An iterative scale purification procedure on lz for the detection of aberrant responses

Xuelan QIU, Sheng-Yun HUANG, Wen Chung WANG, You-Gan WANG

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

Many person-fit statistics have been proposed to detect aberrant response behaviors (e.g., cheating, guessing). Among them, lz is one of the most widely used indices. The computation of lz assumes the item and person parameters are known. In reality, they often have to be estimated from data. The better the estimation, the better lz will perform. When aberrant behaviors occur, the person and item parameter estimations are inaccurate, which in turn degrade the performance of lz. In this study, an iterative procedure was developed to attain more accurate person parameter estimates for improved performance of lz. A series of simulations were conducted to evaluate the iterative procedure under two conditions of item parameters, known and unknown, and three aberrant response styles of difficulty-sharing cheating, random-sharing cheating, and random guessing. The results demonstrated the superiority of the iterative procedure over the non-iterative one in maintaining control of Type-I error rates and improving the power of detecting aberrant responses. The proposed procedure was applied to a high-stake intelligence test. Copyright © 2023 Taylor & Francis Group, LLC.

Original languageEnglish
Pages (from-to)62-77
JournalMultivariate Behavioral Research
Volume59
Issue number1
Early online dateJun 2023
DOIs
Publication statusPublished - 2024

Citation

Qiu, X., Huang, S.-Y., Wang, W.-C., & Wang, Y.-G. (2024). An iterative scale purification procedure on lz for the detection of aberrant responses. Multivariate Behavioral Research, 59(1), 62-77. https://doi.org/10.1080/00273171.2023.2211564

Keywords

  • Person-fit statistics
  • lz
  • Scale purification
  • Aberrant behaviors
  • Cheating
  • Guessing
  • Item response theory

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