{"title":"项目间网格多维计算机分类测试中序列概率比测试的预规则。","authors":"Po-Hsien Hu, Ching-Lin Shih, Cheng-Te Chen","doi":"10.3758/s13428-025-02600-x","DOIUrl":null,"url":null,"abstract":"<p><p>The measurement efficiency of a grid multidimensional computerized classification test (grid MCCT), which makes a classification decision per dimension, can be improved by taking the correlations between the dimensions into account in the termination criterion. The higher the correlations, the better the improvement in measurement efficiency. However, a termination criterion utilizing inter-dimensional information (i.e., SPRT-C; Liu et al., 2022) was found to yield lower levels of correct classification rates than not utilizing it (i.e., SPRT-SF; Seitz & Frey, 2013) under the between-item grid MCCT when the cutoff was set at the mean of the latent trait distribution. This study proposes a pre-rule to determine whether the SPRT-SF or SPRT-C should be used during the process of classification test administration. Through a series of simulation studies, the results showed that our proposed method (called P-SPRT) can substantially improve upon the SPRT-C in terms of correct classification rates, while maintaining its high measurement efficiency in terms of test length. This paper concludes with a discussion of the findings and further applications.</p>","PeriodicalId":8717,"journal":{"name":"Behavior Research Methods","volume":"57 2","pages":"79"},"PeriodicalIF":4.6000,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11779759/pdf/","citationCount":"0","resultStr":"{\"title\":\"A pre-rule for the sequential probability ratio test in a between-item grid multidimensional computerized classification test.\",\"authors\":\"Po-Hsien Hu, Ching-Lin Shih, Cheng-Te Chen\",\"doi\":\"10.3758/s13428-025-02600-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The measurement efficiency of a grid multidimensional computerized classification test (grid MCCT), which makes a classification decision per dimension, can be improved by taking the correlations between the dimensions into account in the termination criterion. The higher the correlations, the better the improvement in measurement efficiency. However, a termination criterion utilizing inter-dimensional information (i.e., SPRT-C; Liu et al., 2022) was found to yield lower levels of correct classification rates than not utilizing it (i.e., SPRT-SF; Seitz & Frey, 2013) under the between-item grid MCCT when the cutoff was set at the mean of the latent trait distribution. This study proposes a pre-rule to determine whether the SPRT-SF or SPRT-C should be used during the process of classification test administration. Through a series of simulation studies, the results showed that our proposed method (called P-SPRT) can substantially improve upon the SPRT-C in terms of correct classification rates, while maintaining its high measurement efficiency in terms of test length. This paper concludes with a discussion of the findings and further applications.</p>\",\"PeriodicalId\":8717,\"journal\":{\"name\":\"Behavior Research Methods\",\"volume\":\"57 2\",\"pages\":\"79\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11779759/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavior Research Methods\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3758/s13428-025-02600-x\",\"RegionNum\":2,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, EXPERIMENTAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavior Research Methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3758/s13428-025-02600-x","RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHOLOGY, EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
摘要
网格多维计算机分类测试(grid mct)是一种按维度进行分类决策的测试方法,通过在终止准则中考虑维度之间的相关性,可以提高测试效率。相关性越高,测量效率的提高越好。然而,利用维间信息的终止准则(即SPRT-C;Liu et al., 2022)被发现比不使用它产生更低的正确分类率(即SPRT-SF;Seitz & Frey, 2013)在项目间网格MCCT下,将截断点设置为潜在特质分布的平均值。本研究提出了在分类测验管理过程中使用SPRT-SF还是SPRT-C的预规则。通过一系列的仿真研究,结果表明我们提出的方法(P-SPRT)在正确分类率方面大大提高了SPRT-C,同时在测试长度方面保持了其较高的测量效率。本文最后讨论了研究结果和进一步的应用。
A pre-rule for the sequential probability ratio test in a between-item grid multidimensional computerized classification test.
The measurement efficiency of a grid multidimensional computerized classification test (grid MCCT), which makes a classification decision per dimension, can be improved by taking the correlations between the dimensions into account in the termination criterion. The higher the correlations, the better the improvement in measurement efficiency. However, a termination criterion utilizing inter-dimensional information (i.e., SPRT-C; Liu et al., 2022) was found to yield lower levels of correct classification rates than not utilizing it (i.e., SPRT-SF; Seitz & Frey, 2013) under the between-item grid MCCT when the cutoff was set at the mean of the latent trait distribution. This study proposes a pre-rule to determine whether the SPRT-SF or SPRT-C should be used during the process of classification test administration. Through a series of simulation studies, the results showed that our proposed method (called P-SPRT) can substantially improve upon the SPRT-C in terms of correct classification rates, while maintaining its high measurement efficiency in terms of test length. This paper concludes with a discussion of the findings and further applications.
期刊介绍:
Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.