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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: Meeting ID: 5044356512
Mobile Access: +13126266799,,5044356512#
Instructor Email:
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

Lesa's Stata guide from
Link to UIowa Virtual Desktop

Link to Homework Portal

Video: Introduction to Homework System (using HW0)
Resources for
Doing Homework
Using SAS or STATA:
Handouts: Videos:

Planned Schedule of Events (Printable Syllabus; last updated 11/16/2020)



Topics and Course Materials


8/25 Lecture 0: Introduction to this Course and to Quantitative Methods
Lecture 0 Part 1: Video
Howell ch. 1
Mitchell ch. 1
8/27 Lecture 0, continued
Lecture 0 Part 2: Video

Lecture 1
: Univariate Data Description and Inference
Example 1 Data Files (used in Lecture 1)
Lecture 1 Part 1: Video
Howell ch. 2-3, 4-5
2 8/31 NO HW OR FA DUE  
9/1 Watch video on your own: Introduction to the Online Homework System
Lecture 1, continued
Lecture 1 Part 2: Video
9/3 Lecture 1, continued
Lecture 1 Part 3: Video
3 9/7 HW0 (3 points extra credit) DUE ONLINE BY 11:59 PM  
9/8 Watch videos on your own: Using SAS or STATA on the UIowa Virtual Desktop
9/10 Lecture 1, continued
Lecture 1 Part 4: Video
4 9/14 FA1 DUE VIA ICON BY 11:59 PM  
9/15 Lecture 1, continued
Lecture 1 Part 5: Video

Lecture 1 (outside of class) Part 6: Video
9/17 Lecture 2 and Example 2: Bivariate Association and Significance Testing
Example 2 Data Files (used in Example 2 handout)
Lecture 2 Part 1: Video
Howell ch. 6
Mitchell ch. 2-3
5 9/21 NO HW OR FA DUE  
9/22 Lecture 2 and Example 2, continued
Lecture 2 and Example 2 Part 2: Video
9/24 HW1 (based on Example 1) DUE ONLINE BY 11:59 PM
Lecture 2 and Example 2, continued
Lecture 2 and Example Part 3: Video
Cohen (1994)
6 9/28 FA2 DUE VIA ICON BY 11:59 PM  
9/29 Lecture 2 and Example 2, continued
Lecture 2 and Example Part 4: Video
Spreadsheet for Bivariate Association (for FA2)
10/1 Lecture 2, continued, and discussion using:
This Article, These Effect Size Conversions, and These Power Tables

No class video from today, but I re-did the end of Lecture 2 offline:
Lecture 2 Part 5: Video
7 10/5 NO HW OR FA DUE  
10/6 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)
Article disucssion (at 10/1); Lecture 3 Part 1: Video
Howell ch. 7-9, 11-12
Mitchell ch. 4-5
Williams et al. (2013)
10/8 HW2 (based on Example 2) DUE ONLINE BY 11:59 PM
Watch outside-of-class Lecture 3 Part 2: Video
8 10/12 FA3 DUE VIA ICON BY 11:59 PM  
10/13 Lecture 3 and Example 3, continued
Lecture 3 and Example 3 Part 3: Video
10/15 Lecture 3 and Example 3, continued
Lecture 3 and Example 3 Part 4: Video
9 10/19 NO HW OR FA DUE  
10/20 Lecture 4 and Example 4: GLMs with Multiple Fixed Effects for Each Predictor
Example 4 Data Files (used in Example 4 handout)
Lecture 4 Part 1: Video
Howell ch. 15
Mitchell ch. 6-11
10/22 Graphic for 3-Group Analysis in Example 4
Lecture 4 and Example 4, continued
Lecture 4 and Example 4 Part 2: Video
10 10/26 HW3 (based on Example 3) DUE ONLINE BY 11:59 PM  
10/27 Lecture 4 and Example 4, continued
Lecture 4 and Example 4 Part 3: Video
10/29 Lecture 4 and Example 4, continued
Lecture 4 and Example 4 Part 4 (first 20 min of class): Video
Lecture 4 and Example 4 Part 5 (re-recording of rest of class): Video
11 11/2 FA4 DUE VIA ICON BY 11:59 PM  
11/3 Lecture 4 and Example 4, continued
Lecture 4 and Example 4 Part 6: Video
11/5 Lecture 4 and Example 4, continued
Lecture 4 and Example 4 Part 7: Video
Excel sheet for HW4 (four-group and piecewise coding)
12 11/9 NO HW OR FA DUE  
11/10 Lecture 5, 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
Mitchell ch.14-19
Hoffman ch. 2 sect. 1
11/12 Updated Excel sheet for HW4 (four-group and piecewise coding)
Lecture 5, Example 5a, and Example 5b, continued
Lecture 5 and Example 5a Part 2: Video
13 11/16 HW4 (based on Example 4) DUE ONLINE BY 11:59 PM  
11/17 Lecture 5, Example 5a, and Example 5b, continued
Lecture 5 and Example 5a Part 3: Video
11/19 FA5 DUE VIA ICON BY 11:59 PM WED 11/18
Effect size diagram for FA5
Spreadsheet with R2 example
Lecture 5, Example 5a, and Example 5b, continued
Lecture 5 and Example 5a Part 4: Video

Example 5b (p. 1-11outside of class) Part 1: Video
Example 5b (p. 12+ outside of class) Part 2: Video
14 11/23 NO HW OR FA DUE  
11/24 NO CLASS, but office hours will be held from 12:30-4:45 PM  
15 11/30 NO HW OR FA DUE  
12/1 Lecture 6 and Example 6: GLMs with Single-Slope Interaction Effects
Example 6 Data Files (used in Example 6 handout)
Lecture 6 Part 1: Video
Hoffman ch. 2 sect. 2
12/3 HW5 (based on Example 5a/5b) DUE ONLINE BY 11:59 PM
Lecture 6 and Example 6, continued
16 12/7 FA6 DUE VIA ICON BY 11:59 PM  
12/8 Lecture 6 and Example 6, continued  
12/10 Lecture 6 and Example 6, continued

Bonus Material:
Lecture 7 and Example 7: GLMs with Multiple-Slope Interaction Effects
Example 7 Data Files (used in Example 7 handout)
Hoffman ch. 2 sect. 3+
Howell ch. 13, 16
17 12/18 HW6 (based on Example 6) DUE BY 11:59 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, Materials, and Pre-Requisites:

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, December 18, 2020 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; STATA was previously not available off-campus but it is currently available due to the need for remote work. A second option that allows the use of both SAS and STATA off-campus is the Research Remote Desktop, although this option is not meant for course work. As a third 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.

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 I can use to help 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!