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<p>Multidimensional forced-choice (MFC) testing has been proposed as an alternative to single-statement (SS) Likert-type measures to reduce response biases in noncognitive measurement. Research progress has been made on MFC computerized adaptive testing (CAT) to improve testing efficiency. CAT enhances efficiency by successively selecting items that are most informative at each respondent’s trait estimate. In MFC CAT, this causes some forced-choice items and the statements composing them to be frequently exposed while others are rarely used, which adversely affects test security and costs. This research developed an exposure control method for MFC CAT based on the multi-unidimensional pairwise preference model (MUPP; Stark et al. &#xa0;<i>Applied Psychological Measurement</i>, 29,184–203, <CitationRef CitationID="CR37">2005</CitationRef>). Because the method was intended to prevent the overuse of the most informative items and statements, it tended to decrease overall measurement accuracy and precision. Thus, a second purpose of this research was to examine the extent to which these losses in accuracy and precision might be offset by incorporating collateral information. The effectiveness of the exposure control method and the incorporation of collateral information in MFC CAT were investigated in a Monte Carlo study that also manipulated test length and the correlation between dimensions. A byproduct of this research was an MFC CAT algorithm that improves test security and cost-effectiveness, while simultaneously maintaining measurement accuracy and precision of noncognitive constructs.</p>

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Examining the tradeoffs of exposure control and collateral information with multidimensional forced-choice computerized adaptive testing

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Naidan Tu,
  • Sean Joo,
  • Stephen Stark

摘要

Multidimensional forced-choice (MFC) testing has been proposed as an alternative to single-statement (SS) Likert-type measures to reduce response biases in noncognitive measurement. Research progress has been made on MFC computerized adaptive testing (CAT) to improve testing efficiency. CAT enhances efficiency by successively selecting items that are most informative at each respondent’s trait estimate. In MFC CAT, this causes some forced-choice items and the statements composing them to be frequently exposed while others are rarely used, which adversely affects test security and costs. This research developed an exposure control method for MFC CAT based on the multi-unidimensional pairwise preference model (MUPP; Stark et al.  Applied Psychological Measurement, 29,184–203, 2005). Because the method was intended to prevent the overuse of the most informative items and statements, it tended to decrease overall measurement accuracy and precision. Thus, a second purpose of this research was to examine the extent to which these losses in accuracy and precision might be offset by incorporating collateral information. The effectiveness of the exposure control method and the incorporation of collateral information in MFC CAT were investigated in a Monte Carlo study that also manipulated test length and the correlation between dimensions. A byproduct of this research was an MFC CAT algorithm that improves test security and cost-effectiveness, while simultaneously maintaining measurement accuracy and precision of noncognitive constructs.