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Multilevel Modeling Course 2017-07-13T13:43:26+00:00

Multilevel Modeling Course

Session 1: June 4 – 8, 2018
Albuquerque, NM – Embassy Suites

FAQVenue Info
$1,795 Faculty/Professional or $1,095 Student/Post-Doc

Payment Options
Per Course Qty
Professionalshow details + $1,795 (USD)  
Studentshow details + $1,095 (USD)  

Statistics Course Description

This course is designed to provide theoretical and applied understandings of multilevel modeling. The fundamentals of multilevel modeling are taught by extending knowledge of regression analyses to designs involving a nested data structure. Nested data structures include, for example, students within classrooms, professionals within corporations, patients within hospitals, or repeated observations from the same person. In each of these cases and many more, the data are hierarchically arranged and may require methods beyond multiple regression or analysis of variance. These methods fall under the heading of multilevel modeling, which is also sometimes referred to as mixed modeling, hierarchical linear modeling, or random coefficients modeling.

This course will help you begin to learn how to analyze multilevel data sets and interpret results of multilevel modeling analyses. Organizational analysis and growth curve modeling, the most common multilevel modeling applications, are featured in the course. Using real datasets provided in the course, participants will learn how to use the HLM software program to obtain analysis results. Further, the course will emphasize proper interpretation of analysis results and illustrate procedures that can be used to specify multilevel models. Coverage of multilevel models for binary outcomes and cross-classified random effects modeling will also be included.

Course Syllabus

Monday June 4, 2018
9:00-9:30 Welcome and introductions
9:30-10:45 From regression to multilevel modeling
10:45-11:00 Snack and refreshment break
11:00-12:30 From regression to multilevel modeling (continued)
12:30-1:30 Lunch break
1:30-3:00 Random effects
3:00-3:15 Snack and refreshment break
3:15-5:00 HLM software
Tuesday June 5, 2018
9:00-10:45 Estimation
10:45-11:00 Snack and refreshment break
11:00-12:30 Centering
12:30-1:30 Lunch break
1:30-3:00 Model building and testing
3:00-3:15 Snack and refreshment break
3:15-5:00 Interactions and contextual effects
Wednesday June 6, 2018
9:00-10:45 Assumption checking
10:45-11:00 Snack and refreshment break
11:00-12:30 Power and sample size
12:30-1:30 Lunch break
1:30-3:00 Binary outcomes
3:00-3:15 Snack and refreshment break
3:15-5:00 Binary outcomes (continued)
Thursday June 7, 2018
9:00-10:45 Longitudinal modeling
10:45-11:00 Snack and refreshment break
11:00-12:30 Longitudinal modeling (continued)
12:30-1:30 Lunch break
1:30-3:00 Three-level modeling
3:00-3:15 Snack and refreshment break
3:15-5:00 Three-level modeling (continued)
Friday June 8, 2018
9:00-10:45 Cross-classified random effects modeling
10:45-11:00 Snack and refreshment break
11:00-12:30 Cross-classified random effects modeling (continued)
12:30-1:30 Lunch break
1:30-5:00 One-on-one consultations with instructor

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

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

Stats Camp 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.

Course Prerequisites


  • 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.

Course Files

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

Multilevel Modeling Stats Camp Course