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Validity research often shows Black and Hispanic students’ freshman grade-point averages are lower than predicted and Asian students slightly higher. Using NYC public high school graduates from 2011 who enrolled at CUNY, the analysis finds no race/ethnicity differential within high schools once school factors are included; overprediction aligns with the enrolled Black and Hispanic proportion and is larger when models use high school test scores.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/differential-prediction-for-disadvantaged-students-and-schools-the-role-of-high-school-characteristics/590263/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/differential-prediction-for-disadvantaged-students-and-schools-the-role-of-high-school-characteristics/590263.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does “differential prediction” mean in this article?","Question",{"text":112,"@type":113},"Differential prediction refers to over- or underprediction of average freshman year grade-point averages for student subgroups based on race/ethnicity when admissions test scores are used.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the researchers test whether bias is driven by students or schools?",{"text":117,"@type":113},"They used data from students who graduated from New York City public high schools in 2011 and enrolled in the City University of New York, analyzing college admissions and high school test scores while accounting for high school characteristics.",{"name":119,"@type":110,"acceptedAnswer":120},"What was the key finding about race/ethnicity within high schools?",{"text":121,"@type":113},"There was no differential prediction based on race/ethnicity among students within the same high school after school characteristics were accounted for.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},590263,1792161600,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1374404737137,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","AERA Open  \nJanuary-December 2024, Vol. 10, No. 1, pp. 1–13 DOI: 10. 1177/23328584241245088  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2024. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero)  \nDifferential Prediction for Disadvantaged Students and Schools: The Role of High School Characteristics  \nPreeya P. Mbekeani  \nAmerican Institutes for Research  \nDaniel Koretz  \nHarvard University  \nValidity studies of college admissions tests have found that, on average, students who are Black or Hispanic earn lower freshman grade-point averages (FGPAs) than predicted by these test scores. This differential prediction is used as a measure of bias. These studies, however, conflate student and school characteristics. The differential prediction affecting minoritized groups may arise in part because they attended high schools in which college enrollees, regardless of race, perform worse than predicted. Using data on students who graduated from New York City public high schools in 2011 and enrolled in the City University of New York, we examined this using college admissions and high school test scores. There was no differential prediction based on race/ethnicity among students within high schools when school characteristics were accounted for. Instead, overprediction of FGPA was associated with the school proportion of enrolled Black and Hispanic students. Overprediction was larger in models with high school test scores.  \nKeywords: assessment, college admissions tests, differential prediction, hierarchical linear modeling, high schools, highstakes testing  \nIntroduction  \nSeveral decades of validity studies of college admissions tests have shown that test scores over- or underpredict the average freshman year grade point average (FGPA) for some student subgroups defined by race/ethnicity. We refer to both over- and underprediction as differential prediction. These studies have typically found that when college admissions test scores are used alone or in combination with high school grade point average (HSGPA) to predict FGPA, Black and Hispanic students’ FGPAs are moderately overpredicted—that is, their average FGPAs are lower than predicted—and Asian students’ FGPAs are slightly underpredicted (Bridgeman et al., 2000; Mattern et al., 2008; Young, 2001) .  \nDifferential prediction ofFGPAis of concern for two reasons. First, underprediction may be a form of bias, distorting decisions about admission. Second, student subgroups whose performance is overpredicted may underperform relative to expectations in college, which could jeopardize college completion.  \nThese validity studies, however, have used student-level ordinary least squares (OLS) regression models, and therefore, what appears to be differential prediction associated  \nwith individual characteristics may, in whole or in part, reflect characteristics of the high schools students attend. American high schools are highly segregated by race and income, and they vary along many dimensions of quality that may contribute to differential prediction (Owens et al., 2016; Reardon et al., 2000) . Schools serving predominantly Black, Hispanic, and/or low-income students rank lower on numerous measures, including teacher quality, access to advanced coursework, and support for applying to college (Clotfelter et al., 2005; Goldhaber et al., 2015; Klopfenstein, 2004; Lankford et al., 2002; Roderick et al., 2008) . Differences in school quality can impact skills and knowledge measured by tests, but they may also affect other knowledge, skills, and dispositions that are not reflected in test scores but that may influence college performance. For example, lack of access to challenging coursework in high school may leave students underprepared for the coursework and assessment demands of college. Lack of guidance from counselors and teachers in the college applicatio","cbCaihIwiNS13ddv","https://ap.wps.com/l/cbCaihIwiNS13ddv","pdf",480323,13,"English","# Introduction\n## Differential prediction and bias\n## Limits of student-level validity studies\n## High school segregation and quality differences","[{\"question\":\"What does “differential prediction” mean in this article?\",\"answer\":\"Differential prediction refers to over- or underprediction of average freshman year grade-point averages for student subgroups based on race/ethnicity when admissions test scores are used.\"},{\"question\":\"How did the researchers test whether bias is driven by students or schools?\",\"answer\":\"They used data from students who graduated from New York City public high schools in 2011 and enrolled in the City University of New York, analyzing college admissions and high school test scores while accounting for high school characteristics.\"},{\"question\":\"What was the key finding about race/ethnicity within high schools?\",\"answer\":\"There was no differential prediction based on race/ethnicity among students within the same high school after school characteristics were accounted for.\"}]","Differential Prediction for Disadvantaged Students and Schools - The Role of High School Characteristics | PDF",1792069196,33]