{"title":"潜在能力的自适应权重估计:在计算机自适应测试中的应用","authors":"Shiyu Wang, Houping Xiao, A. Cohen","doi":"10.3102/1076998620972800","DOIUrl":null,"url":null,"abstract":"An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"46 1","pages":"560 - 591"},"PeriodicalIF":1.9000,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3102/1076998620972800","citationCount":"1","resultStr":"{\"title\":\"Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing With Response Revision\",\"authors\":\"Shiyu Wang, Houping Xiao, A. Cohen\",\"doi\":\"10.3102/1076998620972800\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.\",\"PeriodicalId\":48001,\"journal\":{\"name\":\"Journal of Educational and Behavioral Statistics\",\"volume\":\"46 1\",\"pages\":\"560 - 591\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2020-11-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3102/1076998620972800\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational and Behavioral Statistics\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.3102/1076998620972800\",\"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/10.3102/1076998620972800","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}
Adaptive Weight Estimation of Latent Ability: Application to Computerized Adaptive Testing With Response Revision
An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.
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.