Lesa's course directory

Previous version of this course (Spring 2023)

Instructor 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: Dr. Martin Kivlighan
Course Location
and Time:
166 North Lindquist Center (LC) or via zoom
Tuesdays and Thursdays 2:00–3:15 PM
Instructor Office Hours: Mondays and Wednesdays 3:00–4:30 PM in an online group format via zoom
(first-come, first-serve) or individually by appointment
Zoom Link for Class and Office Hours: https://uiowa.zoom.us/my/lesahoffmaniowa
Meeting ID: 5044356512; Mobile Access: +13126266799
(please use your real name as your account name to be admitted)
Graduate Teaching Assistant Contact Information and
Office Hours:

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: Tuesdays and Thursdays 9:00-11:59 AM in N476 LC or via zoom: https://uiowa.zoom.us/j/7961502515

Nicole "Nikki" Tennessen (she/her)
PhD candidate in Higher Education and Student Affairs in EPLS and PhD student in Educational Measurement and Statistics in PSQF
Email: Nicole-Tennessen@UIowa.edu
Office hours in an online group format: Mondays 8:30-10:00 AM and Fridays 10:00-11:30 AM on zoom: https://uiowa.zoom.us/j/96225611925?pwd=NEtnYmlOcHhhUEJCMUpBTUNYNTV6dz09

Coursework
Access:
ICON for Formative Assessments

UIowa Virtual Desktop Software

Online Homework System (now available!)

For help getting started with the online homework system, please watch this video
Program
Documentation:
- 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

- STATA: REGRESS, OLOGIT, GOLOGIT2, GLM, NBREG, ZIP, ZINB, MEGLM, (BETABIN, ZIB, and ZIBBIN), MIXED, (SEM and GSEM)

- R Packages: TeachingDemos, HAVEN, EXPSS, READXL, LM, MULTCOMP, GLM, PREDICTION, DESCTOOLS, 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

Planned Schedule of Events (Printable Syllabus last updated 2/17/24)

Week
Number

Weekday
and Date

Topics and Course Materials

Readings and Other Resources for Each Topic

1 M: 1/15 NO OFFICE HOURS
NO HOMEWORK (HW) OR FORMATIVE ASSESSMENT (FA) DUE
 
T: 1/16 MEET ON ZOOM ONLY
Lecture 0: Introduction to this Course on Generalized Linear Models
Video Part 1: Lecture 0 (slides 1-25)
 
R: 1/18 NO BLADIMIR OFFICE HOURS TODAY
Lecture 0, continued
Log-likelihood examples: (Excel) (SAS)
Video Part 2: Lecture 0 (slides 23-33)

Lecture 1 and Example 1: Review of General Linear Models
Example 1 Files (.zip folder of data, syntax, and output)
Video Part 1: Lecture 1 (slides 1-5)
Agresti (2015) ch. 1–3
Hoffman (2015) ch. 2
Enders (2010) ch. 3
Darlington & Hayes (2016) ch. 10
Finsaas & Goldstein (2021)
Johfre & Freese (2021)
       
2 M: 1/22 NO HW OR HA DUE  
T: 1/23 MEET ON ZOOM ONLY
Lecture 1 and Example 1, continued
Video Part 2: Lecture 1 (slides 4-17)
 
R: 1/25 NO BLADIMIR OFFICE HOURS TODAY
Lecture 1 and Example 1, continued
Lecture 1 Bonus: More on Interaction Terms (completed in-class version)
Video Part 3: Lecture 1 (slides 18-23) and Lecture 1 bonus (all)
 
       
3 M: 1/29 HW0 (2 points extra credit) DUE ONLINE BY 11:59 PM Video: Intro to Online Homework
T: 1/30 Lecture 1 and Example 1, continued
Video Part 4: Example 1 (pages 1-6)
 
R: 2/1 Lecture 1 and Example 1, continued
Video Part 5: Example 1 (pages 6-14) and Lecture 1 (slides 22-28)
 
       
4 M: 2/5 FA1 DUE VIA ICON BY 11:59 PM  
T: 2/6 Discussion of FA1 (finished version)
Lecture 1 and Example 1, continued
Spreadsheet for In-Class Examples (finished version)
Video Part 6: Discussion of FA1; In-class interaction examples
 
R: 2/8 Lecture 2: Models for Categorical Outcomes
Example 2a: Models for Binary Outcomes
Example 2ab Files (.zip folder of data, syntax, and output)
Video Part 1: Discussion of HW1; Lecture 2 slides 1-16 and Example 2a (pages 1-7)
Agresti (2015) ch. 4–5
H & H ch. 2, 9
Hsieh (1989); Mize (2019)
Rohrer & Arslan (2021)
       
5 M: 2/12 HW1 (based on Example 1) DUE ONLINE BY 11:59 PM  
T: 2/13 GUEST LECTURE BY JONATHAN TEMPLIN (CHECK EMAIL FOR ZOOM LINK)
Lecture 2 and Example 2a, continued
Video Part 2: Lecture 2 slides 1-15
 
R: 2/15 Lecture 2 and Example 2a, continued
Video Part 3: Lecture 2 (slides 14-23, 29-33) and Example 2a (pages 3-7)
 
       
6 M: 2/19 FA2 DUE VIA ICON BY 11:59 PM  
T: 2/20 Discussion of FA2; Lecture 2 and Example 2a, continued
Video Part 4: Discussion of FA2 and Example 2a page 3 and 5-11
 
R: 2/22 Example 2b: Models for Ordinal and Nominal Outcomes
(Example 2ab Files as posted for Example 2a))
Video Part 5: Lecture 2 slides 24-28 and Example 2b pages 1-5
Agresti (2015) ch. 6
H & H ch. 15, 16
Bürkner & Vuorre (2019)
Williams (2016)
       
7 M: 2/26 HW2 (based on Example 2a) NOW DUE ONLINE !!! WED 2/28 !!! BY 11:59 PM  
T: 2/27 Lecture 2 and Example 2b, continued
Video Part 6: Lecture 2 (slide 25) and Example 2b (pages 2-10)
 
R: 2/29 Example 2b, continued
Demonstration of Logistic Regression (by Bladimir Padilla, updated 3/3/2024)
Video Part 7: Lecture 2 (slides 25 and 28) and Example 2b (pages 11-16); Demonstration of Logistic Regression (Part 1)
 
       
8 M: 3/4 FA3 DUE VIA ICON BY 11:59 PM  
T: 3/5 Discussion of FA3
Demonstration of Logistic Regression, continued
Demonstration of Ordinal Regression (by Nikki Tennessen, updated 3/7/24)

Video Part 8: Discussion of FA3, Demonstration of Logistic Regression (Part 2), and Demonstration of Ordinal Regression (Part 1)

Spreadsheet for LRT Model Comparisons
 
R: 3/7 BLADIMIR OFFICE HOURS START AT 10 INSTEAD
Video Part 9: Demonstration of Ordinal Regression (Part 2)

Lecture 3: Models for Count Outcomes
Video Part 1: Lecture 3 (slides 1-9)
Agresti (2015) ch. 7
H & H ch. 12–14
Green (2021)
McGinley et al. (2015)
McCabe et al. (2022)
       
9 M: 3/11 NO HW OR HA DUE  
T: 3/12 NO CLASS OR OFFICE HOURS THIS WEEK  
R: 3/14 NO CLASS OR OFFICE HOURS THIS WEEK  
       
10 M: 3/18 FA4 DUE VIA ICON BY 11:59 PM  
T: 3/19 MEET ON ZOOM ONLY
Discussion of FA4; Lecture 3, continued
Example 3: Models for Count Outcomes
Example 3 Files (.zip folder of syntax and output, but no data)
Video Part 2: Discussion of FA4; Example 3 (pages 1-6)
 
R: 3/21 Lecture 3 and Example 3, continued
Video Part 3: Lecture 3 (slides 10-17) and Example 3 (pages 6-13)
 
       
11 M: 3/25 HW3 (based on Example 2b) DUE ONLINE BY 11:59 PM  
T: 3/26 NO CLASS TODAY  
R: 3/28 MEET ON ZOOM ONLY
Lecture 3 and Example 3, continued
Video Part 4: Lecture 3 (slides 14-21) and Example 3 (pages 11-15)

Lecture 4: Models for Other Non-Normal Outcomes
Example 4a: Models for Outcomes with Ceiling or Floor Effects
Example 4a Files (.zip folder of syntax and output, but no data)
Video Part 1: Lecture 4 (slides 1-9) and Example 4 (pages 1-8)



Agresti (2015) ch. 8
H & H ch. 10–11
Hardin & Hilbe (2014)
Certo et al. (2020)
Long (1997) ch. 7
       
12 M: 4/1 HW5 PLAN DUE VIA ICON BY 11:59 PM: Download plan template here  
T: 4/2 Lecture 4 and Example 4a, continued
HW4 in-class help
Video Part 2: HW4 help, Lecture 4 (slides 4-5 and 9-17), and Example 4a (pages 1-13)
 
R: 4/4 Lecture 4, continued
Example 4b: Models for Skewed Continuous Outcomes
Example 4b Files (.zip folder of data, syntax, and output)
In-class review taxonomy spreadsheet
Video Part 3: Review of models covered, Lecture 4 (slides 17-20), and Example 4b (all)
H & H ch. 6
Knief & Forstmeier (2021)
Konstantopoulos et al. (2019)
       
13 M: 4/8 HW4 (based on Example 3) DUE ONLINE BY 11:59 PM  
T: 4/9 HW5 PLAN REVISIONS DUE VIA ICON !!! WED APRIL 10 !!! BY 11:59 PM

Lecture 5: Multivariate Models via Univariate Software
Video Part 1: Lecture 5 (slides 1-19, 27-34)
Agresti (2015) ch. 9
H & H ch. 18–19
Kumle et al. (2021)
R: 4/11 NO NIKKI OFFICE HOURS FRIDAY APRIL 12
Lecture 5, continued
Example 5 Part 1: Models for Family (Triadic) Outcomes
Example 5 Files (.zip folder of syntax and output, but no data)
Bonus Coding Example
Video Part 2: Lecture 5 (slides 30-39), Bonus Coding Example (all), Example 5 (pages 1-4)

       
14 M: 4/15 FA5 DUE VIA ICON BY 11:59 PM  
T: 4/16 Discussion of FA5;
Lecture 5 and Example 5, continued
 
R: 4/18 Lecture 5 and Example 5, continued

Bonus: Models for Repeated Measures Outcomes -- see Example 4a from 2020 class
Bonus: Models for Difference Score Outcomes -- see Example 5a from 2020 class

 

Hoffman (2015) ch. 3

       
15 M: 4/22 HW5 USING OWN DATA DUE VIA ICON BY 11:59 PM: Download instructions here  
T: 4/23 Lecture 6: Multivariate Models via Path Analysis
Enders (2010) ch. 4–5
Gonzales et al. (2023)
R: 4/25 Lecture 6, continued
Example 5 Part 2
 
       
16 M: 4/29 FA6 DUE VIA ICON BY 11:59 PM  
T: 4/30 Discussion of FA6; Lecture 6 and Example 5 Part 2, continued
Example 6a: Path Models for Mediation with Normal Outcomes
Example 6a Files (.zip folder of syntax and output, but no data)
 
R: 5/2 Lecture 6 and Example 6a, continued
Example 6b: Path Models for Mediation with Binary Outcomes
Example 6b Files (.zip folder of syntax and output)
Example 6c: Path Models for Mediation with Nominal Outcomes
 
       
17 M: 5/6 Office hours from 3:00-4:30 PM  
T: 5/7 NO CLASS, but office hours from 12:30-3:30 PM  
W: 5/8 Office hours from 3:00-4:30 PM  
R: 5/9 NO CLASS, but office hours from 12:30-3:30 PM  
F: 5/10 HW6 DUE BY 5:00 PM ONLINE: Practice with Path Models
OPTIONAL REVISION TO HW5 DUE VIA ICON BY 5:00 PM
ALL OUTSTANDING WORK MUST BE COMPLETED BY 5:00 PM
 

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, Prerequisites, and Materials:

This course will illustrate 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), such as is covered in PSQF 6243.

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 simultaneously or sequentially.

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 88 points can be earned from submitting homework assignments (6 initially planned) through a custom online system or ICON as noted—these will be graded for accuracy. Homework assignments that involve individual 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. Unless otherwise instructed, please use “track changes” and retain all original instructor comments so that the instructor can easily see how your revisions address the comments.

Up to 12 points may be earned from submitting formative assessments (6 initially planned) through ICON; these will be graded for effort only—incorrect answers will not be penalized. Participants may 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 grade. However, to encourage participants to keep up with the class, late homework assignments will incur a 2-point penalty; late HW plans, HW written revisions, or formative assessments will incur a 1-point penalty (overall, not per day). 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 10, 2024 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—STATA, or R+Rstudio—that can estimate the models presented. Each of these programs is freely available to course participants in multiple ways:

SAS may also be used for specific examples throughout the course. The last unit of the course on path analysis will also use Mplus software. Both of these are freely available on the U Iowa Virtual Desktop.

Course Textbook:

(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 e-book (for one user at a time).

Recommended Textbook for Background on General Linear Models (as needed for review):

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 e-book (for multiple users at the same time).

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 have included these sources to give you some additional tutorials and examples. I encourage you to read as many of these sources 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. 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. https://doi.org/10.1177/2515245918823199

Certo, S. T., Busenbark, J. R., Kalm, M., & LePine, J. A. (2020). Divided we fall: How ratios undermine research in strategic management. Organizational Research Methods, 23(2), 211–237. https://doi.org/10.1177/1094428118773455

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

Finsaas, M. G., & Goldstein, B. L. (2021). Do simple slopes follow-up tests lead us astray? Advancements in the visualization and reporting of interactions. Psychological Methods, 26(1), 38–60. https://psycnet.apa.org/doi/10.1037/met0000266

Gonzales, O., Valente, M. J., Cheong, J., & MacKinnon, D. P. (2023). Mediation/indirect effects in structural equation modeling. In R. H. Hoyle (Ed.) Handbook of structural equation modeling (2nd ed.), pp. 409–426. Guilford.

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. https://doi.org/10.1080/21642850.2021.1920416

Hardin, J. W., & Hilbe, J. M. (2014). Estimation and testing of binomial and beta-binomial regression models with and without zero inflation. The Stata Journal, 14(2), 292–303. https://journals.sagepub.com/doi/pdf/10.1177/1536867X1401400204

Hoffman, L. (2015 chapters 2–3). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge / Taylor & Francis. Also available at the University of Iowa library in electronic form.

Hsieh, F. Y. (1989). Sample size tables for logistic regression. Statistics in Medicine, 8(7), 795–802. https://doi.org/10.1002/sim.4780080704

Johfre, S. S., & Freese, J. (2021). Reconsidering the reference category. Sociological Methodology, 51(2), 235–269. https://doi.org/10.1177/0081175020982632

Knief, U., & Forstmeier, W. (2021). Violating the normality assumption may be the lesser of two evils. Behavior Research Methods, 53, 2576–2590. https://doi.org/10.3758/s13428-021-01587-5

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. https://doi.org/10.1177/0013164419837321

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. https://doi.org/10.3758/s13428-021-01546-0

Long, J. S. (1997 chapter 7). Regression models for categorical and limited dependent variables. Sage.

McCabe, C. J., Halvorson, M. A., King, K.M., Cao, X., & Kim, D. S. (2022). Interpreting interaction effects in generalized linear models of nonlinear probabilities and counts. Multivariate Behavioral Research, 57(2–3), 243-263. https://doi.org/10.1080/00273171.2020.1868966

McGinley, J. S., Curran, P. J., & Hedeker, D. (2015). A novel modeling framework for ordinal data defined by collapsed counts. Statistics in Medicine, 34(15), 2312–2324. https://doi.org/10.1002/sim.6495

Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science 6(4), 81–117. http://dx.doi.org/10.15195/v6.a4

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. https://doi.org/10.1177/25152459211007368

Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40(1), 7–20. https://doi.org/10.1080/0022250X.2015.1112384

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 be completed individually. 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 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 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, 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!