Introduction

Confirmatory factor analysis (CFA) is a commonly used statistical method in the social sciences. Although these models have been used for over a century, debate remains about how to evaluate the fit of factor models. Recently, we proposed the use dynamic fit index (DFI) cutoffs to evaluate model fit1,2 and introduced a corresponding Shiny application to facilitate their use3.

This book was written as a tutorial for applied psychologists that are interested in using the DFI Shiny App to compute model specific cutoffs for their CFA models. We wrote this to make DFI cutoffs more accessible to everyone, especially those that use SPSS Amos and Mplus.

In this book, we will walk through 12 commonly asked questions about DFI cutoffs and use an applied example to demonstrate how to use the Shiny app to calculate them. For R users, DFI cutoffs are also available on CRAN under the package dynamic4.

install.packages("dynamic")
library(dynamic)

References

1. McNeish, D., & Wolf, M. G. (2021). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods. https://doi.org/10.1037/met0000425
2. McNeish, D., & Wolf, M. G. (2022). Dynamic fit index cutoffs for one-factor models. Behavior Research Methods.
3. Wolf, M. G., & McNeish, D. (2020). Dynamic Model Fit. https://www.dynamicfit.app
4. Wolf, M. G., & McNeish, D. (2022). Dynamic: DFI cutoffs for latent variable models. https://github.com/melissagwolf/dynamic