Digital Module 06: Bayesian Psychometrics—Posterior Predictive Model Checking https://ncme.elevate.commpartners.com

2019 ◽  
Vol 38 (2) ◽  
pp. 116-117
Author(s):  
Allison Ames ◽  
Aaron Myers
Keyword(s):  
Model Checking ◽  
Predictive Model ◽  
Posterior Predictive Model Checking ◽  
Bayesian Psychometrics ◽  
Digital Module

Posterior Predictive Model Checking and Proportion of Explained Variance

2015 ◽  
pp. 161-174
Author(s):  
Fränzi Korner-Nievergelt ◽  
Tobias Roth ◽  
Stefanie von Felten ◽  
Jérôme Guélat ◽  
Bettina Almasi ◽  
...  
Keyword(s):  
Model Checking ◽  
Predictive Model ◽  
Posterior Predictive Model Checking ◽  
Explained Variance

scholarly journals A Posterior Predictive Model Checking Method Assuming Posterior Normality for Item Response Theory

2018 ◽  
Vol 43 (2) ◽  
pp. 125-142 ◽  
Author(s):  
Megan Kuhfeld
Keyword(s):  
Item Response Theory ◽  
Model Checking ◽  
Predictive Model ◽  
Item Response ◽  
Local Dependence ◽  
Response Theory ◽  
Full Information Maximum Likelihood ◽  
Posterior Predictive Model Checking ◽  
Irt Models ◽  
Straightforward Method

This study investigated the violation of local independence assumptions within unidimensional item response theory (IRT) models. Bayesian posterior predictive model checking (PPMC) methods are increasingly being used to investigate multidimensionality in IRT models. The current work proposes a PPMC method for evaluating local dependence in IRT models that are estimated using full-information maximum likelihood. The proposed approach, which was termed as “PPMC assuming posterior normality” (PPMC-N), provides a straightforward method to account for parameter uncertainty in model fit assessment. A simulation study demonstrated the comparability of the PPMC-N and the Bayesian PPMC approach in the detection of local dependence in dichotomous IRT models.

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