A New Online Calibration Method for Multidimensional Computerized Adaptive Testing

Ping Chen, Chun Wang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Multidimensional-Method A (M-Method A) has been proposed as an efficient and effective online calibration method for multidimensional computerized adaptive testing (MCAT) (Chen & Xin, Paper presented at the 78th Meeting of the Psychometric Society, Arnhem, The Netherlands, 2013). However, a key assumption of M-Method A is that it treats person parameter estimates as their true values, thus this method might yield erroneous item calibration when person parameter estimates contain non-ignorable measurement errors. To improve the performance of M-Method A, this paper proposes a new MCAT online calibration method, namely, the full functional MLE-M-Method A (FFMLE-M-Method A). This new method combines the full functional MLE (Jones & Jin in Psychometrika 59:59–75, 1994; Stefanski & Carroll in Annals of Statistics 13:1335–1351, 1985) with the original M-Method A in an effort to correct for the estimation error of ability vector that might otherwise adversely affect the precision of item calibration. Two correction schemes are also proposed when implementing the new method. A simulation study was conducted to show that the new method generated more accurate item parameter estimation than the original M-Method A in almost all conditions.

Original languageEnglish (US)
Pages (from-to)674-701
Number of pages28
JournalPsychometrika
Volume81
Issue number3
DOIs
StatePublished - Sep 1 2016

Keywords

  • full functional maximum likelihood estimator
  • multidimensional computerized adaptive testing
  • multidimensional two-parameter logistic model
  • new item
  • online calibration
  • operational item

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