Fall 2024 PSQF 6243 Section 0002/0EXW: Intermediate Statistical Methods
(or what I really mean: Introduction to General Linear Models, or GLMs)

Schedule of Events (Printable Syllabus: last updated 8/21/24)

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 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 associations; creating predictor variables and building models to evaluate their unique effects; and interpreting and presenting empirical findings. Prior to enrolling, participants should be familiar 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 (first-come, first-served), in which multiple participants can receive 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 (HW; 6 planned inititally) through a custom online system—these will be graded for accuracy. Up to 12 points may be earned from submitting formative assessments (FA; 6 planned initially); these will be graded for effort only—incorrect answers will not be penalized. Participants may earn up to 2 extra credit points for completing 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. If that happens, this syllabus will be updated to reflect the new point totals.

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 encourage participants to keep up with the class, late homework assignments will incur a 2-point penalty, and late 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 Thursday, December 19, 2024, at 11:59 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:

• 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 could also pay $48 to install a 6-month student copy of STATA on your local machine

Course Textbook:

(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).

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 assignments must ultimately be completed individually using the student-specific datasets provided for each assignment. Please consult the instructor if you have questions.

The use of ChatGPT or any other Artificial Intelligence (AI) should not be needed (or helpful), as the course materials will provide examples of all software code needed to complete homework assignments. Similarly, the use of AI in completing formative assessments (FAs) will defeat their purpose, as these structured reviews are designed to help participants recognize remaining sources of confusion or inexperience (and FA points will be given regardless, so long as there is some effort made in trying to answer each question).

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 circumstances 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 internet). 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 Civil Rights Compliance. 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!

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. https://psycnet.apa.org/doi/10.1037/met0000248

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. https://doi.org/10.1016/j.addbeh.2018.09.018

Cohen, J. (1994). The earth is round ( p < .05). American Psychologist, 49(12), 997–1003. https://psycnet.apa.org/doi/10.1037/0003-066X.49.12.997

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. https://doi.org/10.1016/j.tics.2019.12.009

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://doi.org/10.1037/met0000266

Hoffman, L. (2015 chapter 2). Longitudinal analysis: Modeling within-person fluctuation and change . Routledge / Taylor & Francis. https://psycnet.apa.org/record/2015-01073-000. Also available for free at the University of Iowa library in electronic form.

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

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for  t-tests and ANOVAs. Frontiers in Psychology, article 863. https://doi.org/10.3389/fpsyg.2013.00863

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. https://psycnet.apa.org/record/2019-78567-005

Westfall, J., & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. PLOS ONE 11(3), e0152719. https://doi.org/10.1371/journal.pone.0152719

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. https://files.eric.ed.gov/fulltext/EJ1015680.pdf