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The testlet model is a popular statistical approach widely used byresearchers and practitioners to address local item dependence (LID), aviolation of the local independence assumption in item response theory (IRT)which can cause various deleterious psychometric consequences. Same as otherpsychometric models, the utility of the testlet model relies heavily onaccurate estimation of its model parameters. The two-parameter logistic (2PL)testlet model has only been systematically investigated in the psychometricliterature regarding its model parameter recovery with one full informationestimation methods, namely Markov chain Monte Carlo (MCMC) method, althoughthere are other estimation methods available such as marginal maximumlikelihood estimation (MMLE) and limited information estimation methods.  In the current study, a comprehensive simulation study was conducted toinvestigate how MCMC, MMLE, and one limited information estimation method(WLSMV), all implemented in Mplus, recovered the item parameters and thetestlet variance parameter of the 2PL testlet model. The manipulated factorswere sample size and testlet effect magnitude, and parameter recovery wereevaluated with bias, standard error, and root mean square error. We found thatthere were no statistically significant differences regarding parameterrecovery between the three methods. When both sample size and magnitude oftestlet variance were small, both WLSMV and MCMC had convergence issues, whichdid not occur to MCMC regardless of sample size and testlet variance. A realdataset from a high-stakes test was used to demonstrate the estimation of the2PL testlet model with the three estimation methods.  Keywords: IRT, testlet model, estimation, full-information,limited-information.

Item Parameter Recovery for the Two-Parameter Testlet Model with Different Estimation Methods