Adaptive pairwise comparison for educational measurement

Elise Crompvoets, A.A. Béguin, K. Sijtsma

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Abstract

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.
Original languageEnglish
Number of pages23
JournalJournal of Educational and Behavioral Statistics
DOIs
Publication statusE-pub ahead of print - 2020

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Keywords

  • adaptive measurement
  • comparative judgment
  • holistic measurement
  • pairwise comparison

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