{"title":"Weighted Answer Similarity Analysis.","authors":"Nicholas Trout, Kylie Gorney","doi":"10.1177/01466216251322353","DOIUrl":null,"url":null,"abstract":"<p><p>Romero et al. (2015; see also Wollack, 1997) developed the <i>ω</i> statistic as a method for detecting unusually similar answers between pairs of examinees. For each pair, the <i>ω</i> statistic considers whether the observed number of similar answers is significantly larger than the expected number of similar answers. However, one limitation of <i>ω</i> is that it does not account for the particular items on which similar answers are observed. Therefore, in this study, we propose a weighted version of the <i>ω</i> statistic that takes this information into account. We compare the performance of the new and existing statistics using detailed simulations in which several factors are manipulated. Results show that while both the new and existing statistics are able to control the Type I error rate, the new statistic is more powerful, on average.</p>","PeriodicalId":48300,"journal":{"name":"Applied Psychological Measurement","volume":" ","pages":"01466216251322353"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873304/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Psychological Measurement","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/01466216251322353","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PSYCHOLOGY, MATHEMATICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Romero et al. (2015; see also Wollack, 1997) developed the ω statistic as a method for detecting unusually similar answers between pairs of examinees. For each pair, the ω statistic considers whether the observed number of similar answers is significantly larger than the expected number of similar answers. However, one limitation of ω is that it does not account for the particular items on which similar answers are observed. Therefore, in this study, we propose a weighted version of the ω statistic that takes this information into account. We compare the performance of the new and existing statistics using detailed simulations in which several factors are manipulated. Results show that while both the new and existing statistics are able to control the Type I error rate, the new statistic is more powerful, on average.
Romero et al. (2015；另见Wollack， 1997)发展了ω统计作为一种方法来检测异常相似的答案对考生之间。对于每一对，ω统计量考虑观察到的相似答案的数量是否显著大于预期的相似答案的数量。然而，ω的一个限制是，它没有考虑到观察到类似答案的特定项目。因此，在本研究中，我们提出了一个考虑到这些信息的ω统计量的加权版本。我们使用几个因素被操纵的详细模拟来比较新的和现有的统计数据的性能。结果表明，虽然新的和现有的统计量都能够控制第一类错误率，但平均而言，新的统计量更强大。
期刊介绍：
Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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