, 9(1), 436–455.
Hardin, J. W., & Hilbe, J. M. (2014). Estimation and testing of binomial and betabinomial regression models with and without zero inflation. The Stata Journal, 14(2), 292–303.
Hoffman, L. (2015 chapters 2–3). Longitudinal analysis: Modeling withinperson fluctuation and change. Routledge / Taylor & Francis.
Hsieh, F. Y. (1989). Sample size tables for logistic regression. Statistics in Medicine, 8, 795–802.
Johfre, S. S., & Freese, J. (2021). Reconsidering the reference category. Sociological Methodology, 51(2), 235–269.
Knief, U., & Forstmeier, W. (2021). Violating the normality assumption may be the lesser of two evils. Behavior Research Methods, 53, 2576–2590.
Konstantopoulos, S., Li, W., Miller, S., & van der Ploeg, A. (2019). Using quantile regression to estimate intervention effects beyond the mean. Educational and Psychological Measurement, 79(5), 883–910.
Kumle L., Võ, M. L.H., & Draschkow, D. (2021). Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R. Behavior Research Methods, 53, 2528–2573.
MacKinnon, D. P. (2008 chapter 6). Introduction to statistical mediation analysis. Routledge / Taylor & Francis.
McGinley, J. S., Curran, P. J., & Hedeker, D. (2015). A novel modeling framework for ordinal data defined by collapsed counts. Statistics in Medicine, 34, 2312–2324.
Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science 6, 81–117.
Rohrer, J. M., Arslan, R. C. (2021). Precise answers to vague questions: Issues with interactions. Advances in Methods and Practices in Psychological Science, 4(2), 1–19.
Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40, 7–20.
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 assignment must be completed individually using the studentspecific datasets provided for each assignment. Please consult the instructor if you have questions.
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 strongly encourages everyone to be vaccinated against COVID19 and to wear a face mask in all classroom settings and during inperson office hours. If it possible that you have been exposed to COVID19 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 zoomonly 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 streamed 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).
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, and the university acknowledgement of land and sovereignty is here.
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!