Instructor Contact Information: |
Professor Lesa Hoffman (she/her—you can call me Lesa) Department of Education and Human Development Email: LesaH@Clemson.edu (preferred mode of contact) Office: 213D Tillman Hall (forthcoming) | Zoom Link for Class and Office Hours: | https://clemson.zoom.us/j/8236434097 Meeting ID: 823 643 4097; Mobile Access: +19292056099 (please use your real name as your account name to be admitted) |
| Course Location and Time: |
324 Tillman Hall or via zoom Mondays 4:40–7:25 PM Eastern | Zoom-Only Office Hours | Tuesdays and Thursdays 2:30–4:00 PM Eastern in a group format (first-come, first-served, no appointments) or individually by appointment |
| Coursework Access: |
Canvas for Formative Assessments Online Homework System (now available!) |
R Package Dcoumentation: | TeachingDemos, readxl, supernova, lm, lm.beta, multcomp, ppcor, Hmisc, lmhelpers, prediction |
Week |
Class |
Due Date |
Materials |
Resources |
|---|---|---|---|---|
| 1 | 1/12 | 11:59 PM Sunday | Qualtrics intake survey | |
| During class | Introductions Lecture 0: Introduction to this Course Video: Lecture 0 (all, in class) Lecture 1: Univariate Data Description Lecture 1 Files (.zip folder of data, syntax, and output used in Lecture 1) Video Part 1: Lecture 1 slides 1-23 (in class; updated 1/24/26) |
D & H ch. 1 |
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| 2 | 1/19 | 11:59 PM Sunday | HW0 (online, based on the syllabus for 1 point of extra credit) | Video: Intro to Online HW |
| During class | NO CLASS (MLK DAY) | |||
| 3 | 1/26 | 11:59 PM Sunday | Watch Video Part 2: Lecture 1 slides 18-40 (out of class) Install R software and then install RStudio software Watch R Demo Video based on Lecture 1 Files |
|
| During class | NO CLASS (DUE TO WEATHER) |
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| 4 | 2/2 | 11:59 PM Sunday | FA1 (in Canvas) HW1 (online, based on Lecture 1 files) |
Handout: Steps for Doing HW |
| During class | Discussion of FA1 Lecture 2: GLMs with Single-Slope Predictors Lecture 2 Files (.zip folder of data, syntax, and output) |
D & H ch. 2, ch. 5.1 Power Tables Cohen (1994) Correll et al. (2020) |
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| 5 | 2/9 | 11:59 PM Sunday | FA3 and FA3 (in Canvas) |
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| During class | Discussion of FA2 and FA3 Lecture 2, continued Example 2: GLMs with Single-Slope Predictors Example 2 Files (.zip folder of data, syntax, and output) |
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| 6 | 2/16 | 11:59 PM Sunday | HW2 (online, based on Example 2) Video catchup and questions (if needed) |
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| During class | Lecture 3 and Example 3: GLMs with Multiple-Slope Predictors Example 3 Files (.zip folder of data, syntax, and output) |
D & H ch. 4, ch. 9–12 Johfre & Freese (2021) Rodgers (2019) |
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| 7 | 2/23 | 11:59 PM Sunday | FA4 (in Canvas) Video catchup and questions (if needed) |
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| During class | Discussion of FA4 Lecture 3 and Example 3, continued |
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| 8 | 3/2 | 11:59 PM Sunday | HW3 (online, based on Example 3) Video catchup and questions (if needed) |
|
| During class | Lecture 4 and Example 4a: GLMs with Multiple Predictors Example 4a Files (.zip folder of data, syntax, and output) |
D & H ch. 3, ch. 5.3, ch. 8 Lakens (2013) Williams et al. (2013) |
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| 9 | 3/9 | 11:59 PM Sunday | FA5 (in Canvas) Video catchup and questions (if needed) |
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| During class | Discussion of FA5 Lecture 4 and Example 4a, continued Example 4b: Review and Multiple-Predictor GLMs Example 4b Files (.zip folder of data, syntax, and output) |
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| 10 | 3/16 | 11:59 PM Sunday | Nothing due | |
| During class | NO CLASS (SPRING BREAK) | |||
| 11 | 3/23 | 11:59 PM Sunday | HW4 (online, based on Example 4a and 4b) Video catchup and questions (if needed) |
|
| During class | Lecture 5 and Example 5: GLMs with Interactions Example 5 Files (.zip folder of data, syntax, and output) Interaction example spreadsheet |
D & H ch. 13–14 Hoffman (2015 ch. 2) Belzak & Bauer (2019) Finsaas & Goldstein (2021) Certo et al. (2020) |
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| 12 | 3/30 | 11:59 PM Sunday | Project Plan (in Canvas) Video catchup and questions (if needed) |
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| During class | Lecture 5 and Example 5, continued | |||
| 13 | 4/6 | 11:59 PM Sunday | FA6 and FA7 (in Canvas) Project Plan Revisions (in Canvas if needed) Video catchup and questions (if needed) |
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| During class | Discussion of FA6 and FA7 Lecture 5 and Example 5, continued |
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| 14 | 4/13 | 11:59 PM Sunday | HW5 (online, based on Example 5) Video catchup and questions (if needed) |
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| During class | Lecture 6: Caveats | D & H ch. 16–17 Anderson (2020) Westfall & Yarkoni (2016) |
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| 15 | 4/20 | 11:59 PM Sunday | Project Report (in Canvas) | |
| During class | Lecture 7: Next Steps | Hoffman & Walters (2022) TBD |
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| 16 | 4/27 | 11:59 PM Sunday | Nothing due | |
| During class | NO CLASS (FINALS WEEK) | |||
| 5/1 | 11:59 PM Friday | Project Report Revisions (in Canvas, if needed) and all outstanding coursework |
Intermediate inferential statistical methods course for educational research. Emphasis is on understanding the theory and application of univariate statistics and developing the ability to conduct independent empirical research in education. Prerequisite: EDF 9270 or equivalent.
The planned schedule of topics and events given here may need to be adjusted during the course. The course's external website will always have the most current schedule of events and due dates. The instructor will provide announcements and reminders via email; larger changes will also be announced through Canvas. Emails will be answered within 1 business day barring unforeseen circumstances.
Instructor office hours will be held on zoom in a group format (first-come, first-served), in which multiple participants can receive assistance in succession (or simultaneously) without an appointment. Individual zoom meetings with the instructor are also available by appointment upon request.
This HyFlex course will meet synchronously in person in 324 Tillman Hall and in the instructor's zoom room Mondays from 4:40–7:25 PM. Participants may change the modality—in person as a “roomer” or online as a “zoomer”—in which they attend each week as desired. Attendance is expected and strongly encouraged but is not formally required. Auditors and visitors are always welcome to attend class, and participants may bring food or drink to consume during class. No required sessions will be held outside the regular class time (i.e., no additional midterm or final exam sessions).
This quantitative methods course will focus on the prediction of numeric 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 necessary steps in conducting a GLM analysis: describing variables and their associations; creating predictor variables and building models to evaluate their unique effects; and interpreting and presenting empirical findings. Participants should already 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 reviews, the materials for which will be available for download at the course's external website. Readings and other resources have been suggested for each unit and may be updated later. Video recordings of each class will be made available on a YouTube playlist so that searchable closed captioning will be provided, and supplemental videos (e.g., additional lectures, examples, or software demonstrations) may be posted to YouTube as well. Only the screen share and classroom audio will be captured on the video recordings.
Participants can earn up to 100 total points by completing out-of-class coursework as follows:
- Up to 21 points may be earned from submitting formative assessments (FAs; 7 planned initially) administered using Canvas. In each of these review activities, 2 points will be given automatically for completion, and up to 1 additional point will be given based on accuracy. Feedback will be made available on the day of each class, and the responses will be discussed during class.
- Up to 59 points can be earned from submitting homework assignments (HWs; 5 planned initially) through a custom online system created by the instructor—these will be graded for accuracy. In these data analysis activities, the computational questions allow infinite attempts at accuracy, whereas the results questions allow one attempt. Complete feedback will be available after the due date passes.
- Up to 20 points may be earned from completing an individual project (including a plan document and a report document). Project reports must be at least ¾ complete to be accepted and may be revised once to earn additional points. Further requirements and a rubric will be made available separately.
- Up to 1 point of extra credit for completing HW 0; there may be other opportunities to earn extra credit at the instructor's discretion.
- Revisions to the planned course schedule and/or content may result in fewer requirements (and thus fewer total points) at the instructor's discretion. If that happens, this syllabus will be updated to reflect the new point totals.
Participants may submit work at any point during the semester to be counted towards their grade. However:
- Late HW will incur a 3-point penalty, late FAs and project plans will incur a 1-point penalty, and late project reports will incur a 5-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 unforeseen dire circumstances and entirely at the instructor's discretion. All work must be submitted by 11:59 PM on Friday, May 1, 2026, to be included in the course grade.
- Final grades will be determined by the percentage earned out of the total possible points:
≥ 93 = 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
This course will use R software for statistical analysis, which is free online. Participants will need to first install R software (currently version 4.5.2) and then install RStudio software (currently version 2026.01.0 build 392).
(D & H): Darlington, R. B., & Hayes, A. F. (2016). Regression analysis and linear models: Concepts, applications, and implementation. Guilford. Available electronically from the Clemson University library.
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
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
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., & Walters, R. W. (2022). Catching up on multilevel modeling. Annual Review of Psychology, 73, 629–658. https://doi.org/10.1146/annurev-psych-020821-103525
Hoffman, L. (2015 chapter 2). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge / Taylor & Francis. https://psycnet.apa.org/record/2015-01073-000.
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
While students can work with each other to understand the course content, all coursework must ultimately be completed individually. Please consult the instructor if you have questions.
The use of Artificial Intelligence (AI) should not be needed (or helpful), as the course materials will provide examples of all software code needed to complete HW. The instructor will not help troubleshoot R code generated by AI for HW purposes that uses different packages or conventions than was used in class. Similarly, the use of AI in completing FAs will defeat their purpose, as these structured reviews are designed to help participants recognize remaining sources of confusion or inexperience. In the project report, acceptable uses of AI are limited to grammatical and proof-reading advice (and should be credited). Any other uncredited use of AI will be treated as academic misconduct. Please refer to the simple syllabus in the course Canvas site for other relevant university policies.
The instructor wants ALL participants to feel welcome and encouraged to actively 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 special needs are encouraged to contact the instructor for a confidential discussion of their individual considerations for academic accommodation.
All participants are welcome to attend class via zoom instead of in person for any reason at any time. If you are seriously ill, 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.
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 strongly encouraged but is not required. Please note that class video recordings posted on YouTube will NOT include any video from course participants—only the screen share from the instructor and class audio 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 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!