Chen Jyun-Hong, Chao Hsiu-Yi
Department of Psychology, National Cheng Kung University, No. 1, University Road, Tainan City, 701401, Taiwan.
Department of Psychology, Soochow University, No. 70, Linhsi Road, Taipei City, 111002, Taiwan.
Behav Res Methods. 2025 May 28;57(7):180. doi: 10.3758/s13428-025-02673-8.
In computerized adaptive testing (CAT), information-based item selection rules (ISRs), such as maximum Fisher information (MFI), often excessively rely on discriminating items, leading to unbalanced utilization of the item pool. To address this challenge, the present study introduced the MaxiMin Information (MMI) criterion, which is grounded in decision theory. MMI calculates each item's minimum information (I) within the current confidence interval (CI) of the trait level, selecting the item with the maximum I to be administered. For examinees with broader CIs (less precise trait estimates), MMI leans toward administering less discriminating items, which tend to yield larger I. Conversely, for narrower CIs, MMI aligns more closely with MFI by favoring items with higher discrimination. This indicates that MMI's item selection is tailored to each examinee based on his or her provisional trait estimate and its estimation precision. Five simulation studies were conducted to assess MMI's performance in CAT under various conditions. Results demonstrate that although MMI is comparable with other ISRs in terms of trait estimation precision, it excels in balancing item pool utilization. By fine-tuning confidence levels, MMI not only efficiently schedules the use of discriminating items toward the test's later stages to enhance test efficiency but also effectively adapts to different testing scenarios. From these findings, we generally recommend applying MMI with a confidence level of 95% to optimize item pool utilization without compromising trait estimation accuracy. With its evident advantages, MMI holds promise for practical applications, especially for high-stakes tests requiring utmost test efficiency and security.
在计算机自适应测试（CAT）中，基于信息的项目选择规则（ISRs），如最大费舍尔信息（MFI），往往过度依赖区分性项目，导致项目库的利用不均衡。为应对这一挑战，本研究引入了基于决策理论的最大最小信息（MMI）准则。MMI计算每个项目在当前特质水平置信区间（CI）内的最小信息（I），选择I值最大的项目进行施测。对于置信区间较宽（特质估计不太精确）的考生，MMI倾向于施测区分性较低的项目，这些项目往往会产生较大的I值。相反，对于较窄的置信区间，MMI通过青睐具有较高区分度的项目，与MFI更为接近。这表明MMI的项目选择是根据每个考生的临时特质估计及其估计精度量身定制的。进行了五项模拟研究，以评估MMI在各种条件下在CAT中的表现。结果表明，虽然MMI在特质估计精度方面与其他ISRs相当，但在平衡项目库利用方面表现出色。通过微调置信水平，MMI不仅有效地将区分性项目的使用安排到测试后期以提高测试效率，还能有效适应不同的测试场景。从这些发现中，我们一般建议应用置信水平为95%的MMI来优化项目库利用，同时不影响特质估计的准确性。凭借其明显的优势，MMI在实际应用中具有前景，特别是对于需要极高测试效率和安全性的高风险测试。
