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Cancellation Policy: If you cancel your registration two weeks or more before the course is scheduled to begin, you are entitled to receive your choice of either a credit for a future seminar (which can be applied toward any of our courses) or a refund of the registration fee (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 3-Day Livestream Seminar Taught by Stephen Vaisey, Ph.D.

This seminar provides an intensive introduction to multilevel and mixed models, a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within classrooms, patients nested within hospitals, or survey respondents nested within countries.

Using techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques prevents researchers from asking substantively interesting questions about how processes work at different levels and how effects may vary across units in a population.

In addition to providing a solid foundation in using mixed models in Stata, this course will also equip you with a set of structured prompts to use with your Large Language Model (LLM) of choice. LLMs like ChatGPT can serve as invaluable “research assistants” but need to be prompted in a skillful way to maximize their usefulness and avoid pitfalls. You will learn how to use ChatGPT to help design, estimate, interpret, and understand the assumptions of your models. Explicit discussion of LLM prompting will comprise approximately 15-20% of course time.

After introducing the key concepts of within and between variance, we will begin with simple multilevel variance components models that can tell us how much of the variance in a measure can be allocated to different levels of observation. We will then move on to mixed models (random effects models with fixed covariates) that allow us to ask how factors at different levels can influence the outcome.

Next, we will investigate how using random coefficients and cross-level interactions can help us discover hidden structure in our data and help us investigate how individual-level processes work differently in different contexts. We will also briefly consider how these techniques can be applied to cases where we have repeated observations of individuals or other entities over time.

Although the course will focus primarily on the continuous outcome case, we will also cover how these models can easily be extended for use with categorical and limited dependent variables.

The seminar will focus on a hands-on understanding and draw from examples across the social and behavioral sciences. After completing the course, you will know:

1. 1. The technical and substantive difference between fixed and random effects and how these terms relate to complete, partial, and no pooling estimators.
   2. The meanings of random intercepts and random slopes are and when to use each one.
   3. How to use cross-level interactions to investigate effect heterogeneity.
   4. How to combine the strengths of random-effects and fixed-effects approaches into a single “between-within” model.
   5. How to estimate these models and interpret the results with the assistance of LLMs.

Although these techniques apply to both clustered and longitudinal data, in the interest of time we will focus almost exclusively on the clustered case. For courses focused on longitudinal data analysis, check out Longitudinal Data Analysis Using R or Longitudinal Data Analysis Using Stata.