Lesa's course directory

Previous version of this course (Spring 2020)

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. Megan Foley Nicpon
Course Location
and Time:
166 North Lindquist Center or via zoom
Tuesdays and Thursdays 12:30–1:45 PM
Zoom-Only Office Hours: Tuesdays and Thursdays 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
ID: 5044356512; Mobile: +13126266799
Coursework
Access:
ICON for Formative Assessments
UIowa Virtual Desktop Software
Online Homework System (coming soon)
Coursework
Access:
ICON for Formative Assessments

UIowa Virtual Desktop Software

Online Homework System (coming soon)

For help using Virtual Desktop, SAS, STATA, or R, see the handout and videos posted at 2/7 for PSQF 6243
Software Resources: Lesa's resources:
- Make Friends with SAS class (at UNL)
- Manuals for SAS, SPSS, STATA, and Mplus at PilesOfVariance.com

Software documentation (to be updated as needed):
- SAS: PROC GLM, PROC GLIMMIX
- STATA: REGRESS, OLOGIT
- R: LM package; GLM package

Planned Schedule of Events (Printable Syllabus; last updated 1/20/2022)

Week
Number

Weekday
and Date

Topics and Course Materials

Readings and Other Resources for
Each Topic

1 M: 1/17 NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
T: 1/18 MEETING ON ZOOM ONLY FOR TODAY
Lecture 0: Introduction to this Course
Lecture 0 Part 1 (slides 1-15): Video
 
R: 1/20 MEETING ON ZOOM ONLY FOR TODAY
Lecture 0, continued
Log-likelihood examples: (Excel) (SAS)
 
       
2 M: 1/24 HW0 (for 2 points extra credit) DUE ONLINE BY 11:59 PM  
T: 1/25 Lecture 1 and Example 1: Review of General Linear Models Agresti (2015) ch. 1-3
Hoffman (2015) ch. 2
Enders (2010) ch. 3
Finsaas & Goldstein (2021)
R: 1/27 Lecture 1 and Example 1, continued  
       
3 M: 1/31 FA1 DUE VIA ICON BY 11:59 PM  
T: 2/1 Lecture 1 and Example 1, continued  
R: 2/3 Lecture 2: Models for Categorical Outcomes
Example 2a: Models for Binary Outcomes
Agresti (2015) ch. 4–5
H & H ch. 2, 9
Hsieh (1989)
Mize (2019)
Rohrer & Arslan (2021)
       
4 M: 2/7 HW1 (based on Example 1) DUE ONLINE BY 11:59 PM  
T: 2/8 Lecture 2 and Example 2a, continued  
R: 2/10 Lecture 2 and Example 2a, continued
Example 2b: Models for Ordinal and Nominal Outcomes
Agresti (2015) ch. 6
H & H ch. 15, 16
Bürkner & Vuorre (2019)
Williams (2016)
       
5 M: 2/14 FA2 DUE VIA ICON BY 11:59 PM  
T: 2/15 Lecture 2 and Example 2b, continued  
R: 2/17 Lecture 2 and Example 2b, continued  
       
6 M: 2/21 HW2 (based on Example 2a) DUE ONLINE BY 11:59 PM  
T: 2/22 Lecture 3 and Example 3: Models for Count Outcomes Agresti (2015) ch. 7
H & H ch. 12–14
Green (2021)
McGinley et al. (2015)
R: 2/24 Lecture 3 and Example 3, continued  
       
7 M: 2/28 FA3 DUE VIA ICON BY 11:59 PM  
T: 3/1 Lecture 3 and Example 3, continued  
R: 3/3 Lecture 4: Models for Other Non-Normal Outcomes
Example 4a: Models for Proportion (Binomial) Outcomes
Agresti (2015) ch. 8
H & H ch. 10–11
Hardin & Hilbe (2014)
       
8 M: 3/7 HW3 (based on Example 2b) DUE ONLINE BY 11:59 PM  
T: 3/8 Lecture 4 and Example 4a, continued  
R: 3/10 Lecture 4, continued
Example 4b: Models for Skewed Continuous Outcomes
H & H ch. 6
Knief & Forstmeier (2021)
Konstantopoulos et al. (2019)
       
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 FA4 DUE VIA ICON BY 11:59 PM  
T: 3/22 Lecture 4 and Example 4b, continued  
R: 3/24 Review  
       
11 M: 3/28 HW4 (based on Example 3) DUE ONLINE BY 11:59 PM  
T: 3/29 Lecture 5: Multivariate Models via Univariate Software
Example 5a Part 1: Models for Triadic (Family) Outcomes
Hoffman (2015) ch. 3
Agresti (2015) ch. 9
H & H ch. 18–19
Kumle et al. (2021)
R: 3/31 Lecture 5 and Example 5a Part 1, continued
 
       
12 M: 4/4 NO HW OR HA DUE  
T: 4/5 Lecture 5 and Example 5a Part 1, continued  
R: 4/7 Lecture 5, continued
Example 5b Part 1: Models for Difference Score Outcomes
 
       
13 M: 4/11 FA5 DUE VIA ICON BY 11:59 PM  
T: 4/12 Lecture 5 and Example 5b Part 1, continued  
R: 4/14 Lecture 5, continued
Example 5c: Models for Repeated Measures Outcomes
 
       
14 M: 4/18 NO HW OR HA DUE  
T: 4/19 Lecture 6: Multivariate Models via Path Analysis
Example 5a Part 2
Example 5b Part 2
Enders (2010) ch. 4–5
MacKinnon (2008) ch. 6
R: 4/21 Lecture 6, continued
Example 6a: Path Models for Mediation with Normal Outcomes
 
       
15 M: 4/25 HW5 (based on Example 5a/5b Part 1) DUE ONLINE BY 11:59 PM  
T: 4/26 Lecture 6 and Example 6a, continued  
R: 4/28 Lecture 6, continued
Example 6b: Path Models for Mediation with Binary Outcomes
 
       
16 M: 5/2 FA6 DUE VIA ICON BY 11:59 PM  
T: 5/3 Lecture 6 and Example 6b, continued  
R: 5/5 Review  
       
17 T: 5/10 NO CLASS, but office hours from 12:30-4:30 PM  
R: 5/12 NO CLASS, but office hours from 12:30-4:30 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
 

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 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 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 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.

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 provide participants with prompt feedback, late homework assignments will incur a 1-point penalty, and late formative assessments will incur a 0.5-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, May 13, 2021 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:

Recommended Course Textbook (to be purchased separately):

(H & H): Hardin, J. W. & Hilbe, J. M. (2018). Generalized linear models and extensions (4th ed.). STATA Press.

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 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 follow-up 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 beta-binomial regression models with and without zero inflation. The Stata Journal, 14(2), 292–303.

Hoffman, L. (2015 chapters 2–3). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge / Taylor & Francis.

Hsieh, F. Y. (1989). Sample size tables for logistic regression. Statistics in Medicine, 8, 795–802.

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.

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 assignment must 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 strongly 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 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.

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!