ABAI Webinar

Acknowledgements and Disclaimers

• None of what I am talking about today reflects the views or opinions of Advocates for Human Potential

• These are my own ideas and thoughts, heavily informed by my own experiences, things that I’ve read, and people that I’ve talked to

• Have to acknowledge many of the people who have shaped my thinking and informed my approaches to data analysis

• People including: Derek Reed, Paul Johnson, Mikhail Koffarnus, Warren Bickel, Chris Franck, Brady DeHart, Steve Hursh, and others

• Also I’m not a statistician nor am I a practicing applied behavior analyst, so take everything I say with skepticism

What you will NOT learn

• A full cookbook of model fitting in R (or other software)

• Exhaustive diagnostic procedures and model selection strategies

• How to interpret every model coefficient and how to use them to make predictions

• Deep mathematical derivations

What I hope you will learn

• Recognize when mixed-effects models are appropriate

• Distinguish fixed vs. random effects (with intuition)

• Identify data scenarios (e.g. repeated measures or hierarchical datasets) where mixed-effects models are an appropriate analytic approach

• Explain how incorporating random effects in a model accounts for individual differences and addresses limitations of traditional analyses that rely on data aggregation

• Learn how they apply to single-case designs and behavioral economic data

• Gain basic intuition through visual examples

• Leave with resources and confidence to explore further

Deep reflection