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Quantitative Research Labs

Health and Developmental Methods Lab  - Kevin Grimm

The Health and Developmental Methods Lab seeks to develop and evaluate methods and statistical models used to capture key characteristics of individual change processes, the determinants of longitudinal change process, and the determinants of between-person differences in the individual change process. Recently, we have focused on the development and application of data mining methods that combine machine learning algorithms with latent variable models. We have published papers on measurement invariance, latent growth models, linear and nonlinear mixed-effects models, growth mixture models, latent class models, linear dynamic models, integrative data analysis, structural equation models, data mining techniques, and exploratory approaches to studying individual change. The majority of our applications are focused on health, cognition, and achievement outcomes.

Please visit our Google website for more info!

Mixed Effects Modeling Lab - Daniel McNeish

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. 

Research in Prevention Laboratory - David MacKinnon

RiPL (Research in Prevention Lab) focuses on prevention research to influence public health and choosing healthy behaviors. Additionally, the lab conducts quantitative research focused on methodology, exploring innovative methods to analyze data gathered for research within all disciplines. Current and past projects include work on mediation analysis, identifying risky behaviors for health problems due to impaired self-regulation (ONTOLOGY), reducing risk of obesity among adolescents (ORBIT), healthy behaviors in firefighters and police officers (IGNITE, PHLAME, and SHIELD), steroid use prevention (ATLAS), drug testing of student athletes (SATURN), and alcohol warning labels (ABLE).