Event Title
Assessing Local Fit in Confirmatory Factor Models by Approximating Probabilities
Date of Publication
2-3-2021
Document Type
Presentation
Abstract
Validity evidence for factor structures underlying a set of items can come from how well a proposed model reconstructs, or fits, the observed relationships. Global model fit is limited in that some components of the proposed model fit better than other components. This limitation has led to the recommendation of examining fit locally within model components. A new probabilistic approach to assessing local fit using a Bayesian approximation will be described and illustrated with the use of a simulated dataset. I will show how the posterior approximation closely approximated the sampling distribution of the true parameter. Potential limitations and possible generalizations will be discussed.
Keywords
Models, Research
Persistent Identifier
http://hdl.handle.net/10950/2956
Assessing Local Fit in Confirmatory Factor Models by Approximating Probabilities
Validity evidence for factor structures underlying a set of items can come from how well a proposed model reconstructs, or fits, the observed relationships. Global model fit is limited in that some components of the proposed model fit better than other components. This limitation has led to the recommendation of examining fit locally within model components. A new probabilistic approach to assessing local fit using a Bayesian approximation will be described and illustrated with the use of a simulated dataset. I will show how the posterior approximation closely approximated the sampling distribution of the true parameter. Potential limitations and possible generalizations will be discussed.