Statistical Methods Course Description
Overview: An intermediate 5-day course introducing multilevel modeling for analyzing hierarchically organized data using HLM 7 software.
- Very brief review of multiple linear regression
- Foundational skills for two-level modeling including random effects, model building, cross-level interactions, and assumption checking
- Multilevel modeling with binary outcomes
- Longitudinal modeling
- Three-level modeling
- Cross-classified random effects modeling
Instructor: Audrey Leroux
Dr. Audrey Leroux, is an Assistant Professor of Research, Measurement, and Statistics in the Department of Educational Policy Studies at Georgia State University’s College of Education and Human Development. She received her doctorate in Quantitative Methods in the Department of Educational Psychology at The University of Texas at Austin under the mentorship of Dr. Tasha Beretvas. Dr. Leroux teaches graduate courses in regression, introductory and advanced multilevel modeling, and programming in R. Her research evaluates innovative models and applications in computerized adaptive testing and multilevel modeling. Specifically, her publications have focused on novel procedures within item exposure controls and stopping rules in computerized adaptive testing, as well as extensions to conventional multilevel modeling that handle individual mobility (e.g., cross-classified random effects modeling and multiple membership random effects modeling).
Course Learning Goals
After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:
- Acquire an understanding of multilevel modeling techniques as applied in the educational, social, health, and behavioral sciences
- Manage and clean multilevel data for analysis
- Specify, estimate, evaluate, and compare different multilevel models using HLM 7 software
- Interpret and present the results of a multilevel modeling analysis
- Critically evaluate applications of multilevel modeling in scientific studies
- Become acquainted with multilevel modeling for binary outcomes and for non-purely hierarchical data
Participants will also complete the course with a foundation for future learning about multilevel modeling and knowledge about available resources to guide such endeavors.
- Advanced proficiency in multiple linear regression, including use of categorical independent variables
- Intermediate proficiency with at least one statistical software package (e.g., SPSS, Stata, SAS, R, etc.)
Not required but advantageous:
- At least limited experience (e.g., graduate-level course) with multivariate data analysis
- At least limited experience in categorical data analysis, including measures of association (e.g., odds ratio)
- At least limited experience in binary logistic regression
No level of proficiency beyond basic awareness is assumed for skills related to:
- Multilevel modeling
- Advanced mathematical or statistical topics such as matrix algebra, calculus, or likelihood theory
Course Software and Computer Support
Participants need to bring a laptop computer with Wi-Fi capabilities.
All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.
Instruction will be provided for the methods using the most current version of HLM 7 software (student edition). HLM 7 student edition is available for free only on Windows environments (http://www.ssicentral.com/hlm/student.html). Information for purchasing a personal full license can be found at www.ssicentral.com.
Note: This course will also require the use of SPSS, SAS, Stata, R, or any other software of your choice for data cleaning.
Below are links to course files for those who enrolled in the course. Please download these files onto your computer before the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information you agree not to share your login information or the content protected by it.
After May 1st: Click Here to Access The Multilevel Modeling Course Files