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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:

Office Hours:
Course: Tuesdays and Thursdays 2:00–3:15 PM
Office Hours: Tuesdays and Thursdays 3:15-4:15 PM
in N166 LC or 356 LC
Course Textbook: Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling 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 12/21/2019)

Week

Date

Topics and Course Materials

Readings

1 8/26 NO HOMEWORK (HW) OR FORMATIVE ASSESSMENTS (FA) DUE  
8/27 Course Introduction
Lecture 1: Introduction to Multilevel Models (MLMs)
Lecture 1: Video
S & B ch. 1-2
8/29 Lecture 2a: Review of Single-Level General Linear Models
Lecture 2a Part 1: Video
Hoffman (2015) ch. 2 sec. 1
       
2 9/2 FA1 DUE VIA ICON BY 11:59 PM  
9/3 Lecture 2a, continued
Example 2a: Review of General Linear Models in SAS and STATA
Data, Syntax, and Output for Example 2a, 2b, and 2c
Example 2a Part 1: Video
 
9/5 HW0 DUE ONLINE BY 11:59 PM FOR 3 POINTS EXTRA CREDIT
Lecture 2a and Example 2a, continued
Example 2a Part 2: Video
Lecture 2b: Interactions among Continuous Predictors
Lecture 2b Part 1: Video
Hoffman (2015) ch. 2 sec. 2
       
3 9/9 NO HW OR FA DUE  
9/10 Lecture 2b, continued
Example 2b: Interactions among Continuous Predictors in SAS and STATA
Lecture 2b Part 2 and Example 2b: Video
 
9/12 NO CLASS OR OFFICE HOURS  
       
4 9/16 FA2 DUE VIA ICON BY 11:59 PM  
9/17 Lecture 2c: Interactions among Categorical Predictors
Example 2c: Interactions among Categorical Predictors in SAS and STATA
Lecture 2c and Example 2c Part 1: Video
Hoffman (2015) ch. 2 sec. 3+
9/19 Lecture 2c and Example 2c, continued
Lecture 2c and Example 2c Part 2: Video
Open lab time for HW1
 
       
5 9/23 HW1 DUE ONLINE BY 11:59 PM
 
9/24 Lecture 3a: Fixed Effects in General MLMs for Two-Level Nested Data
Example 3a: Fixed Effects in General MLMs for Two-Level Nested Data (Data, Syntax, and Output)
Lecture 3a and Example 3a Part 1: Video
S & B ch. 3-4
9/26

Lecture 3a and Example 3a, continued
Lecture 3a and Example 3a Part 2: Video

 
       
6 9/30 FA3 DUE VIA ICON BY 11:59 PM  
10/1 Lecture 3a and Example 3a, continued
Lecture 3a and Example 3a Part 3: Video
 
10/3 Lecture 3a and Example 3a, continued
Lecture 3a and Example 3a Part 4: Video
 
       
7 10/7 HW2 DUE ONLINE BY 11:59 PM
 
10/8 Lecture 3a, continued
Lecture 3b: Fixed and Random Effects in General MLMs for Two-Level Nested Data
Example 3b: Fixed and Random Effects in General MLMs for Two-Level Nested Data (Spreadsheets, Syntax, and Output)
Lecture 3a Part 5, Lecture 3b and Example 3b Part 1: Video
S & B ch. 5-7
Raudenbush & Bryk (2002) ch. 5
10/10 NO CLASS OR OFFICE HOURS  
       
8 10/14 FA4 DUE VIA ICON BY 11:59 PM  
10/15 Lecture 3b and Example 3b, continued
Lecture 3b and Example 3b Part 2: Video
Rights & Sterba (2019)
10/17 Lecture 3b and Example 3b, continued
Lecture 3b and Example 3b Part 3: Video
Hoffman (2019)
       
9 10/22 Lecture 3b and Example 3b, continued
Review; Lecture 3b and Example 3b Part 4: Video
Enders (2010) ch. 3-5
10/23 NO HW OR FA DUE  
10/24 NO CLASS OR OFFICE HOURS  
       
10 10/29 Lecture 3b and Example 3b, continued
Lecture 3b and Example 3b Part 5: Video
 
10/30 HW3 DUE ONLINE BY 11:59 PM  
10/31 Lecture 4: General MLMs for Two-Level Cross-Classified Data
Example 4: General MLMs for Two-Level Crossed Schools (Spreadsheets, Data, Syntax, and Output)
Lecture 4 and Example 4: Video

Bonus Example for Changes in Nesting over Time (Hoffman, 2015 11b): (SAS) (STATA)
Bonus Example for Subjects Crossed with Items: See Example 4 from 2018 Illinois Workshop
S & B ch. 13
Raudenbush & Bryk (2002) ch. 12
Hoffman (2015) ch. 11-12
       
11 11/4 FA5 DUE VIA ICON BY 11:59 PM  
11/5 Effect Size Conversions
Lecture 5: Generalized MLMs for Two-Level Nested Data
Review; Lecture 5 Part 1: Video
DeMaris (2003)
11/7 Lecture 5 continued
Example 5a: MLM for Clustered Binary Outcomes (Spreadsheets, Syntax, and Output)
Lecture 5 and Example 5a Part 2: Video

Bonus Example for Ordinal Longitudinal Outcomes: Example 7b from CLDP 945
Bonus Example for Binomial Longitudinal Outcomes: Example 7c from CLDP 945
S & B ch. 10, 17
Bauer (2009)
       
12 11/11 FA6 DUE VIA ICON BY 11:59 PM  
11/12 Lecture 5 and Example 5a, continued
Lecture 5 and Example 5a Part 3: Video
Hox (2010) ch. 6-7
11/14 Lecture 5, continued
Example 5b: MLM for Clustered Count Outcomes (Spreadsheets, Data, Syntax, and Output)
Lecture 5 and Example 5b Part 4: Video
Nakagawa & Schielzeth (2010)
       
13 11/19 Lecture 6: MLMs for Subjects Crossed with Items (Explanatory IRT)
Example 6: Explanatory IRT Models as Crossed Random Effects Models
(Spreadsheets, Syntax, and Output)
Lecture 6 and Example 6: Video
Rijmen et al. (2003)
11/21 NO CLASS OR OFFICE HOURS  
11/22 HW4 (SAS) OR HW5 (STATA) DUE ONLINE BY 11:59 PM  
       
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 Lecture 7: A Crash Course in Multilevel Models for Longitudinal Data
Lecture 7 Part 1: Video
Hoffman (2015) ch. 6
12/5 Lecture 7, continued
Lecture 8: Three-Level Random Effects Models
Lecture 7 Part 2 and Lecture 8 Part 1: Video
Hoffman (2015) ch. 11
       
16 12/9 FA7 DUE VIA ICON BY 11:59 PM  
12/10 Lecture 8, continued
Example 8: Longitudinal Twin Models (Spreadsheet)
Lecture 8 and Example 8 Part 2: Video
 
12/12 Lecture 8 and Example 8, continued
Lecture 8 and Example 8 Part 3: Video
Time for Course Evaluations
 
       
17 12/20 HW6 (SAS) OR HW7 (STATA) DUE BY 11:59 PM ONLINE
ALL OUTSTANDING WORK MUST BE SUBMITTED BY 11:59 PM FOR COURSE CREDIT
 

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 multilevel models (i.e., general linear mixed-effect models, hierarchical linear models) for the analysis of clustered data (persons nested in groups). The course is organized to take participants through each of the cumulative steps in a multilevel analysis: deciding which type of model is appropriate, organizing the data and creating predictor variables, testing fixed and random effects, predicting multiple sources of 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 estimating and interpreting general linear models (i.e., analysis of variance, regression) prior to enrolling in this course.

Course Requirements:

INITIAL PLAN: 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.

REVISED 10/21/19: Participants will have the opportunity to earn up to 85 total points in this course. Up to 71 points can be earned from homework assignments (5 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.

REVISED 12/21/19: Participants will have the opportunity to earn up to 82 total points in this course. Up to 68 points can be earned from homework assignments (5 in total)—these will be graded for accuracy. Up to 14 points may be earned from submitting outside-of-class formative assessments (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 be able to provide the entire class with prompt feedback, homework assignments submitted any time after the deadline 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 formative assessments 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 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).

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. Please note that Stata is only available when using the Virtual Desktop on campus, whereas SAS is available remotely as well.

Course Textbook (to be purchased):

S & B: Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.

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

Bauer, D. (2009). A note on comparing the estimates of models for cluster-correlated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74(1), 97-105.

DeMaris, A. (2003). Logistic regression. In I. B. Weiner, D. K. Freedheim, J. A. Shinka, & W. F. Velicer (Eds.) Handbook of Psychology, Research Methods in Psychology (pp. 509-532). Hoboken, NJ: Wiley.

Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.

Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.

Hoffman, L. (2019). On the interpretation of parameters in multivariate multilevel models across different combinations of model specification and estimation. Advances in Methods and Practices in Psychological Science, 2(3), 288-311.

Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed). New York, NY: Routledge Academic.

Nakagawa, S., & Schielzeth, H. (2010). Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biological Reviews, 85, 935-956.

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309-338.

Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185-205.