Multilevel Models for Cross Sectional Data: Summer 2013 (ICPSR)

Upcoming Workshops:

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Applied Multilevel Models for Cross Sectional Data
Workshop Syllabus

ICPSR Summer Workshop in Boulder, Colorado
July 15 – 19, 2013

Presented by:

Jonathan Templin, Ph.D.
Associate Professor, Department of Psychology
University of Nebraska-Lincoln

Teaching Assistant:

Ryan Walters, M.S.
Research Analyst and Instructor, Creighton University

Course Description

Multilevel models are powerful statistical models that partition multiple sources variation that may be present due to dependencies in data. Also known as hierarchical linear models mixed effects models, multilevel models extend traditional linear models (such as regression or analysis of variance) to analyses where data structures are clustered, nested, or hierarchical in nature. This workshop presents an introduction to multilevel models featuring their use in cross-sectional analyses. By attending the workshop, participants will gain an understanding of the multilevel modeling approach and will be able to evaluate and conduct basic multilevel model analyses.

The week long workshop will span topics in an integrated framework, with the first day being a review of general linear models beginning with unconditional models and the rules of model comparisons. The second day will feature two-level models: adding random components and adding single predictors, including a discussion of predictor centering techniques. The third and fourth day will be spent on multilevel models with multiple predictors and models with three or more levels. The final day will be spent discussing advanced topics: multilevel models with multivariate predictors and crossed random effects models.

The primary software package used for instruction will be SAS, but some reference examples using SPSS, Mplus, and R will be provided. The course will also include daily opportunities for hands-on practice and individual consultation. Participants should be familiar with ANOVA and regression, but no prior experience with multilevel models or knowledge of advanced mathematics is assumed.

Overall Course Files
Zipped Folder of All Syntax FilesZip File
All Lecture Slides PDFPDF File
Schedule of Topics
DateTopic Comments Page LinkLecture SlidesSyntax FilesData Files
Monday, July 15thIntroduction to Multilevel Models and Hierarchical DataLecture Slides
The General Linear ModelLecture SlidesSAS Syntax
Simple, Marginal, and Interaction Effects in GLMs Lecture SlidesSAS SyntaxSAS Data
Statistical Distribution Assumptions of GLM/Maximum LikelihoodLecture Slides
Lab 1: Introduction to Data Manipulation in SASExample Zipped Folder
Tuesday, July 16thMultilevel Models – a Guiding ExampleLecture SlidesSAS Syntax
SAS Syntax Handout
Data File (CSV)
Centering Predictors and Variance Decomposition Lecture SlidesSAS Syntax
SAS Syntax Handout
SPSS Syntax
SPSS Syntax Handout
Mplus Syntax Handout
Data File (CSV)
Random Slopes, Cross-Level Interactions, InterpretationsLecture SlidesSAS SyntaxData File (CSV)
Lab 2: Fitting Single-Predictor Multilevel ModelsExample Zipped Folder
Wednesday, July 17thComprehensive Overview of Multilevel ModelsLecture Slides
Two-Level Clustered Data – Students within SchoolsSAS Syntax Handout
SPSS and STATA Syntax Handout
Two-Level Cross-Classified Data ModelsSAS Syntax Handout
Three Level Models (Part 1)Lecture SlidesSAS Syntax
Lab 3: Fitting Multi-Predictor Multilevel ModelsExample Zipped Folder
Thursday, July 18thThree Level Models (Part 2): Clustered Longitudinal DesignsLecture Slides
Three Level Models ExampleSAS Syntax Handout
SPSS and STATA Syntax Handout
Multivariate Normal Distribution and Multivariate AnalysesLecture SlidesSAS Syntax
Multilevel Models in Matrix FormLecture Slides
Lab 4: Multivariate ModelsExample Zipped Folder
Friday, July 19thMultivariate Multilevel ModelsLecture SlidesSAS Syntax
Generalized Multilevel Models (Non-Normal Outcomes)Lecture SlidesSAS Syntax
Generalized Multivariate Multilevel ModelsLecture SlidesSAS Syntax