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.

中文翻译：

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教育测量的自适应成对比较

成对比较作为一种整体测量方法在教育中正变得越来越流行。不幸的是，要进行可靠的测量，需要进行许多比较。为了减少所需比较的数量，我们开发了一种自适应选择算法（ASA），该算法在考虑对象参数的不确定性的同时选择最有用的比较。仿真研究的结果表明，在进行比较的情况下，与作为基准的随机选择算法相比，ASA导致对象参数估计的标准误差较小。两种算法的等级顺序准确性和可靠性相似。由于使用ASA时，秤分离可靠性（SSR）可能会高估基准可靠性，因此在解释SSR时需要谨慎。