The Mixed Effects Modeling Lab focuses on development, evaluation, and application of statistical methods to appropriately model data that come from a correlated data structure. Examples of such structures include students nested within schools, patients nested within hospitals, and repeated measures nested within people. In particular, the lab’s research focuses on modeling small sample data, missing data, Bayesian estimation, and extending mixed effects models to novel situations where they may possess untapped advantages.
Dan McNeish is the director of the Mixed Effects Modeling Lab and is an associate professor of quantitative psychology at Arizona State. He received his PhD in Measurement and Statistics from the University of Maryland. His interests include models for clustered and longitudinal data, methods for small samples, and structural equation models. More information about Dan can be found on his personal research website (https://sites.google.com/site/danielmmcneish/home) or his ASU faculty page (https://isearch.asu.edu/profile/3146016). Updates about work conducted in the lab can also be found on his Twitter page, @dmcneish18.
Andrea Savord is pursuing her PhD in quantitative psychology. She earned her MS in Psychology from Northern Michigan University. Her interests include model fit, Bayesian methods, dyadic data, and dynamic structural equation models. More information about Andrea can be found her website, https://sites.google.com/asu.edu/andreasavord/home
An updated and complete list of publications can be found here, https://sites.google.com/site/danielmmcneish/acdemic-work