Kylie Gorney, James A. Wollack, S. Sinharay, Carol Eckerly
{"title":"利用项目得分和干扰因素检测项目妥协和预知识","authors":"Kylie Gorney, James A. Wollack, S. Sinharay, Carol Eckerly","doi":"10.3102/10769986231159923","DOIUrl":null,"url":null,"abstract":"Any time examinees have had access to items and/or answers prior to taking a test, the fairness of the test and validity of test score interpretations are threatened. Therefore, there is a high demand for procedures to detect both compromised items (CI) and examinees with preknowledge (EWP). In this article, we develop a procedure that uses item scores and distractors to simultaneously detect CI and EWP. The false positive rate and true positive rate are evaluated for both items and examinees using detailed simulations. A real data example is also provided using data from an information technology certification exam.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"636 - 660"},"PeriodicalIF":1.9000,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Using Item Scores and Distractors to Detect Item Compromise and Preknowledge\",\"authors\":\"Kylie Gorney, James A. Wollack, S. Sinharay, Carol Eckerly\",\"doi\":\"10.3102/10769986231159923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Any time examinees have had access to items and/or answers prior to taking a test, the fairness of the test and validity of test score interpretations are threatened. Therefore, there is a high demand for procedures to detect both compromised items (CI) and examinees with preknowledge (EWP). In this article, we develop a procedure that uses item scores and distractors to simultaneously detect CI and EWP. The false positive rate and true positive rate are evaluated for both items and examinees using detailed simulations. A real data example is also provided using data from an information technology certification exam.\",\"PeriodicalId\":48001,\"journal\":{\"name\":\"Journal of Educational and Behavioral Statistics\",\"volume\":\"48 1\",\"pages\":\"636 - 660\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational and Behavioral Statistics\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3102/10769986231159923\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational and Behavioral Statistics","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.3102/10769986231159923","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Using Item Scores and Distractors to Detect Item Compromise and Preknowledge
Any time examinees have had access to items and/or answers prior to taking a test, the fairness of the test and validity of test score interpretations are threatened. Therefore, there is a high demand for procedures to detect both compromised items (CI) and examinees with preknowledge (EWP). In this article, we develop a procedure that uses item scores and distractors to simultaneously detect CI and EWP. The false positive rate and true positive rate are evaluated for both items and examinees using detailed simulations. A real data example is also provided using data from an information technology certification exam.
期刊介绍:
Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.