|Instructor:||Professor Lesa Hoffman (she, her, hers)|
Educational Measurement and Statistics Program
|Department:||Psychological and Quantitative Foundations
Office: 361 Lindquist Center (South); DEO: Dr. Megan Foley Nicpon
|Instructor Office:||356 Lindquist Center (South)||Instructor Email:||Lesa-Hoffman@UIowa.edu|
|Course Room:||North 166 Lindquist Center||Course Time:
|Tuesdays and Thursdays 12:30–1:45 PM
Tuesdays and Thursdays 3:15-4:15 PM in the course room
|Course Textbook:||see info below||SAS Resources:||Lesa's SAS guide from PilesOfVariance.com
SAS MIXED Online Manual
|Online Homework:||Homework Portal (coming soon)||Stata Resources:||Lesa's Stata guide from PilesOfVariance.com
Stata MIXED Online Manual
Topics and Course Materials
|1||8/26||NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE|
|8/27||Topic 1: Course Introduction; Research and Data Terminology||Howell ch. 1
Mitchell ch. 1
|8/29||Topic 2: Univariate Data||Howell ch. 2-3|
|2||9/2||FA1 DUE VIA INCON BY 11:59 PM|
|9/3||HW0 DUE ONLINE BY 11:59 PM: 3 POINTS EXTRA CREDIT
Topic 2: Univariate Data
|Howell ch. 5|
|9/5||Topic 3: Significance Testing and Bivariate Association||Howell ch. 4
Mitchell ch. 2-3
|3||9/9||HW1 DUE ONLINE BY 11:59 PM|
|9/10||Topic 3 continued||Howell ch. 6|
|9/12||NO CLASS OR OFFICE HOURS|
|4||9/16||NO HW OR FA DUE|
|9/17||Topic 4: Introduction to General Linear Models (GLMs)||Hoffman ch. 2 sect. 1|
|9/19||Topic 4 continued||Howell ch. 7-9|
|5||9/23||HW2 DUE ONLINE BY 11:59 PM|
|9/24||Topic 4 continued||Howell ch. 11-12|
|9/26||Topic 4 continued||Mitchell ch. 4-5|
|6||9/30||FA2 DUE VIA INCON BY 11:59 PM|
|10/1||Topic 5: GLMs with Multiple Predictors||Howell ch. 15|
|10/3||Topic 5 continued||Mitchell ch. 6-9|
|7||10/7||HW3 DUE ONLINE BY 11:59 PM|
|10/8||Topic 5 continued||Mitchell ch. 10-11; 14-19|
|10/10||NO CLASS OR OFFICE HOURS|
|8||10/14||FA3 DUE VIA INCON BY 11:59 PM|
|10/15||Topic 6: GLMs with Ambiguous Predictor Types|
|10/17||Topic 6 continued|
|9||10/21||FA4 DUE VIA INCON BY 11:59 PM|
|10/22||Topic 7: GLMs with Interactions Among Continuous Predictors||Hoffman ch. 2 sect. 2|
|10/24||Topic 7 continued|
|10||10/28||HW4 DUE ONLINE BY 11:59 PM|
|10/29||Topic 7 continued|
|10/31||Topic 8: GLMs with Interactions Among Categorical Predictors||Hoffman ch. 2 sect. 3+|
|11||11/4||FA5 DUE VIA INCON BY 11:59 PM|
|11/5||Topic 8 continued||Howell ch. 13|
|11/7||Topic 8 continued|
|12||11/11||HW5 DUE ONLINE BY 11:59 PM|
|11/12||Topic 9: GLMs with Interactions of Continuous with Categorical Predictors||Howell ch. 16|
|11/14||Topic 9 continued|
|13||11/18||FA6 DUE VIA INCON BY 11:59 PM|
|11/19||Topic 10: Introduction to Multivariate GLMs||Hoffman ch. 3 sec. 1-2|
|11/21||Topic 10 continued||Howell ch. 14
Mitchell ch. 12-13
|14||11/25||NO HW OR FA DUE|
|11/26||NO CLASS OR OFFICE HOURS|
|11/27||NO CLASS OR OFFICE HOURS|
|15||12/2||NO HW OR FA DUE|
|12/3||Topic 10 continued||Hoffman ch. 3 sec. 3+|
|12/5||Topic 11: GLMs with Nested Effects and Heterogeneity of Variance|
|16||12/9||FA6 DUE VIA INCON BY 11:59 PM|
|12/10||Topic 11 continued|
|12/12||Topic 12: Sales Pitch for Advanced Classes|
|17||12/20||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 general linear models models (i.e., regression, analysis of variance, analysis of covariance) for the analysis of univariate and multivariate data. The course is organized to take participants through each of the cumulative steps in a statistical analysis: deciding which type of model is appropriate, organizing the data and creating predictor variables, building models to predict outcome variation, 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 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. Book chapters and journal articles will be assigned for each specific topic as needed; the initial list of readings below may be updated later. There will be no exams nor any required attendance outside the regular class time. However, because the course will have an applied focus requiring the use of statistical software, instructor office hours will also be held in a group-based format, in which multiple participants will have opportunities to work on course assignments simultaneously and receive immediate assistance. Participants should be comfortable with basic concepts of research prior to enrolling in this course.
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.
In order to be able to provide the entire class 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.
>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 my 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. All course participants—enrolled students and auditors—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.
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.
Howell, D. C. (2010). Statistical methods for psychology (7 th ed). Belmont, CA: Cengage Wadsworth.
Mitchell, M. N. (2015). Stata for the behavioral sciences. College Station, TX: Stata Press.