Andrea Spoto, Massimo Nucci, Elena Prunetti, Michele Vicovaro
{"title":"通过正式的内容有效性分析改进评估工具的内容有效性评估。","authors":"Andrea Spoto, Massimo Nucci, Elena Prunetti, Michele Vicovaro","doi":"10.1037/met0000545","DOIUrl":null,"url":null,"abstract":"<p><p>Content validity is defined as the degree to which elements of an assessment instrument are relevant to and representative of the target construct. The available methods for content validity evaluation typically focus on the extent to which a set of items are relevant to the target construct, but do not afford precise evaluation of items' behavior, nor their exhaustiveness with respect to the elements of the target construct. Formal content validity analysis (FCVA) is a new procedure combining methods and techniques from various areas of psychological assessment, such as (a) constructing Boolean classification matrices to formalize relationships among an assessment instrument's items and target construct elements, and (b) computing interrater agreement indices. We discuss how FCVA can be extended through the implementation of a Bayesian procedure to improve the interrater agreement indices' accuracy (Bayesian formal content validity analysis [B-FCVA]). With respect to extant methods, FCVA and B-FCVA can provide a great amount of information about content validity while not demanding much more work for authors and experts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"203-222"},"PeriodicalIF":7.8000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving content validity evaluation of assessment instruments through formal content validity analysis.\",\"authors\":\"Andrea Spoto, Massimo Nucci, Elena Prunetti, Michele Vicovaro\",\"doi\":\"10.1037/met0000545\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Content validity is defined as the degree to which elements of an assessment instrument are relevant to and representative of the target construct. The available methods for content validity evaluation typically focus on the extent to which a set of items are relevant to the target construct, but do not afford precise evaluation of items' behavior, nor their exhaustiveness with respect to the elements of the target construct. Formal content validity analysis (FCVA) is a new procedure combining methods and techniques from various areas of psychological assessment, such as (a) constructing Boolean classification matrices to formalize relationships among an assessment instrument's items and target construct elements, and (b) computing interrater agreement indices. We discuss how FCVA can be extended through the implementation of a Bayesian procedure to improve the interrater agreement indices' accuracy (Bayesian formal content validity analysis [B-FCVA]). With respect to extant methods, FCVA and B-FCVA can provide a great amount of information about content validity while not demanding much more work for authors and experts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>\",\"PeriodicalId\":20782,\"journal\":{\"name\":\"Psychological methods\",\"volume\":\" \",\"pages\":\"203-222\"},\"PeriodicalIF\":7.8000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psychological methods\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1037/met0000545\",\"RegionNum\":1,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHOLOGY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psychological methods","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1037/met0000545","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/2 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
Improving content validity evaluation of assessment instruments through formal content validity analysis.
Content validity is defined as the degree to which elements of an assessment instrument are relevant to and representative of the target construct. The available methods for content validity evaluation typically focus on the extent to which a set of items are relevant to the target construct, but do not afford precise evaluation of items' behavior, nor their exhaustiveness with respect to the elements of the target construct. Formal content validity analysis (FCVA) is a new procedure combining methods and techniques from various areas of psychological assessment, such as (a) constructing Boolean classification matrices to formalize relationships among an assessment instrument's items and target construct elements, and (b) computing interrater agreement indices. We discuss how FCVA can be extended through the implementation of a Bayesian procedure to improve the interrater agreement indices' accuracy (Bayesian formal content validity analysis [B-FCVA]). With respect to extant methods, FCVA and B-FCVA can provide a great amount of information about content validity while not demanding much more work for authors and experts. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.