{"title":"阅读理解测试项目的反应需求——项目难度建模研究综述","authors":"Steve Ferrara, J. Steedle, R. Frantz","doi":"10.1080/08957347.2022.2103135","DOIUrl":null,"url":null,"abstract":"ABSTRACT Item difficulty modeling studies involve (a) hypothesizing item features, or item response demands, that are likely to predict item difficulty with some degree of accuracy; and (b) entering the features as independent variables into a regression equation or other statistical model to predict difficulty. In this review, we report findings from 13 empirical item difficulty modeling studies of reading comprehension tests. We define reading comprehension item response demands as reading passage variables (e.g., length, complexity), passage-by-item variables (e.g., degree of correspondence between item and text, type of information requested), and item stem and response option variables. We report on response demand variables that are related to item difficulty and illustrate how they can be used to manage item difficulty in construct-relevant ways so that empirical item difficulties are within a targeted range (e.g., located within the Proficient or other proficiency level range on a test’s IRT scale, where intended).","PeriodicalId":51609,"journal":{"name":"Applied Measurement in Education","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Response Demands of Reading Comprehension Test Items: A Review of Item Difficulty Modeling Studies\",\"authors\":\"Steve Ferrara, J. Steedle, R. Frantz\",\"doi\":\"10.1080/08957347.2022.2103135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Item difficulty modeling studies involve (a) hypothesizing item features, or item response demands, that are likely to predict item difficulty with some degree of accuracy; and (b) entering the features as independent variables into a regression equation or other statistical model to predict difficulty. In this review, we report findings from 13 empirical item difficulty modeling studies of reading comprehension tests. We define reading comprehension item response demands as reading passage variables (e.g., length, complexity), passage-by-item variables (e.g., degree of correspondence between item and text, type of information requested), and item stem and response option variables. We report on response demand variables that are related to item difficulty and illustrate how they can be used to manage item difficulty in construct-relevant ways so that empirical item difficulties are within a targeted range (e.g., located within the Proficient or other proficiency level range on a test’s IRT scale, where intended).\",\"PeriodicalId\":51609,\"journal\":{\"name\":\"Applied Measurement in Education\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Measurement in Education\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1080/08957347.2022.2103135\",\"RegionNum\":4,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Measurement in Education","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/08957347.2022.2103135","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Response Demands of Reading Comprehension Test Items: A Review of Item Difficulty Modeling Studies
ABSTRACT Item difficulty modeling studies involve (a) hypothesizing item features, or item response demands, that are likely to predict item difficulty with some degree of accuracy; and (b) entering the features as independent variables into a regression equation or other statistical model to predict difficulty. In this review, we report findings from 13 empirical item difficulty modeling studies of reading comprehension tests. We define reading comprehension item response demands as reading passage variables (e.g., length, complexity), passage-by-item variables (e.g., degree of correspondence between item and text, type of information requested), and item stem and response option variables. We report on response demand variables that are related to item difficulty and illustrate how they can be used to manage item difficulty in construct-relevant ways so that empirical item difficulties are within a targeted range (e.g., located within the Proficient or other proficiency level range on a test’s IRT scale, where intended).
期刊介绍:
Because interaction between the domains of research and application is critical to the evaluation and improvement of new educational measurement practices, Applied Measurement in Education" prime objective is to improve communication between academicians and practitioners. To help bridge the gap between theory and practice, articles in this journal describe original research studies, innovative strategies for solving educational measurement problems, and integrative reviews of current approaches to contemporary measurement issues. Peer Review Policy: All review papers in this journal have undergone editorial screening and peer review.