|Title:||Innovative, Translational, and User-Friendly Tools for Comprehensive Statistical Model Evaluation|
|Principal Investigator:||Bonifay, Wesley||Awardee:||University of Missouri, Columbia|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (06/01/2021 - 05/31/2024)||Award Amount:||$900,000|
|Type:||Methodological Innovation||Award Number:||R305D210032|
Co-Principal Investigator: Cai, Li
A key component of any scientific undertaking is the construction of a model that explains the data. No model is an exact representation of the phenomena under investigation and, especially in the education sciences, useful models are often simplistic approximations of immensely complex processes. Goodness-of-fit (GOF) assessment provides a limited view of a model's usefulness. While GOF addresses the closeness of the model to the observed data, generalizability measures the potential fit of a model to unseen data samples that have been or will be generated by the same underlying processes that produced the observed data. To make model-based inferences more informative, defensible, and replicable, applied educational researchers should contextualize GOF by also quantifying the generalizability and complexity of their models.
The purpose of this project is two-fold: first, to develop the theoretical basis for an eventual software package by combining Bayesian, classical, and information-theoretic perspectives into a unified approach to statistical modeling. Then, after extensive Monte Carlo simulations to develop and test the unified approach, the research team will develop a user-friendly R package and Shiny user interface for CoSME (comprehensive statistical model evaluation). Additional products will include journal manuscripts, vignettes, and interactive tutorials, along with training workshops offered at conferences.