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Instructor:



GTA:
Professor Lesa Hoffman (she, her, hers)
Educational Measurement and Statistics Program

Cassie Olmstead (she, her, hers)
Doctoral Student, Educational Psychology
and Learning Sciences Program


Department:
Psychological and Quantitative Foundations
Office: South 361 Lindquist Center
DEO: Dr. Megan Foley Nicpon
Instructor Office:
GTA Office:
South 356 Lindquist Center
North 468 Lindquist Center
Instructor Email:
GTA Email:
Lesa-Hoffman@UIowa.edu
Cassandra-Olmstead@UIowa.edu
Course Room and Time: North 166 Lindquist Center (N166 LC)
Tuesdays and Thursdays 12:30–1:45 PM
Office Hours
in N166 LC:
Instructor: Tuesdays and Thursdays 3:15-4:15 PM
GTA: Wednesdays 2:30-4:30 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

Planned Schedule of Events (Printable Syllabus; last updated 1/11/2020)

S

Week

Date

Topics and Course Materials

Readings for Each Unit

1 1/20 NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
1/21 Lecture 0: Introduction to this Course and to Quantitative Methods
Lecture 0 Part 1: Video
Howell ch. 1
Mitchell ch. 1
1/23 Lecture 0, continued
Intro to Homework System and Lecture 0 Part 2: Video
Howell ch. 2-3, 4-5
       
2 1/27 HW0 DUE ONLINE BY 11:59 PM: 3 POINTS EXTRA CREDIT  
1/28 Lecture 1 and
Example 1
: Univariate Data Description; Introduction to using SAS and STATA
At-Home Checklist for Getting the UIowa Virtual Desktop to Work

Lecture 1 Part 1: Video
 
1/30 Lecture 1, continued
Lecture 1 Part 1: Video
Getting Ready for Example 1
 
       
3 2/3 FA1 DUE VIA ICON BY 11:59 PM  
2/4 Going Over FA1: Video
Introduction to SAS and STATA via Example 1 (no video)
 
2/6 Introduction to SAS and STATA via Example 1: Video
 
       
4 2/11 Lecture 2 and Example 2: (updated 2/26/20)
Significance Testing in the Context of Univariate Statistics and Bivariate Association
Example 2 SAS and STATA Files
Lecture 2 and Example 2 Part 1: Video
Howell ch. 6
Mitchell ch. 2-3
2/12 Steps to Completing Homework
HW1 DUE ONLINE BY 11:59 PM
 
2/13 Lecture 2 and Example 2, continued
Lecture 2 and Example 2 Part 2: 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 3: Video
 
2/20 Lecture 2 and Example 2, continued
Lecture 2 and Example 2 Part 4: Video
 
       
6 2/24 NO HW OR FA DUE  
2/25 Lecture 2 and Example 2, continued
Lecture 2 and Example 2 Part 5: Video
 
2/27 Lecture 2 and Example 2, continued
Review and Discussion Using This Article, Power Tables, and Effect Size Conversion Table
Lecture 2 and Example 2 Part 6: Video

       
7 3/3 Lecture 3 and Example 3: General Linear Models with a Single Fixed Effect for a Single Predictor
Example 3 SAS and STATA Files
Howell ch. 7-9, 11-12;
Mitchell ch. 4-5
3/5 Lecture 3 and Example 3, continued
 
3/6 HW2 DUE ONLINE BY 11:59 PM  
       
8 3/10 Lecture 4 and Example 4: General Linear Models with Multiple Fixed Effects for a Single Predictor Howell ch. 15
Mitchell ch. 6-11
3/11 FA3 DUE VIA ICON BY 11:59 PM  
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 3/24 Lecture 4 and Example 4, contisnued  
3/25 HW3 DUE ONLINE BY 11:59 PM
NO OFFICE HOURS
 
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: General Linear Models with Multiple Predictors Mitchell ch. 14-19
Hoffman ch. 2 sect. 1
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 with Single-Slope Interaction Effects Hoffman ch. 2 sect. 2
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: General Linear Models with Multiple-Slope Interaction Effects Hoffman ch. 2 sect. 3+
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
 

Schedule of Topics and Events:

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.

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, deciding which type of model is appropriate, creating predictor variables, building models to evaluate unique predictors of univariate outcomes, 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 basic concepts of research prior to enrolling in this course. Auditors and visitors are always welcome.

Course Requirements:

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.

Policy on Late Homework Assignments and Incompletes:

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.

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−, 67–69 = D+, 63–66 = D, 60–62 = D−, <60 = F

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).

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 (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!

Course Software:

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.

Course Readings (available via "Files" in Icon):

Hoffman, L. (2015 chapter 2). 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.

Optional Course Textbook (for future Stata reference):

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