Introduction to the Vol. 50, No. 1, 2023

A simple mechanism to evaluate and correct the artificial attenuation is proposed. Because the formulae of η and point-biserial correlation are equal, η can also get negative values. The original paper “Empirical evaluation of fully Bayesian information criteria for mixture IRT models using NUTS” by Rehab AlHakmani and Yanyan Sheng (2023) evaluates the performance of fully Bayesian information criteria, namely, LOO, WAIC, and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with conventional information criteria including AIC, BIC, CAIC, SABIC, and DIC. Because the classical estimator of item difficulty p is a biased estimator of the latent difficulty level, the item parameters A and B and the person ability parameter within IRT modeling are, consequently, biased estimators of item discrimination and item difficulty as well as ability levels of the test takers. In the presence of some unmeasured covariates, some instrumental variable methods, such as the two-stage residual inclusion (2SRI) estimator or limited-information maximum-likelihood (LIML) estimator, can still obtain an unbiased estimate for causal effects despite the existence of nonlinear models, such as logistic regression and probit models.