Multilevel Modeling: A Second Course

A 4-Week On-Demand Seminar Taught by
Kristopher Preacher, Ph.D.

Read reviews of the remote version of this seminar

To see a sample of the course slides, click here.

Hierarchically clustered (multilevel or nested) data are common in the social sciences, medical fields, and business research. Clustered data violate the assumption of independence required by ordinary statistical methods. Increasingly complex research designs and hypotheses have created a need for sophisticated methods that go beyond standard multilevel modeling (MLM).

This “second course” in MLM will introduce a variety of MLM extensions, including cutting-edge multilevel structural equation modeling (MSEM) to handle complex designs and modeling objectives. Throughout the seminar, empirical examples will be presented to illustrate key concepts. A background in structural equation modeling (SEM) is not necessary.

The course takes place online in a series of four weekly installments of videos, quizzes, readings, and exercises, and requires about 8 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.

This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 11 modules:

  1. Review of multilevel modeling (MLM) and Mplus
  2. Univariate MLM and estimating, plotting, and probing interaction effects
  3. Modeling discrete dependent variables
  4. Power analysis for MLM
  5. Modeling cross-classified data
  6. Overview of single-level SEM and orientation to Mplus for SEM
  7. Multilevel structural equation modeling (MSEM) overview, equations, and path diagrams
  8. Orientation to Mplus for MSEM
  9. Multilevel path analysis, multilevel confirmatory factor analysis, and model fit in MSEM
  10. Multilevel exploratory factor analysis, general multilevel SEM with latent variables, and multilevel mediation
  11. Three-level models in MLM vs. MSEM and multilevel reliability estimation

The modules contain videos of the live, 4-day remote version of the course in its entirety. Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set.

There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Preacher.  

More Details About Course Content

We will begin the seminar by reviewing the basics of MLM, including

  • The motivation for MLM
  • Key concepts
  • Equation conventions
  • The univariate two-level MLM with fixed and random coefficients

Mplus will be introduced as a flexible and powerful software environment for fitting basic and advanced multilevel models. Next, we will cover several advanced MLM topics, including

  • Estimating, plotting, and probing interaction effects
  • Modeling cross-classified data
  • Modeling discrete (e.g., binary, count) dependent variables
  • Conducting power analysis for MLM using a general Monte Carlo technique
  • Fitting multivariate multilevel models

Nextmultilevel structural equation modeling will be introduced as a general approach for more complex modeling tasks. After a brief overview of single-level SEM, we will turn to the development of MSEM and the important advantages of MSEM over MLM (e.g. inclusion of latent variables, complex causal pathways, upper-level outcomes, and model fit assessment). Standard SEM and MLM will be recast as special cases of MSEM. Next we will cover a variety of MSEM topics:

  • Multilevel exploratory and confirmatory factor analysis
  • Multilevel path analysis
  • Multilevel structural models with latent variables
  • Multilevel mediation analysis
  • Multilevel reliability estimation
  • Applications to cross-classified and three-level data

Throughout the course, models will be presented in several formats—path diagrams, equations, and software syntax. Data and Mplus syntax for all of the examples will be included in the workshop materials.

Participants in this seminar can expect to gain:

  • Mastery of advanced topics in MLM
  • A deeper understanding of the relationship between MLM and SEM
  • The ability to use multilevel SEM to test complex structural hypotheses
  • Resources to conduct power analysis for virtually any multilevel design
  • The ability to fluently interpret and translate among path diagrams, model equations, and Mplus syntax for advanced MLM and MSEM
  • Documented Mplus syntax templates for fitting a variety of multilevel models.


In the videos, Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential. You are welcome and encouraged to use a computer with Mplus installed (including either the multilevel or combination add-on). However, this is not required. You will still benefit from the comprehensive set of slides and syntax that you can apply at a later time.  

WHO SHOULD Register?

This seminar is designed for researchers who have some prior experience with multilevel modeling (e.g., in a seminar, workshop, or course) and who want to deepen and extend their knowledge. At a minimum, participants should have a good working knowledge of basic principles of statistical inference (e.g. standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the theory and practice of linear regression.

This seminar covers much of the same content as Kristopher Preacher’s 2-week remote Multilevel Structural Equation Modeling seminar.

REVIEWS OF THE Remote VERSION OF Multilevel Modeling: A Second Course

“This is a comprehensive course with great sample syntax and output provided. The instructor is highly competent; knowledgeable, approachable, and explains things in a way that tends to the varying skill levels of attendees.”
  Laura Johnson, University of Nevada, Reno

“The facilitator, Kristopher Preacher, explained concepts and steps in a way that made it easy for me, a beginner, to understand. He walks you through how to run different models and gives clear explanations that help you to better understand other reading materials.”
  Emmanuel Gamor, The Hong Kong Polytechnic University

“I liked the detailed working through of the material. I also appreciated the demonstrations of using Mplus to do unusual “I never would have thought of that” things, particularly in the appendices sections.”
  Eugene (Gene) Maguin, University of Buffalo