a Hofstra University.
b University of Illinois Urbana Champaign.
Multivariate Behav Res. 2017 Sep-Oct;52(5):562-575. doi: 10.1080/00273171.2017.1341829. Epub 2017 Jul 17.
A nonparametric technique based on the Hamming distance is proposed in this research by recognizing that once the attribute vector is known, or correctly estimated with high probability, one can determine the item-by-attribute vectors for new items undergoing calibration. We consider the setting where Q is known for a large item bank, and the q-vectors of additional items are estimated. The method is studied in simulation under a wide variety of conditions, and is illustrated with the Tatsuoka fraction subtraction data. A consistency theorem is developed giving conditions under which nonparametric Q calibration can be expected to work.
本研究提出了一种基于汉明距离的非参数技术，其认识到一旦属性向量已知，或者以高概率正确估计，就可以确定正在进行校准的新项目的逐属性向量。我们考虑 Q 对于大型项目库已知的情况，并估计其他项目的 q-向量。该方法在各种条件下进行了模拟研究，并通过 Tatsuoka 分数减法数据进行了说明。开发了一个一致性定理，给出了可以预期非参数 Q 校准起作用的条件。
