|Title:||Extending Dynamic Fit Index Cutoffs for Latent Variable Models|
|Principal Investigator:||McNeish, Daniel||Awardee:||Arizona State University|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (06/1/2022 – 05/31/2025)||Award Amount:||$557,555|
|Type:||Methodological Innovation||Award Number:||R305D220003|
The goal of this proposed project is to provide empirical researchers with more generalizable, accessible, and accurate guidelines for assessing model fit in latent variable models. Traditional cutoffs have multiple known shortcomings, including the tendency to favor models with low construct reliability over those with high construct reliability. To address some of the drawbacks of traditional fit indices, the research team will expand the scope, testing, and software availability of a relatively new approach called the dynamic fit index (DFI). The DFI is a simulation-based technique that adapts to the context of a latent model to determine fit cutoffs which are specific to the model being tested.
Current software can handle only continuous indicators. The primary DFI expansion in this project is the development of software that allows for estimation of latent variable models that have categorical factor indicators; The researchers will also adapt DFI for use in testing measurement invariance across two groups. After these approaches are derived and programmed into software, the team will use Monte Carlo simulations to investigate the performance of DFI relative to more traditional fit indices. The researchers plan to create both a Shiny app for applied researchers and an adaptation of the R package lavaan for use by methodological researchers who are interested in exploring additional uses of DFI.