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Using an inferential reading comprehension test administered to 625 third-, fourth-, and fifth-grade students, the study evaluates four subscore creation methods. Three subscores derived from patterns of incorrect answers and one efficiency-based score show reliability, with the incorrect-answer subscores improving logistic regression prediction of not reaching statewide proficiency. Models with subscores also yield higher ROC AUC statistics, and grade-specific findings are analyzed to illustrate practical implications for risk identification and reporting.",{"@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/constructing-subscores-that-add-validity-a-case-study-of-identifying-students-at-risk/601893/",{"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/constructing-subscores-that-add-validity-a-case-study-of-identifying-students-at-risk/601893.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-20","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why do subscores sometimes add little value compared with a total score?","Question",{"text":112,"@type":113},"Prior work suggests subscores from a single achievement test may not outperform a single total score because the subdomains may not provide a sound basis for subscores. 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ROC analyses also showed higher AUC values for models that included subscores.","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},601893,1792210896,{"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":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":31},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Constructing Subscores That Add Validity: A Case Study of Identifying Students at Risk  \nEducational and Psychological Measurement  \n2019, Vol. 79(1) 65–84  \n􀀂 The Author(s) 2018  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/0013164418763255](DOI: 10.1177/0013164418763255)[ ](DOI: 10.1177/0013164418763255)[journals.sagepub.com/home/epm](journals.sagepub.com/home/epm)  \nGina Biancarosa 1, Patrick C. Kennedy 1,  \nSarah E. Carlson 1, HyeonJin Yoon 1, Ben Seipel2,3,  \nBowen Liu4, and Mark L. Davison4  \nAbstract  \nPrior research suggests that subscores from a single achievement test seldom add value over a single total score. Such scores typically correspond to subcontent areas in the total content domain, but content subdomains might not provide a sound basis for subscores. Using scores on an inferential reading comprehension test from 625 third, fourth, and fifth graders, two new methods of creating subscores were explored. Three subscores were based on the types of incorrect answers given by students. The fourth was based on temporal efficiency in giving correct answers. All four scores were reliable. The three subscores based on incorrect answers added value and validity. In logistic regression analyses predicting failure to reach proficiency on a statewide test, models including subscores fit better than the model with a single total score. Including the pattern of incorrect responses improved fit in all three grades, whereas including the comprehension efficiency score only modestly improved fit in fourth and fifth grades, but not third grade. Area under the curve (AUC) statistics from receiver operating characteristic (ROC) curves based on the various models were higher for models including subscores than those without subscores. Implications for using models with and without subscores are illustrated and discussed.  \nKeywords  \ndiagnostic testing, formative assessment, at-risk screening, reading comprehension, subscores  \n1 University of Oregon, Eugene, OR, USA  \n2University of Wisconsin–River Falls, River Falls, WI, USA 3California State University, Chico, CA, USA 4University of Minnesota, Minneapolis, MN, USA  \nCorresponding Author:  \nMark L. Davison, Department of Educational Psychology, University of Minnesota, 56 East River Road, Minneapolis, MN 55455, USA.  \n[Email: mld@umn.edu](Email: mld@umn.edu)  \nGuidelines for educational assessment often recommend reporting diagnostic information to guide students and teachers in addressing specific needs (e.g., the Elementary and Secondary Education Act of 2001 and 2015), but how can we increase the diagnostic information provided by assessments? One way is by reporting subscores as well as a total score, although researchers have found that subscores often provide relatively little added value over a single total score (Haberman, 2008a; Lyren, 2009; Puhan, Sinharay, Haberman, & Larkin, 2010; Sinharay, 2010) . For instance, Puhan et al. found that in a mathematics test for beginning teachers containing four subcontent areas (concepts, integrate knowledge, models, real-life problems), one could estimate a person’s true score in one of the content areas (say concepts) more accurately from their total test score than from their subscore in concepts. In these studies, subscores most often corresponded to the number of items correct in subareas of the broader content domain (e.g., real-life problems in the test of mathematics) . However, this is not the only way to create subscores. For instance, subscores can be used to quantify the number of incorrect responses of a given type that a student makes or the efficiency with which a student can correctly answer items.  \nIn addition, past evaluations of subscore utility have been based primarily on the internal structure of the subscores: factor structure or internal consistency reliability (e.g., Puhan et al., 2010; Sinharay, 2010) . Th","cbCaii9RC1Q8L6Pt","https://ap.wps.com/l/cbCaii9RC1Q8L6Pt","pdf",313450,"English","# Abstract\n## Subscore Types","[{\"question\":\"Why do subscores sometimes add little value compared with a total score?\",\"answer\":\"Prior work suggests subscores from a single achievement test may not outperform a single total score because the subdomains may not provide a sound basis for subscores. The article argues for exploring alternative construction approaches beyond internal structure alone.\"},{\"question\":\"How were the subscores constructed in this study?\",\"answer\":\"The study explored two approaches: three subscores based on the types of incorrect answers students gave, and a fourth based on temporal efficiency in producing correct answers (minutes per correct response).\"},{\"question\":\"Do models that include subscores improve identification of at-risk students?\",\"answer\":\"Yes. In logistic regression models predicting failure to reach proficiency, including subscores fit better than using only a single total score. ROC analyses also showed higher AUC values for models that included subscores.\"}]","Constructing Subscores That Add Validity - A Case Study of Identifying Students at Risk | PDF",1792093640]