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 
Instructor Office:  South 356 Lindquist Center  Instructor Email:  LesaHoffman@UIowa.edu 
Course Room and Time:  North 166 Lindquist Center (N166 LC) Tuesdays and Thursdays 2:00–3:15 PM 
Office Hours in N166 LC:  Tuesdays and Thursdays 3:154:15 PM 
Zoom Meeting Link:  https://uiowa.zoom.us/my/lesahoffmaniowa  SAS Resources: 
Lesa's SAS guide from PilesOfVariance.com SAS MIXED Online Manual 
Online Homework:  Homework Portal now available!  Stata Resources: 
Lesa's Stata guide from PilesOfVariance.com
Stata MIXED Online Manual 
Week 
Date 
Topics and Course Materials 
Readings 

1  1/20  NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
1/21  Lecture 0: Introduction to this Course and to Maximum Likelihood Estimation of General Linear Models Supplemental Excel and SAS Files For review of General Linear Models with interaction terms, please visit the material from weeks 1216 of PSQF 6242 Fall 2019 Lecture 0 Part 1: Video 
Agresti ch. 13 Hoffman ch. 2 Enders ch. 3 

1/23  Lecture 0, continued Bonus Example 0 Intro to the Homework System and Lecture 0 Part 2: Video 

2  1/27  HW0 DUE ONLINE BY 11:59 PM: 3 POINTS EXTRA CREDIT  
1/28  Lecture 0, continued Lecture 0 Part 3: Video 

1/30  Lecture 1 and Example 1 (now complete): Generalized Linear Models for Binary and Categorical Outcomes Supplemental STATA, SAS, and Excel Files Lecture 1 and Example 1 Part 1: Video 
Agresti ch. 46 Hardin & Hilbe ch. 2, 9, 15, 16 Hsieh (1989) Mize (2019) Williams (2016) 

3  2/3  FA1 DUE VIA ICON BY 11:59 PM  
2/4  Lecture 1 and Example 1, continued Lecture 1 and Example 1 Part 2: Video 

2/6  Lecture 1 and Example 1, continued Lecture 1 and Example 1 Part 3: Video 

4  2/11  Lecture 1 and Example 1, continued Lecture 1 and Example 1 Part 4: Video 
Agresti ch. 7 Hardin & Hilbe ch. 34, 1214 
2/12 
AtHome Checklist for Getting the UIowa Virtual Desktop to Work (from PSQF 6242) Steps to Completing Homework (from PSQF 6242) HW1 DUE ONLINE BY 11:59 PM 

2/13  Lecture 2 and Example 2: Generalized Linear Models for Count and ZeroInflated Count Outcomes Example 2 Supplemental STATA, SAS, and Excel Files Lecture 2 and Example 2 Part 1: Video 

5  2/17  FA2 DUE VIA ICON BY 11:59 PM  
2/18  Lecture 2 and Example 2, continued Lecture 2 and Example 2 Part 2: Video 

2/20  Lecture 2 and Example 2, continued Lecture 2 and Example 2 Part 3: Video 

6  2/25  Lecture 3: Models for Other Kinds of NonNormal Outcomes Example 3a: Generalized Linear Models for Proportions (Binomial Outcomes) Example 3a Supplemental STATA and SAS Files Example 3b: Models for Positive Skewed Outcomes Example 3b Supplemental STATA, SAS, and Excel Files Lecture 3 and Example 3a Part 1: Video 
Agresti ch. 4, 8 Hardin & Hilbe ch. 5, 10 Hardin & Hilbe (2014) Konstantopoulos et al. (2019) 
2/27  Lecture 3 and Example 3a, continued Lecture 3 and Example 3a Part 2: Video 

2/28  HW2 DUE ONLINE BY 11:59 PM  
7  3/3  Lecture 3 and Example 3b, continued  
3/4  FA3 DUE VIA ICON BY 11:59 PM  
3/5  Lecture 3 and Example 3, continued  
8  3/9  HW3 DUE ONLINE BY 11:59 PM  
3/10  Lecture 4 and Example 4: General Linear Models for Multivariate and Repeated Measures Outcomes  Hoffman ch. 3  
3/12  Lecture 4 and Example 4, continued  
9  3/16  NO HW OR FA DUE  
3/17  NO CLASS OR OFFICE HOURS  
3/19  NO CLASS OR OFFICE HOURS  
10  3/23  NO HW OR FA DUE  
3/24  Lecture 4 and Example 4, continued  
3/26  CLASS AND OFFICE HOURS WILL BE HELD IN N116 (PDA) Lecture 4 and Example 4, continued 

11  3/30  FA4 DUE VIA ICON BY 11:59 PM  
3/31  Lecture 5 and Example 5: Generalized Linear Models for Multivariate and Repeated Measures Outcomes  Agresti ch. 9 Hardin & Hilbe ch. 1819 

4/2  Lecture 5 and Example 5, continued  
12  4/6  HW4 DUE ONLINE BY 11:59 PM  
4/7  Lecture 5 and Example 5, continued 

4/9  Lecture 5 and Example 5, continued  
13  4/13  FA5 DUE VIA ICON BY 11:59 PM  
4/14  Lecture 6 and Example 6: General Linear Models within Path Analysis  Enders ch. 45  
4/16  Lecture 6 and Example 6, continued  
14  4/20  HW5 DUE ONLINE BY 11:59 PM  
4/21  Lecture 6 and Example 6, continued  
4/23  Lecture 6 and Example 6, continued  
15  4/27  FA6 DUE VIA ICON BY 11:59 PM  
4/28  Lecture 7 and Example 7: Generalized Linear Models within Path Analysis  TBD  
4/30  Lecture 7 and Example 7, continued  
16  5/4  FA7 DUE VIA ICON BY 11:59 PM  
5/5  Lecture 7 and Example 7, continued 

5/7  Lecture 7 and Example 7, continued Time for Course Evaluations 

17  5/15  HW6 DUE BY 11:59 PM ONLINE ALL OUTSTANDING WORK MUST BE COMPLETED BY 11:59 PM 
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 course materials.
This course will illustrate the uses of generalized linear models for predicting univariate and multivariate outcomes. The course is organized to take participants through each of the cumulative steps in a statistical analysis: deciding which type of model is appropriate, creating predictor variables, building models to evaluate unique effects of predictors, and interpreting and presenting empirical findings. Class time will be devoted primarily to lectures and examples. Lecture materials will be available for download at the website above no later than the day prior to class, or else paper copies can be requested. Video recordings of the class lectures will also be available online but are not intended to take the place of class attendance (either in person or via zoom). Readings will be assigned for each specific topic as needed; the initial list of readings below may be updated later. There will be no required sessions held outside the regular class time noted above. However, because the course will have an applied focus requiring the use of statistical software, participants are encouraged to attend groupbased office hours, in which multiple participants will have opportunities to work on course assignments simultaneously and receive immediate assistance. Participants should be comfortable with general linear models (e.g., analysis of variance and linear regression) prior to enrolling in this course. Auditors and visitors are always welcome.
Participants will have the opportunity to earn up to 100 total points in this course. Up to 86 points can be earned from homework assignments (approximately 6 in total)—these will be graded for accuracy. Up to 14 points may be earned from submitting outsideofclass formative assessments (approximately 7 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 3point 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 outsideofclass quizzes will incur a 1point penalty when submitted. A final grade of “incomplete” will only be given in dire circumstances and entirely at the instructor's discretion.
>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−, 67–69 = D+, 63–66 = D, 60–62 = D−, <60 = F
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).
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 (in person or over email, as you prefer) 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!
Participants will also need to have access to software that can estimate the models presented. Although the course will feature SAS, Stata, and Mplus primarily, other software packages (e.g., SPSS, R) may potentially also be used to complete homework assignments. These packages are freely available to University of Iowa members through the UIowa Virtual Desktop.
Agresti, A. (2015). Foundations of linear and generalized linear models (1st ed.). Hoboken, NJ: Wiley & Sons.
Hardin, J. W. & Hilbe, J. M. (2018). Generalized linear models and extensions (4th ed.). College Station, TX: STATA Press.
Hardin, J. W., & Hilbe, J. M. (2014). Estimation and Testing of Binomial and BetaBinomial Regression Models with and without Zero Inflation. The Stata Journal, 14 (2), 292–303.
Hsieh, F. Y. (1989). Sample size tables for logistic regression. Statistics in Medicine, 8, 795802.
Hoffman, L. (2015 chapters 23). Longitudinal analysis: Modeling withinperson fluctuation and change. New York, NY: Routledge Academic.
Enders, C. K. (2010; chapters 3–5). Applied missing data analysis. New York, NY: Guilford.
Konstantopoulos, S., Li, W., Miller, S., & van der Ploeg, A. (2019). Using Quantile Regression to Estimate Intervention Effects Beyond the Mean. Educational and Psychological Measurement, 79(5), 883–910.
Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science 6, 81117.
Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40, 720.
Other readings To Be Determined (TBD)...