Mixed and Multilevel Models

Online Workshops

• Analyzing Repeated Measures Data: ANOVA and Mixed Model Approaches

• Introduction to Generalized Linear Mixed Models

The Craft of Statistical Analysis

• Random Intercept and Random Slope Models

• Fixed and Random Factors in Mixed Models

Statistically Speaking Trainings

• Crossed and Nested Factors

• Latent Growth Curve Models

• Matrix Algebra for Data Analysts: A Primer

• Power Analysis and Sample Size Determination Using Simulation

• The Unstructured Covariance Matrix: When It Does and Doesn’t Work

• Covariance Matrices, Covariance Structures, and Bears, Oh My!

• Approaches to Repeated Measures Data: Repeated Measures ANOVA, Marginal, and Mixed Models

• Sample Size Estimates for Multilevel Randomized Trials

• Five Extensions of the General Linear Model

• The Repeated and Random Statements in Mixed Models for Repeated Measures

• How Simple Should a Model Be? The Case of Insignificant Controls, Interactions, and Covariance Structures

• The Difference Between Clustered, Longitudinal, and Repeated Measures Data

• When the Hessian Matrix goes Wacky

• Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

• Concepts in Linear Regression you need to know before learning Multilevel Models

• Multilevel Models with Crossed Random Effects

• Confusing Statistical Terms #3: Levels of a Factor in Multilevel Models Measured at a Nominal Level

• Assessing the Fit of Regression Models

• Mixed Up Mixed Models

• Specifying Fixed and Random Factors in Mixed Models

Books

• SAS for Mixed Models, Second Edition by Ramon Littell, George Miliken, Walter Stroup, Russell Wolfinger, & Oliver Schabenberger

  This is a pretty technical book, and is not for the statistically feeble. But if you’re doing mixed models, you’re not statistically feeble. That said, if you are doing Mixed Modeling in SAS, it’s a must-have book. Back in the Cornell Statistical Consulting office, we actually wore out the book.

• Quantitative Methods in Population Health: Extensions of Ordinary Regression by Mari Palta
• Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith D. Singer & John B. Willett
• Multilevel Analysis for Applied Research: It’s Just Regression! (Methodology In The Social Sciences) by Robert Bickel PhD
• Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling by Professor Tom A.B. Snijders and Professor Roel Bosker
• Linear Mixed Models: A Practical Guide Using Statistical Software by Brady West, Kathleen B. Welch and Andrzej T Galecki

Journal Articles

• Arnold, Carolyn L. (1992) An Introduction to Hierarchical Linear Models. Measurement and Evaluation in Counseling and Development, 25, 58-90.
• Singer, Judith D. (1998) Using SAS PROC MIXED to Fit Multilevel Models, Hierarchial Models, and Individual Growth Models. Journal of Educational and Behavioral Statistics, 24, 323-355.

This articles uses SAS, but the ATS group at UCLA has on their web site papers that go through the examples in HLM, MLwiN, Stata, SPSS, and SPLUS.

• Plewis, Ian. (1998) Multilevel Models. Social Research Update, 23.
• Harner, Robert & Simpson, P.M. (2000). Mixed-Up Mixed Models: Things That Look Like They Should Work But Don’t, and Things That Look Like They Shouldn’t Work But Do. Presented at SUGI.

Web Sources

http://www.lesahoffman.com/Research/MLM.html:

Dr. Lesa Hoffman, of the University of Nebraska, has put on her website her entire lecture slides and podcasts for all her classes and workshops, including:

• Advanced Multilevel Models
• Latent Trait Measurement Models
• Multilevel Models for Longitudinal Data

It includes MPlus, SAS, and SPSS examples.