Literature DB >> 29881091

Detecting Item Preknowledge Using a Predictive Checking Method.

Xi Wang1, Yang Liu2, Ronald K Hambleton3.   

Abstract

Repeatedly using items in high-stake testing programs provides a chance for test takers to have knowledge of particular items in advance of test administrations. A predictive checking method is proposed to detect whether a person uses preknowledge on repeatedly used items (i.e., possibly compromised items) by using information from secure items that have zero or very low exposure rates. Responses on the secure items are first used to estimate a person's proficiency distribution, and then the corresponding predictive distribution for the person's responses on the possibly compromised items is constructed. The use of preknowledge is identified by comparing the observed responses to the predictive distribution. Different estimation methods for obtaining a person's proficiency distribution and different choices of test statistic in predictive checking are considered. A simulation study was conducted to evaluate the empirical Type I error and power rate of the proposed method. The simulation results suggested that the Type I error of this method is well controlled, and this method is effective in detecting preknowledge when a large proportion of items are compromised even with a short secure section. An empirical example is also presented to demonstrate its practical use.

Entities:  

Keywords:  Bayesian inference; generalized fiducial inference; item preknowledge; predictive checking; test security

Year:  2017        PMID: 29881091      PMCID: PMC5978583          DOI: 10.1177/0146621616687285

Source DB:  PubMed          Journal:  Appl Psychol Meas        ISSN: 0146-6216


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Journal:  Psychometrika       Date:  2016-01-14       Impact factor: 2.500

2.  Using Deterministic, Gated Item Response Theory Model to detect test cheating due to item compromise.

Authors:  Zhan Shu; Robert Henson; Richard Luecht
Journal:  Psychometrika       Date:  2013-01-03       Impact factor: 2.500

  2 in total
  3 in total

1.  Detecting Examinees With Item Preknowledge in Large-Scale Testing Using Extreme Gradient Boosting (XGBoost).

Authors:  Cengiz Zopluoglu
Journal:  Educ Psychol Meas       Date:  2019-04-02       Impact factor: 2.821

2.  Second-Order Probability Matching Priors for the Person Parameter in Unidimensional IRT Models.

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Journal:  Psychometrika       Date:  2019-07-01       Impact factor: 2.500

3.  The Lack of Robustness of a Statistic Based on the Neyman-Pearson Lemma to Violations of Its Underlying Assumptions.

Authors:  Sandip Sinharay
Journal:  Appl Psychol Meas       Date:  2021-10-23
  3 in total

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