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:  LesaHoffman@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 3:154:15 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 
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. 12  
8/29  Lecture 2a: Review of SingleLevel 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 TwoLevel Nested Data Example 3a: Fixed Effects in General MLMs for TwoLevel Nested Data (Data, Syntax, and Output) Lecture 3a and Example 3a Part 1: Video 
S & B ch. 34  
9/26  Lecture 3a and Example 3a, continued 

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 TwoLevel Nested Data Example 3b: Fixed and Random Effects in General MLMs for TwoLevel Nested Data (Spreadsheets, Syntax, and Output) Lecture 3a Part 5, Lecture 3b and Example 3b Part 1: Video 
S & B ch. 57 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. 35 
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 TwoLevel CrossClassified Data Example 4: General MLMs for TwoLevel 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. 1112 

11  11/4  FA5 DUE VIA ICON BY 11:59 PM  
11/5  Effect Size Conversions Lecture 5: Generalized MLMs for TwoLevel 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. 67  
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: ThreeLevel 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 
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 multilevel models (i.e., general linear mixedeffect 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 groupbased 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.
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 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.
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 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.
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 outsideofclass 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.
In order to be able to provide the entire class with prompt feedback, homework assignments submitted any time after the deadline 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 formative assessments 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 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. Please note that Stata is only available when using the Virtual Desktop on campus, whereas SAS is available remotely as well.
S & B: Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: Sage.
Bauer, D. (2009). A note on comparing the estimates of models for clustercorrelated or longitudinal data with binary or ordinal outcomes. Psychometrika, 74(1), 97105.
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. 509532). Hoboken, NJ: Wiley.
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.
Hoffman, L. (2015). Longitudinal analysis: Modeling withinperson 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), 288311.
Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed). New York, NY: Routledge Academic.
Nakagawa, S., & Schielzeth, H. (2010). Repeatability for Gaussian and nonGaussian data: A practical guide for biologists. Biological Reviews, 85, 935956.
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 Rsquared measures. Psychological Methods, 24(3), 309338.
Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8(2), 185205.