{"title":"Correcting bias in extreme groups design using a missing data approach.","authors":"Lihan Chen, Rachel T Fouladi","doi":"10.1037/met0000508","DOIUrl":null,"url":null,"abstract":"<p><p>Extreme groups design (EGD) refers to the use of a screening variable to inform further data collection, such that only participants with the lowest and highest scores are recruited in subsequent stages of the study. It is an effective way to improve the power of a study under a limited budget, but produces biased standardized estimates. We demonstrate that the bias in EGD results from its inherent <i>missing at random</i> mechanism, which can be corrected using modern missing data techniques such as <i>full information maximum likelihood</i> (FIML). Further, we provide a tutorial on computing correlations in EGD data with FIML using R. (PsycInfo Database Record (c) 2024 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"1123-1131"},"PeriodicalIF":7.6000,"publicationDate":"2024-12-01","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/met0000508","RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PSYCHOLOGY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Extreme groups design (EGD) refers to the use of a screening variable to inform further data collection, such that only participants with the lowest and highest scores are recruited in subsequent stages of the study. It is an effective way to improve the power of a study under a limited budget, but produces biased standardized estimates. We demonstrate that the bias in EGD results from its inherent missing at random mechanism, which can be corrected using modern missing data techniques such as full information maximum likelihood (FIML). Further, we provide a tutorial on computing correlations in EGD data with FIML using R. (PsycInfo Database Record (c) 2024 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.
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