Instructor: |
Professor Lesa Hoffman (she, her, hers) Educational Measurement and Statistics Program | Department: | Psychological and Quantitative Foundations Office: South 361 Lindquist Center DEO: Dr. Megan Foley Nicpon |
Zoom Access: | no longer available | Instructor Email: | Lesa-Hoffman@UIowa.edu |
Course Time: | Tuesdays and Thursdays 12:30–1:45 PM | Instructor Phone: | 319-384-0522 |
Zoom-Only Office Hours: |
Tuesdays and Thursdays 3:15–4:45 PM as a group or individually by appointment |
SAS Resources: STATA Resources: |
Lesa's SAS guide from PilesOfVariance.com Lesa's Stata guide from PilesOfVariance.com |
Homework Portal: |
Link to UIowa Virtual Desktop Homework Portal (now longer available) Video: Introduction to Homework System (using HW0; recorded Spring 2021) |
Resources for Doing Homework Using SAS or STATA: |
Handouts (from Fall 2020):
Videos (recorded Fall 2020):
|
Week |
Date |
Topics and Course Materials |
Readings |
---|---|---|---|
1 | 1/25 | NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE | |
1/26 | Lecture 0: Introduction to this Course and to Quantitative Methods Lecture 0 Part 1 (slides 1-20): Video |
Howell ch. 1 Mitchell ch. 1 |
|
1/28 | Lecture 0, continued Lecture 0 Part 2 (slides 21-26 end): Video Lecture 1: Univariate Data Description and Inference Example 1 Data Files (Data, Syntax, Output for SAS and STATA also used in Lecture 1) Lecture 1 Part 1 (slides 1-13): Video |
Howell ch. 2-3, 4-5 |
|
2 | 2/1 | NO HW OR FA DUE | |
2/2 | Watch video on your own: Introduction to the Online Homework System Lecture 1, continued Lecture 1 Part 2 (slides 14-41): Video |
||
2/4 | Lecture 1, continued Lecture 1 Part 3 (slides 34-61): Video |
||
3 | 2/8 | HW0 (3 points extra credit) DUE ONLINE BY 11:59 PM | |
2/9 | Watch videos on your own: Using SAS or STATA on the UIowa Virtual Desktop Lecture 1, continued Lecture 4 Part 4 (slides 62-80): Video |
||
2/11 | Lecture 2 and
Example 2: Bivariate Association and Significance Testing Example 2 Data Files (Data, Syntax, Output for SAS and STATA used in Example 2 handout) Lecture 2 (slides 1-21) Part 1: Video |
Howell ch. 6 Mitchell ch. 2-3 Cohen (1994) |
|
4 | 2/15 | FA1 DUE VIA ICON BY 11:59 PM | |
2/16 | Lecture 2 and Example 2, continued Lecture 2 (slides 10-21) and Example 2 (pages 1-3) Part 2: Video |
||
2/18 | Lecture 2 and Example 2, continued Lecture 2 (slides 22-24) and Example 2 (pages 3-8) Part 3: Video |
||
5 | 2/22 | NO HW OR FA DUE | |
2/23 | Lecture 2 and Example 2, continued Lecture 2 (slides 25-38) and Example 2 (page 9) Part 4: Video |
||
2/25 | Lecture 2, continued, and discussion using: This Article, These Effect Size Conversions, and These Power Tables Lecture 2 (slides 38-44) and Example 2 (pages 9-10) Part 5: Video |
||
6 | 3/1 | HW1 (based on Example 1) DUE ONLINE BY 11:59 PM | |
3/2 | NO CLASS OR OFFICE HOURS |
||
3/4 | Lecture 3 and
Example 3: General Linear Models (GLMs) with a Single Fixed Effect for Each Predictor Example 3 Data Files (used in Example 3 handout) Lecture 3 (slides 1-6) Part 1: Video |
Howell ch. 7-9, 11-12 Mitchell ch. 4-5 Williams et al. (2013) |
|
7 | 3/8 | FA2 DUE VIA ICON BY 11:59 PM | |
3/9 | Lecture 3 and Example 3, continued FA2 Spreadsheet of Bivariate Associations Lecture 3 (slides 17-22) Part 2: Video |
||
3/11 | Lecture 3 and Example 3, continued Lecture 3 (none) and Example 3 (pages 1-7) Part 3: Video (recording from class had no audio; was re-recorded offline) |
||
8 | 3/15 | HW2 (based on Example 2) DUE ONLINE BY 11:59 PM | |
3/16 | Lecture 3 and Example 3, continued Lecture 3 (slides 23-27) and Example 3 (pages 6-11) Part 4: Video |
||
3/18 | Lecture 3, continued Lecture 3 (slides 28-33) Part 5: Video Lecture 4 and Example 4: GLMs with Multiple Fixed Effects for Each Predictor Example 4 Data Files (used in Example 4 handout) Lecture 4 (slides 1-10) and Example 4 (none) Part 1: Video |
Howell ch. 15 Mitchell ch. 6-11 Rodgers (2019) |
|
9 | 3/22 | FA3 DUE VIA ICON BY 11:59 PM | |
3/23 | Lecture 4 and Example 4, continued Lecture 4 (slides 5-16) and Example 4 (none) Part 2: Video |
||
3/25 | Lecture 4 and Example 4, continued Lecture 4 (slides 14-22) and Example 4 (pages 1-6) Part 3: Video |
||
10 | 3/29 | HW3 (based on Example 3) DUE ONLINE BY 11:59 PM | |
3/30 | Lecture 4 and Example 4, continued Lecture 4 (none) and Example 4 (pages 4-8) Part 4: Video |
||
4/1 | Lecture 4 and Example 4, continued Lecture 4 (slides 23-35) and Example 4 (pages 7-11) Part 5: Video |
||
11 | 4/5 | FA4 DUE VIA ICON BY 11:59 PM | |
4/6 | Lecture 4 and Example 4, continued Lecture 4 (slides 36-43) and Example 4 (pages 12-18) Part 6: Video |
||
4/8 | Lecture 5 (corrected 6/10/21),
Example 5a, and
Example 5b: GLMs with Multiple Predictors Example 5a Data Files (used in Example 5a handout) Example 5b Data Files (used in Example 5b handout) Lecture 5 Part 1: Video HW4 Help Spreadsheet |
Mitchell ch.14-19 Hoffman ch. 2 sect. 1 |
|
12 | 4/12 | HW4 (based on Example 4) DUE ONLINE BY 11:59 PM | |
4/13 | Lecture 5 and Example 5a, continued Lecture 5 (slides 12-21) and Example 5a (all) Part 2: Video R2 Example Spreadsheet |
||
4/15 | Lecture 5, continued Lecture 5 Part 3 (slides 22-27 from Fall 2020; no recording today): Video Example 5b (p. 1-11 outside of class) Part 1: Video Example 5b (p. 12+ outside of class) Part 2: Video |
||
13 | 4/19 | FA5 DUE VIA ICON BY 11:59 PM | |
4/20 | Lecture 6 and
Example 6: GLMs with Single-Slope Interaction Effects Example 6 Data Files (used in Example 6 handout) Lecture 6 (slides 1-12) and Example 6 (none really) Part 1: Video |
Hoffman ch. 2 sect. 2 | |
4/22 | Lecture 6 and Example 6, continued Lecture 6 (slides 12-18) and Example 6 (none) Part 2: Video |
||
14 | 4/26 | HW5 (based on Example 5a/5b) DUE ONLINE BY 11:59 PM | |
4/27 | Lecture 6 and Example 6, continued Lecture 6 (slides 17-22) and Example 6 (none) Part 3: Video |
||
4/29 | Example 6, continued Example 6 (pages 1-7) Part 4: Video |
||
15 | 5/3 | FA6 DUE VIA ICON BY 11:59 PM | |
5/4 | FA6 Starter Kit FA6 Answer Key Example 6, continued Example 6 (pages 5-14) Part 5: Video |
||
5/6 | Example 6, continued Example 6 (pages 12-18) Part 6: Video Bonus Material: Lecture 7 and Example 7: GLMs with Multiple-Slope Interaction Effects Example 7 Data Files (used in Example 7 handout) Lecture 7 Part 1 (slides 1-18): Video Example 7 Part 1 (pages 1-6): Video Example 7 Part 2 (page 6-15): Video Lecture 7 Part 2 (slides 19-26): Video |
Hoffman ch. 2 sect. 3+ Howell ch. 13, 16 |
|
16 | 5/11 | NO CLASS, but office hours will be held from 12:30-4:45 PM | |
5/13 | NO CLASS, but office hours will be held from 12:30-4:45 PM |
||
5/14 | HW6 (based on Example 6) DUE BY 5:00 PM ONLINE ALL OUTSTANDING WORK MUST BE COMPLETED BY 5:00 PM |
This course will meet synchronously 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 illustrate the uses of univariate statistics, bivariate measures of association, and general linear models (i.e., regression, analysis of variance, analysis of covariance) for univariate outcomes. The course is organized to take participants through each of the cumulative steps in a statistical 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. Participants should be comfortable with basic concepts of research prior to enrolling in this course. Auditors and visitors are always welcome to attend class.
Class time will be devoted primarily to lectures and examples, the materials for which will be available for download at the course website prior to class. Attendance via zoom is encouraged but not required; I intend to make video recordings of each class available online, and supplemental videos for specific topics (e.g., software demos) may be posted as well. Readings have been suggested for each specific topic and may be updated later. There will be no required sessions 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, 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. Up to 88 points can be earned from homework assignments (approximately 6 in total)—these will be graded for accuracy. Up to 12 points may be earned from submitting outside-of-class formative assessments (approximately 6 in total); these will be graded on effort only—incorrect answers will not be penalized. Please note there will also be an opportunity to earn up to 3 points of extra credit (labeled as homework 0; see the above syllabus). There may be other opportunities to earn extra credit at the instructor's discretion.
In order to provide participants with prompt feedback, late homework assignments will incur a 1-point penalty. However, 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. Late or incomplete outside-of-class quizzes will incur a 0.5-point penalty. 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 21, 2021 at 11:59 PM.
>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 software that can estimate the models presented. Although the course will feature SAS and STATA primarily, other software packages (e.g., SPSS, R) could also be used to complete homework assignments. All of these packages are freely available to University of Iowa members through the UIowa Virtual Desktop. A second free option for software off-campus is the Research Remote Desktop, although this option is not meant for course work. A third option is SAS university edition, which is available for free. As a fourth option, 6-month student licenses for STATA may be purchased for $48 here.
Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003.
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.
Howell, D. C. (2010). Statistical methods for psychology (7th ed). Belmont, CA: Cengage Wadsworth.
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.
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.
Mitchell, M. N. (2015). Stata for the behavioral sciences. College Station, TX: Stata Press.
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. Please consult the instructor if you have questions.
Students with disabilities or who have other special needs are encouraged to contact the instructor for a confidential discussion of their individual needs for academic accommodation.
It is the instructor's intent that students from ALL backgrounds and perspectives 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 and follow the course content. You may do so during class, in office hours, over email, or in individual appointments with the instructor (available by request).
With respect to zoom class sessions, please provide the name you wish for me to call you inside your zoom account (i.e., so that it appears on your window while in use) along with a picture—it can be a real picture, an avatar, or a cartoon—just something by which I can use differentiate you. Student use of cameras and microphones during class 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 zoom audio and screen share from the instructor will be captured).
The University of Iowa is committed to making the classroom a respectful and inclusive space for people of all gender, sexual, racial, religious, and other identities. Toward this goal, students are invited in MyUI 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 set forth in the University’s Human Rights policy. For more information, contact the Office of Equal Opportunity and Diversity.
The instructor realizes that this course is not your only obligation in your work or your life. If work or life events (expected or unexpected) may compromise your ability to succeed in this course, 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 to do so until you are too far behind to catch up!