Current version of this course (Fall 2024)
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 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 |
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 |
Coursework Access: |
ICON for Formative Assessments U Iowa Virtual Desktop Software 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: - SAS: PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC CORR, PROC GLM, PROC REG - STATA: SUMMARIZE, TABULATE, REGRESS, PWCORR, PCORR - R: TeachingDemos package, HAVEN package, EXPSS package, READXL package, LM package, MULTCOMP package, PPCOR package, HMISC package, CAR package, PREDICTION package |
Week |
Weekday |
Topics and Course Materials |
Readings and Other Resources for Each Topic |
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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 1-17): Video |
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R: 1/20 | MEET ON ZOOM ONLY 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 |
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2 | M: 1/24 | FA1 DUE VIA ICON BY 11:59 PM | |
T: 1/25 | MEET ON ZOOM ONLY Lecture 1, continued Lecture 1 Part 2 (slides 14-39): Video |
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R: 1/27 | OFFICE HOURS END AT 4 PM 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) |
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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 |
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R: 2/3 | MEET ON ZOOM ONLY Lecture 2 and Example 2, continued Lecture 2 Part 3 (slides 11-25): Video |
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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 Lecture 2 Part 4 (slides 27-46 + excel demo): Video |
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R: 2/10 | Lecture 2 and Example 2, continued Lecture 2 and Example 2 (pages 1-4) Part 5: Video |
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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 |
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R: 2/17 | Lecture 2 and Example 2, continued Lecture 2 (slides 47 and 52) and Example 2 (pages 8-13) Part 7: Video |
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6 | M: 2/21 | NO HW OR HA DUE | |
T: 2/22 | MEET ON ZOOM ONLY 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) |
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R: 2/24 | Lecture 3 and Example 3, continued Lecture 3 (slides 5-8) and Example 3 (pages 1-3) Part 2: Video |
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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 |
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R: 3/3 | Lecture 3 and Example 3, continued Lecture 3 (slides 9-18) Part 4: Video |
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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 |
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R: 3/10 | Lecture 3 and Example 3, continued Lecture 3 (slides 23-31) and Example 3 (pages 6-9) Part 6: Video |
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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 32-35) and Example 3 (pages 10-13) Part 7: Video |
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R: 3/24 | MEET ON ZOOM ONLY Lecture 3 and Example 3, continued Lecture 3 (review + new slides 36-39) and Example 3 (review + new pages 14-18) Part 8: Video |
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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) |
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R: 3/31 | Lecture 4 and Example 4a, continued Lecture 4 (slides 19-26) and Example 4a (pages 1-4) Part 2: Video |
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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 |
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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 |
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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 |
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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 |
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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) |
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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 |
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R: 4/28 | Lecture 5 and Example 5, continued Lecture 5 (none) and Example 5 (pages 1-12) Part 3: Video |
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16 | M: 5/2 | FA6 DUE VIA ICON BY 11:59 PM | |
T: 5/3 | FA 6 Starter Kit Lecture 5 and Example 5, continued Example 5 (pages 9-24) and Lecture 5 (slide 21) Part 4: Video |
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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) |
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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 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 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.
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 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.
>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 web-based SAS OnDemand platform for free on your local machine
- You could also pay $48 to install a 6-month student copy of STATA on your local machine
(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).
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.
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.
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.
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!