Publication Date
10-2021
Document Type
Presented Paper
Abstract
Marginal maximum likelihood (MML), a common estimation method for IRT models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for MML estimation. In this study, using samples sizes of 1000 or smaller, not using priors often led to extreme, implausible parameter estimates. Applying prior distributions to the c-parameters alleviated the estimation problems with samples of 1000; priors on both the a-parameters and c-parameters were needed for the samples of 500, and frequent problems remained for the samples of 100 even with prior distributions. Estimates were biased when the mean of the prior did not match the true parameter value, but the degree of the bias did not depend on the variance of the prior unless it was extremely informative. The RMSE of the a-parameters and b-parameters did not depend greatly on either the mean or the variance of the prior unless it was extremely informative. The RMSE of the c-parameters, like the bias, depended on the mean of the prior for c.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Satkus, P., & DeMars, C. (2021, October). Item Parameter Recovery With and Without The Use Of Priors [Paper presentation]. Northeastern Educational Research Association 52nd Annual Meeting, virtual.
