|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:||Lesa-Hoffman@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 3:15-4:15 PM|
|Zoom Meeting Link:||(zoom link removed)||SAS Resources:||
Lesa's SAS guide from PilesOfVariance.com
SAS MIXED Online Manual
|Online Homework:||Homework Portal still available||Stata Resources:||
Lesa's Stata guide from PilesOfVariance.com
Stata MIXED Online Manual
Lesa's Mplus guide from PilesOfVariance.com
Mplus Online Manual
Topics and Course Materials
|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 12-16 of PSQF 6242 Fall 2019
Lecture 0 Part 1: Video
| Agresti ch. 1-3
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. 4-6
Hardin & Hilbe ch. 2, 9, 15, 16
|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. 3-4, 12-14
At-Home 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 Zero-Inflated 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 Non-Normal Outcomes
Example 3a: Generalized Linear Models for Proportions (Binomial Outcomes)
Example 3a Supplemental STATA and SAS Files
Example 3b: Models for Positively 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
Lecture 3 and Example 3b Part 3: Video
|3/4||FA3 DUE VIA ICON BY 11:59 PM|
|3/5||Lecture 3 and Example 3b, continued
Lecture 3 and Example 3b Part 4: Video
|8||3/10||Lecture 4: General Linear Models for Multivariate and Repeated Measures Outcomes
Example 4a: Multivariate General Linear Models for Repeated Measures
Example 4a Supplemental STATA, SAS, and Excel Files
Lecture 4 and Example 4a Part 1: Video
|Hoffman ch. 3|
|3/12||Open time to work on Homework 3|
|3/13||HW3 DUE ONLINE BY 11:59 PM|
|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||NO CLASS OR OFFICE HOURS|
|3/26||NO CLASS OR OFFICE HOURS
|CLASS AND OFFICE HOURS WILL BE HELD ON ZOOM ONLY FROM THIS POINT FORWARD|
|11||3/30||NO HW OR FA DUE|
|3/31||Newer versions of Lecture 4 and Example 4a last updated 3/26/20
Lecture 4: General Linear Models for Multivariate and Repeated Measures Outcomes
Lecture 4 Review: Video
Example 4a: Multivariate General Linear Models for Repeated Measures
Example 4a Supplemental STATA, SAS, and Excel Files
|4/2||Lecture 4 and Example 4a, continued
Lecture 4 and Example 4a Part 2: Video (sorry, audio is lost after 19 min for no apparent reason)
|12||4/6||FA4 DUE VIA ICON BY 11:59 PM (RECOMMENDED)|
|4/7|| FA4 Discussion: Video
Lecture 4 and Example 4a, continued: Example 4a In-Class Result
Lecture 4 and Example 4a Part 3: Video
|4/9|| Lecture 4, continued
Example 4b Part 1: Multivariate General Linear Models for Family (Triadic) Data
Example 4b Supplemental STATA, SAS, and Mplus Files
Lecture 4 and Example 4b: Video
|13||4/13||FA5 DUE VIA ICON BY 11:59 PM (RECOMMENDED)|
Part 1: Multivariate General and Generalized Linear Models for Difference Scores
Example 5a Supplemental STATA, SAS, and Mplus Files
Example 5a Part 1: Video
|4/16|| Example 5a, continued
Example 5a Part 2: Video
|14||4/20||NO HW OR FA DUE|
|4/21||Lecture 5: Generalized Linear Models for Multivariate and Repeated Measures Outcomes
Lecture 5: Video
(Example 5b cancelled for lack of time)
|Agresti ch. 9
Hardin & Hilbe ch. 18-19
|4/23||Open time to work on HW4 during class (no lecture)|
|15||4/27||HW4 DUE ONLINE BY 11:59 PM (RECOMMENDED)|
|4/28|| Lecture 6: General and Generalized Linear Models within Path Analysis
Sorry, no video today :(
|Enders ch. 4-5
MacKinnon (2008) ch. 6
|4/30||Lecture 6, continued:
Part 1 Video
Part 2 Video
Example 4b Part 2: Path Models for Family (Triadic) Data (see Example 4b posted for 4/9/20)
Part 3 Video
|16||5/4||FA6 DUE VIA ICON BY 11:59 PM (RECOMMENDED)|
|5/5||Lecture 6 and Example 4b, continued
Lecture 6, Example 4b Part 4: Video
Example 5a Part 2: Path Models for Difference Scores (see Example 5a posted for 4/14/20) not presented for lack of time
|5/7||Lecture 6, continued
Example 6a: Path Models for Mediation with Conditionally Normal Outcomes (updated 4/29/20)
Example 6a Supplemental STATA, SAS, and Mplus Files
Example 6b: Path Models for Mediation with Binary Outcomes
Example 6b Supplemental STATA and Mplus Files
Example 6a and Example 6b: Video
|17||5/11||OFFICE HOURS 2:00-4:00|
|5/12||OFFICE HOURS 1:00-4:00|
|5/13||OFFICE HOURS 2:00-4:00|
|5/14||OFFICE HOURS 1:00-4:00|
|5/15||OFFICE HOURS 2:00-3:30
HW5 DUE BY 11:59 PM ONLINE
ALL OUTSTANDING WORK MUST BE COMPLETED BY 11:59 PM (HARD DEADLINE)
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 group-based 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 outside-of-class 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.
REVISION for Weeks 11-16: Given the transition to online instruction and loss of one academic week, the coursework has been revised as follows: Participants will now have the opportunity to earn up to 82 total points in this course. Up to 70 points can be earned from homework assignments (5 in total)—these will be graded for accuracy. Up to 12 points may be earned from submitting outside-of-class formative assessments (6 in total); these will be graded on effort only—incorrect answers will not be penalized. Extra credit opportunities have not been changed.
In order to provide participants with prompt feedback, late homework assignments will incur a 3-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 1-point penalty when submitted. A final grade of “incomplete” will only be given in dire circumstances and entirely at the instructor's discretion.
REVISION for Weeks 11-16: Given the general chaos of our world right now, the penalty for late assignments will be removed effective 3/30/20, but participants are still encouraged to complete homework and formative assessments by the recommended due dates. However, the hard deadline for the end of the semester must remain intact: all outstanding work must be submitted by Friday, May 15, 2020 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
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 and STATA primarily, other software packages (e.g., SPSS, R) can also be used to complete homework assignments. These packages are freely available to University of Iowa members through the UIowa Virtual Desktop but STATA is not available off-campus. Another option that allows the use of both SAS and Stata off-campus is the Research Remote Desktop. See info here: https://its.uiowa.edu/rrds.
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 Beta-Binomial 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, 795-802.
Hoffman, L. (2015 chapters 2-3). Longitudinal analysis: Modeling within-person 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.
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Routledge Academic.
Mize, T. (2019). Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociological Science 6, 81-117.
Williams, R. (2016). Understanding and interpreting generalized ordered logit models. The Journal of Mathematical Sociology, 40, 7-20.
Other readings To Be Determined (TBD)...