Lesa's course directory


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
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
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):

Planned Schedule of Events (Printable Syllabus; last updated 5/2/2021)



Topics and Course Materials


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  
3/4 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

Schedule of Topics and Events:

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).

Course Objectives, Pre-Requisites, and Materials:

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.

Course Requirements:

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.

Policy on Late Assignments, Formative Assessments, and Incompletes:

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.

Final grades will be determined according to the proportion 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 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.

Course Readings (all available via "Files" in ICON):

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.

Optional Course Textbook (recommended for future Stata reference, optional purchase):

Mitchell, M. N. (2015). Stata for the behavioral sciences. College Station, TX: Stata Press.

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. Please consult the instructor if you have questions.

Accommodating Students with Disabilities:

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.

Respect for Diversity:

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.

Respect for The Rest of Your World:

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!