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A New Parameter-estimation Method Of 2PLM Based On Item Response Theory

Posted on:2004-02-28Degree:MasterType:Thesis
Country:ChinaCandidate:F LuoFull Text:PDF
GTID:2168360092993503Subject:Computer applications
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In this thesis, based on Item Response Theory, a number of ways to estimate the latent trait and item parameters were introduced and their advantages and disadvantages were analyzed; what is more, Empirical Logistic regression and two parameters Logistic model (2PLM) are combined to set up a linear model by logit-mapping and a new parameter-estimation method is proposed. The least square estimation in linear model is used to derive the two-stage estimation of the item parameter vector β1 of j th item as follows:Noting that Xj consists of the nuisance parameters θs , ∑j,βj, were updated so that the estimation of 6 s could be renewed. The above algorithm forms a double-two-stage iteration, as following:The results of Monte Carlo stimulation show that the double-two-stage iteration algorithm is more effective than empirical Logistic regression after item and ability parameters recovery study. There are three advantages about the new method: first . the new method can be applied to estimate fewer items; secondly, a test including fewer unusual response patterns can also be evaluated; thirdly, the results compared with homogeneous software dealing with 2PLM are accepted using mean absolute error as the criterion. Further, the linear model and new method could be expanded the situation when polytomous items and items in the testlet are display in a test.
Keywords/Search Tags:Item Response Theory (IRT), condition maximum likelihood estimation, joint maximum likelihood estimation, marginal maximum likelihood estimation and an EM algorithm, Bayesian estimation, empirical-Logistic-regression
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