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Findings show strong precision-adjusted correlations in math and ELA, with discrepancies in ELA more pronounced in high socioeconomic districts.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/validating-the-seda-measures-of-district-educational-opportunities-via-a-common-assessment/590826/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/validating-the-seda-measures-of-district-educational-opportunities-via-a-common-assessment/590826.png","ImageObject",300,407,{"name":42,"@type":43},"Stanford","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-09","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What does the study validate regarding SEDA data?","Question",{"text":63,"@type":64},"It validates the convergent validity of SEDA growth estimates for mathematics and ELA by comparing them against growth estimates from MAP Growth assessments.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How are the SEDA growth estimates compared in the analysis?",{"text":68,"@type":64},"The study compares SEDA estimates with MAP Growth–derived achievement levels and growth estimates for districts that participate in both assessments.",{"name":70,"@type":61,"acceptedAnswer":71},"What differences are observed between SEDA and MAP Growth estimates?",{"text":72,"@type":64},"The correlations are strong, but small systematic discrepancies appear, with ELA discrepancies more pronounced in high socioeconomic districts, suggesting limited generalization in some states.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},590826,1792160560,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},2336477552062,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","AERA Open  \nApril-June 2019, Vol. 5, No. 2, pp. 1–18 DOI: 10. 1177/2332858419858324  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2019. [http://journals.sagepub.com/home/ero](http://journals.sagepub.com/home/ero)  \nValidating the SEDA Measures of District Educational Opportunities via a Common Assessment  \nMegan Kuhfeld  \nNWEA  \nThurston Domina  \nUniversity of North Carolina, Chapel Hill  \nPaul Hanselman  \nUniversity of California, Irvine  \nThe Stanford Educational Data Archive (SEDA) is the first data set to allow comparisons of district academic achievement and growth from Grades 3 to 8 across the United States, shining a light on the distribution of educational opportunities. This study describes a convergent validity analysis of the SEDA growth estimates in mathematics and English Language Arts (ELA) by comparing the SEDA estimates against estimates derived from NWEA’s MAP Growth assessments. We find strong precision-adjusted correlations between growth estimates from SEDA and MAP Growth in math (.90) and ELA (.82). We also find that the discrepancy between the growth estimates in ELA is slightly more pronounced in high socioeconomic districts. Our analyses indicate a high degree of congruence between the SEDA estimates and estimates derived from the vertically scaled MAP Growth assessment. However, small systematic discrepancies imply that the SEDA growth estimates are less likely to generalize to estimates obtained through MAP Growth in some states.  \nKeywords: accountability, achievement, hierarchical linear modeling, validity/reliability  \nIntroduction  \nThe Stanford Educational Data Archive (SEDA) districtlevel achievement and achievement growth data provide unprecedented insights into the spatial and social distribution of educational opportunity across the United States. Recent research using the SEDA data draws attention to the remarkable degree of variation across U.S. public schools in student achievement and achievement growth (Fahle & Reardon, 2018), the magnitude of racial and gender achievement gaps (Reardon, Kalogrides, & Shore, 2018), as well asthe effects of the Great Recession on student achievement (Shores & Steinberg, 2018) . SEDA compiles local achievement information from nearly all public school students in the United States and harmonizes these scores into a standardized scale. In doing so, SEDA allows detailed comparisons of school district mathematics and English language arts (ELA) test score means and standard deviations for students in third through eighth grades across U.S. public school districts. In addition, by comparing these means overtime (2008–2009 to 2014–2015 school years), the SEDA data allow for comparisons of third-to eighth-grade growth in mathematics and ELA skills across U.S. public school districts.  \nHowever, because SEDA infers continuously scaled district achievement scores from the vertically linked National Assessment of Educational Progress (NAEP) scale, these data are based on potentially important assumptions about the cross-state comparability of achievement growth. In this article, we draw on an alternative source of national testing data, namely, the MAP Growth 1 test scores, as a validation check for the SEDA estimates. MAP Growth assessments are administered in a consistent format and with a common scale to approximately 11 million students in over 9,500 schools (NWEA, 2018) . As such, they make it possible to estimate achievement levels in MAP Growth participating districts as well as achievement growth estimates to compare with SEDA’s estimates. Reardon, Kalogrides, and Ho (2018) used MAP Growth data as a validation check for the district mean scores, but the achievement growth estimates have not been compared previously against an external measure. By investigating the relationship between SEDA and MAP Growth achievement in the subset of U.S. public school dist","cbCaiaE9XSmY9EVo","https://ap.wps.com/l/cbCaiaE9XSmY9EVo","pdf",4402007,18,"English","# Introduction\n# A Unique Measure of Educational Opportunity Across the United States\n# Validation via MAP Growth and Analysis of Discrepancies","[{\"question\":\"What does the study validate regarding SEDA data?\",\"answer\":\"It validates the convergent validity of SEDA growth estimates for mathematics and ELA by comparing them against growth estimates from MAP Growth assessments.\"},{\"question\":\"How are the SEDA growth estimates compared in the analysis?\",\"answer\":\"The study compares SEDA estimates with MAP Growth–derived achievement levels and growth estimates for districts that participate in both assessments.\"},{\"question\":\"What differences are observed between SEDA and MAP Growth estimates?\",\"answer\":\"The correlations are strong, but small systematic discrepancies appear, with ELA discrepancies more pronounced in high socioeconomic districts, suggesting limited generalization in some states.\"}]","Validating the SEDA Measures of District Educational Opportunities via a Common Assessment | PDF",1792070509,45]