000062995 001__ 62995 000062995 005__ 20210507081944.0 000062995 0247_ $$2doi$$a10.1177/0146621617707510 000062995 0248_ $$2sideral$$a101015 000062995 037__ $$aART-2017-101015 000062995 041__ $$aeng 000062995 100__ $$aSorrel, M.A. 000062995 245__ $$aInferential Item-Fit Evaluation in Cognitive Diagnosis Modeling 000062995 260__ $$c2017 000062995 5060_ $$aAccess copy available to the general public$$fUnrestricted 000062995 5203_ $$aResearch related to the fit evaluation at the item level involving cognitive diagnosis models (CDMs) has been scarce. According to the parsimony principle, balancing goodness of fit against model complexity is necessary. General CDMs require a larger sample size to be estimated reliably, and can lead to worse attribute classification accuracy than the appropriate reduced models when the sample size is small and the item quality is poor, which is typically the case in many empirical applications. The main purpose of this study was to systematically examine the statistical properties of four inferential item-fit statistics: S-X2, the likelihood ratio (LR) test, the Wald (W) test, and the Lagrange multiplier (LM) test. To evaluate the performance of the statistics, a comprehensive set of factors, namely, sample size, correlational structure, test length, item quality, and generating model, is systematically manipulated using Monte Carlo methods. Results show that the S-X2 statistic has unacceptable power. Type I error and power comparisons favor LR and W tests over the LM test. However, all the statistics are highly affected by the item quality. With a few exceptions, their performance is only acceptable when the item quality is high. In some cases, this effect can be ameliorated by an increase in sample size and test length. This implies that using the above statistics to assess item fit in practical settings when the item quality is low remains a challenge. 000062995 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/PSI2013-44300-P 000062995 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000062995 590__ $$a0.923$$b2017 000062995 591__ $$aSOCIAL SCIENCES, MATHEMATICAL METHODS$$b34 / 49 = 0.694$$c2017$$dQ3$$eT3 000062995 591__ $$aPSYCHOLOGY, MATHEMATICAL$$b11 / 13 = 0.846$$c2017$$dQ4$$eT3 000062995 592__ $$a1.17$$b2017 000062995 593__ $$aSocial Sciences (miscellaneous)$$c2017$$dQ1 000062995 593__ $$aPsychology (miscellaneous)$$c2017$$dQ1 000062995 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000062995 700__ $$aAbad, F.J. 000062995 700__ $$aOlea, J. 000062995 700__ $$ade la Torre, J. 000062995 700__ $$0(orcid)0000-0001-6887-6277$$aBarrada, J.R.$$uUniversidad de Zaragoza 000062995 7102_ $$14009$$2620$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Metod.Ciencias Comportam. 000062995 773__ $$g41, 8 (2017), [18 pp.]$$pAppl. psychol. meas.$$tAPPLIED PSYCHOLOGICAL MEASUREMENT$$x0146-6216 000062995 8564_ $$s578645$$uhttps://zaguan.unizar.es/record/62995/files/texto_completo.pdf$$yVersión publicada 000062995 8564_ $$s70686$$uhttps://zaguan.unizar.es/record/62995/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000062995 909CO $$ooai:zaguan.unizar.es:62995$$particulos$$pdriver 000062995 951__ $$a2021-05-07-08:09:49 000062995 980__ $$aARTICLE