Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 6-Day Livestream Seminar Taught by Kristopher Preacher, Ph.D.

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

Please note, this course will include all material from the livestream and on demand seminar Multilevel Modeling: A Second Course. 

We will begin by reviewing the basics of MLM. Next, Mplus will be introduced as a flexible and powerful software environment for fitting basic and advanced multilevel models. Then we will cover several advanced MLM topics.

Basic MLM topics include:

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

Advanced MLM topics include:

• Conducting power analysis for MLM using a general Monte Carlo technique
• Fitting multivariate multilevel models
• Modeling cross-classified data

Next, multilevel 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 path analysis
• Multilevel exploratory and confirmatory factor analysis
• Model fit in MSEM

We will then continue to explore special applications of MSEM. Advanced topics will include:

• Multilevel structural models with latent variables
• Applications to three-level (and higher-level) data
• Multilevel reliability estimation
• Multilevel mediation analysis
• Multiple group models
• Estimating, plotting, and probing interaction effects
• Moderation in MLM and MSEM
• Modeling discrete (e.g., binary, count) dependent variables
• Interval estimates for nonnormal statistics
• Handling convergence problems: A bag of tricks
• Conducting Monte Carlo simulation studies

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.

You 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
• Strategies for tackling convergence problems and estimation errors
• Programming skills for conducting Monte Carlo studies to assess model feasibility prior to data collection.
• Documented Mplus syntax templates for fitting a variety of models to multilevel data.