Lesa's course directory

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 Room
and Time:

Instructor Zoom-Only Office Hours:
166 North Lindquist Center (LC) or via zoom
Tuesdays and Thursdays 2:00–3:15 PM

Tuesdays and Thursdays 3:30–4:30 PM in a group
format or individually by appointment
Volunteer Graduate Teaching Assistants' Contact Information and Zoom-Only Office Hours: Nikki Tennessen (she/her; PhD student in Higher Education and Student Affairs in EPLS and MA student in Educational Measurement and Statistics in PSQF)
Email: Nicole-Tennessen@UIowa.edu
Mondays 1:00–2:00 PM and Fridays 11:00 AM–12:00 PM in a group format at: https://uiowa.zoom.us/j/92004226815

Lexi Oakley (she/her; MA student in Educational Measurement and Statistics in PSQF); Email: Alexis-C-Oakley@UIowa.edu
Wednesdays 2:00–4:00 PM in a group format at:
Zoom Link
for Class and Instructor
Office Hours:
no longer available Graduate Teaching Assistant (shared
with PSQF 4143)
Contact Information and
Zoom-Only Office Hours:
Kun Wang (he/him; PhD student in Counseling Psychology in PSQF)
Email: Kun-Wang-2@UIowa.edu
Mondays 8:00–9:30 AM and 12:00–3:00 PM;
Tuesdays 8:00 AM–9:30 AM in a group format at:
ICON for Formative Assessments

U Iowa Virtual Desktop Software

Online Homework System (no longer available)
A video introduction to homework is available here

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

Software documentation:
- R: TeachingDemos package, HAVEN package, EXPSS package, READXL package, LM package, MULTCOMP package, PPCOR package, HMISC package, CAR package, PREDICTION package

Schedule of Events (Printable Syllabus; last updated 5/11/22)


and Date

Topics and Course Materials

Readings and Other Resources for Each Topic

Lecture 0: Introduction to this Course
Lecture 0 Part 1 (slides 1-17): Video
Lecture 0, continued
Lecture 0 Part 2 (slides 17-23): Video

Lecture 1: Univariate Data Description (updated 1/20/22)
Example 1 Files (.zip folder of data, syntax, and output used in Lecture 1)
Lecture 1 Part 1 (slides 1-15): Video

D & H ch. 1
2 M: 1/24 FA1 DUE VIA ICON BY 11:59 PM  
Lecture 1, continued
Lecture 1 Part 2 (slides 14-39): Video

Lecture 2 and Example 2: GLMs with Single-Slope Predictors
Example 2 Files (.zip folder of data, syntax, and output)
Lecture 2 Part 1 (slides 1-20): Video
D & H ch. 2, ch. 5.1
Power Tables
Cohen (1994)
Correll et al. (2020)
3 M: 1/31 HW0 (for 2 points extra credit) DUE ONLINE BY 11:59 PM Video: Intro to Homework
T: 2/1 Lecture 2 and Example 2, continued
Lecture 2 Part 2 (slides 11-25): Video
Lecture 2 and Example 2, continued
Lecture 2 Part 3 (slides 11-25): Video
4 M: 2/7 HW1 (based on Example 1) DUE ONLINE BY 11:59 PM Handout: Intro to the U Iowa
Virtual Desktop

Video: Intro to the U Iowa
Virtual Desktop

Videos: Intro to SAS, STATA, and
R via Rstudio
(uses Example 1 files)
T: 2/8 Lecture 2 and Example 2, continued
Excel Decision Demo (in-class version in second sheet)
Lecture 2 Part 4 (slides 27-46 + excel demo): Video
R: 2/10 Lecture 2 and Example 2, continued
Lecture 2 and Example 2 (pages 1-4) Part 5: Video
5 M: 2/14 FA2 DUE VIA ICON BY 11:59 PM  
T: 2/15 Lecture 2 and Example 2, continued
Lecture 2 (slide 25) and Example 2 (pages 3-8) Part 6: Video
R: 2/17 Lecture 2 and Example 2, continued
Lecture 2 (slides 47 and 52) and Example 2 (pages 8-13) Part 7: Video
6 M: 2/21 NO HW OR HA DUE  
Lecture 2, continued
Lecture 2 (slides 48, 52-60) Part 8: Video

Lecture 3 and Example 3 (updated for effect sizes 4/2/22): GLMs with Multiple-Slope Predictors
Example 3 Files (.zip folder of data, syntax, and output, updated for effect sizes 4/2/22)
Lecture 3 (slides 1-5) Part 1: Video

D & H ch. 4, ch. 9–12
Johfre & Freese (2021)
Rodgers (2019)
R: 2/24 Lecture 3 and Example 3, continued
Lecture 3 (slides 5-8) and Example 3 (pages 1-3) Part 2: Video
7 M: 2/28 HW2 (based on Example 2) DUE ONLINE BY 11:59 PM Handout: Steps for Doing Homework
T: 3/1 Lecture 3 and Example 3, continued
Lecture 3 (slides 5-8) and Example 3 (pages 1-3) Part 3: Video
R: 3/3 Lecture 3 and Example 3, continued
Lecture 3 (slides 9-18) Part 4: Video
8 M: 3/7 FA3 DUE VIA ICON BY 11:59 PM  
T: 3/8 Lecture 3 and Example 3, continued
Lecture 3 (slides 9-22) and Example 3 (pages 4-5) Part 5: Video
R: 3/10 Lecture 3 and Example 3, continued
Lecture 3 (slides 23-31) and Example 3 (pages 6-9) Part 6: Video
9 M: 3/14 NO HW OR HA DUE  
10 M: 3/21 NO HW OR HA DUE  
T: 3/22 Lecture 3 and Example 3, continued
Lecture 3 (slides 32-35) and Example 3 (pages 10-13) Part 7: Video
Lecture 3 and Example 3, continued
Lecture 3 (review + new slides 36-39) and Example 3 (review + new pages 14-18) Part 8: Video
11 M: 3/28 HW3 (based on Example 3 first two models) DUE ONLINE BY 11:59 PM  
T: 3/29 Lecture 4 and Example 4a (updated 4/10/22): GLMs with Multiple Predictors
Example 4a Files (.zip folder of data, syntax, and output)
Lecture 4 (slides 1-19) Part 1: Video
D & H ch. 3, ch. 5.3, ch. 8
Williams et al. (2013)
R: 3/31 Lecture 4 and Example 4a, continued
Lecture 4 (slides 19-26) and Example 4a (pages 1-4) Part 2: Video
12 M: 4/4 FA4 DUE VIA ICON BY 11:59 PM  
T: 4/5 Review: Discussion of HW4 and FA4 (no new material): Video  
R: 4/7 Lecture 4 and Example 4a, continued
Lecture 4 (slides 10, 25-30) and Example 4a (pages 4-7) Part 3: Video
13 M: 4/11 HW4 (based on Example 3 last two models) DUE ONLINE !!!! WED 4/13 !!!! BY 11:59 PM  
T: 4/12 Lecture 4 and Example 4a, continued
Example 4a (pages 6-14) Part 4: Video
R: 4/14 Lecture 4, continued
Example 4b
: Review and Multiple-Predictor GLM
Example 4b Files (.zip folder of data, syntax, and output)
Lecture 4 (slides 31-35) and Example 4b (pages 1-6) Part 1: Video
14 M: 4/18 FA5 DUE VIA ICON BY 11:59 PM  
T: 4/19 Example 4b, continued
Example 4b (pages 6-15) Part 2: Video
R: 4/21 Lecture 5 and Example 5 (updated 4/20/22): GLMs with Single-Slope Interactions
Example 5 Files (.zip folder of data, syntax, and output)
Lecture 5 slides (1-15 ) Part 1: Video
D & H ch. 13–14
Finsaas & Goldstein (2021)
Hoffman (2015 ch. 2)
15 M: 4/25 HW5 (based on Example 4a or 4b) DUE ONLINE !!!! WED 4/27 !!!! BY 11:59 PM  
T: 4/26 Lecture 5 and Example 5, continued
Lecture 5 (impromptu examples + slides 14-17) Part 2: Video
R: 4/28 Lecture 5 and Example 5, continued
Lecture 5 (none) and Example 5 (pages 1-12) Part 3: Video
16 M: 5/2 FA6 DUE VIA ICON BY 11:59 PM  
T: 5/3 FA 6 Starter Kit
FA 6 Answer Key
Lecture 5 and Example 5, continued
Example 5 (pages 9-24) and Lecture 5 (slide 21) Part 4: Video
R: 5/5 Lecture 6: Caveats and Next Steps
Lecture 6 (all): Video

Lecture 7 and Example 7: GLMs with Multiple-Slope Interactions -- see materials and video for Lecture 7 and Example 7 of this class (sorry, SAS and STATA only here, but there is an R version of the first model available as the third model of Example 1 of this class)
D & H ch. 16–17
Anderson (2020)
Westfall & Yarkoni (2016)

Belzak & Bauer (2019)
17 M: 5/9 Nikki's office hours from 1:00-2:00 PM  
T: 5/10 NO CLASS, but Lesa's office hours from 12:30-4:30 PM  
W: 5/11 Lexi's office hours from 2:00-4:00 PM  
R: 5/12 NO CLASS, but Lesa's office hours from 12:30-4:30 PM  
F: 5/13 Nikki's office hours from 11:00-12:00 PM
HW6 (based on Example 5) 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 focus on the analysis of univariate outcomes using the general linear model(GLM; i.e., regression, analysis of variance, analysis of covariance). The course objective is for participants to be able to complete all the necessary steps in a GLM analysis: describing the variables of interest and their zero-order associations; creating predictor variables and building models to evaluate their unique effects; and interpreting and presenting empirical findings. Prior to enrolling, participants should be comfortable with univariate descriptive statistics, measures of bivariate association, and null hypothesis significance testing.

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):

(D & H above): 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"):

Anderson, S. F. (2020). Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of multiple testing, and implications for the replication crisis. Psychological Methods, 25(5), 596–609.

Belzak, W. C. M., & Bauer, D. J. (2019). Interaction effects may actually be nonlinear effects in disguise: A review of the problem and potential solutions. Addictive Behaviors, 94, 99–108.

Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003.

Correll, J., Mellinger, C., McClelland, G. H., & Judd, C. M. (2020). Avoid Cohen's ‘small', ‘medium', and ‘large' for power analysis. Trends in Cognitive Sciences, 24(3), 200–207.

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.

Johfre, S. S., & Freese, J. (2021). Reconsidering the reference category. Sociological Methodology, 51(2), 235–269.

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

Rodgers, J. L. (2019). Degrees of freedom at the start of the second 100 years: A pedagogical treatise. Advances in Methods and Practices in Psychological Science, 2(4), 396–405.

Westfall, J., & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. PLOS ONE, 11(3), e0152719.

Williams, M. N., Grajales, C. A. G., & Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, 18, Article 11.

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!