[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-589916-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-589916-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","how-to-create-and-use-state-level-policy-data-sets-in-education","How to Create and Use State-Level Policy Data Sets in Education","","How to Create and Use State-Level Policy Data Sets in Education outlines guidance for education policy researchers on building and analyzing detailed state-level policy data. It addresses limitations of treating policies as simple presence or absence and explains how to model policy “dosage” and targeted groups. The article uses state performance-based funding as an example and demonstrates difference-in-differences with continuous treatment variables to evaluate policy effectiveness across contexts.",{"@graph":14,"@context":72},[15,34,55],{"@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/how-to-create-and-use-state-level-policy-data-sets-in-education/589916/",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/how-to-create-and-use-state-level-policy-data-sets-in-education/589916.png","ImageObject",300,407,{"name":42,"@type":43},"Cart","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-11","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":30},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Why do researchers need detailed education policy data instead of binary indicators?","Question",{"text":62,"@type":63},"Binary coding of policy presence or absence can hide important details about which conditions make a policy effective or ineffective. The article emphasizes capturing dosage/percentage of funding and targeted groups.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"What is the main purpose of the article?",{"text":67,"@type":63},"The article aims to guide education policy researchers in constructing and analyzing detailed data sets for state-level policies, using performance-based funding in public higher education as an example.",{"name":69,"@type":60,"acceptedAnswer":70},"How does the article extend difference-in-differences analysis?",{"text":71,"@type":63},"It shows how to conduct difference-in-differences analyses with continuous treatment variables, enabling researchers to use more nuanced data and better interpret policy contexts.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},589916,1792068394,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"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":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"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":30,"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":79,"read_time":144,"preview_page_count":4},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","AERA Open  \nJuly-September 2019, Vol. 5, No. 3, pp. 1–14 DOI: 10. 1177/2332858419873619  \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)  \nHow to Create and Use State-Level Policy Data Sets in Education  \nResearch  \nRobert Kelchen  \nSeton Hall University  \nKelly Ochs Rosinger  \nPennsylvania State University  \nJustin C. Ortagus  \nUniversity of Florida  \nAs state governments seek to improve the performance of institutions of K–12 and higher education, they often adopt educational policies that have similar names but different characteristics across states and with variations over time within states. Yet quantitative analyses generally examine the absence or presence of an educational policy instead of diving into details such as the dosage or percentage of funding tied to a policy or the specific groups being targeted by the implementation of the policy. The aim of this article is to provide guidance for education policy researchers in constructing and analyzing detailed data that can inform the design of state-level policies, using state performance-based funding policies in public higher education as an example. We also show how to conduct difference-in-differences analyses with continuous treatment variables in order to take advantage of more-nuanced data and better understand the context in which policies are effective (or ineffective).  \nKeywords: state policies, data collection, difference-in-differences analyses  \nThe role of state governments in shaping educational policies is arguably stronger today than at any point in the past two decades. The passage of the Every Student Succeeds Act in 2015 to replace No Child Left Behind curtailed the power of the U.S. Department of Education over states’K–12 educational practices after years of critiques from both sides of the political aisle (Dillon, 2011; Klein, 2016; Krane, 2007) . In higher education, the Trump administration is moving to reverse a number of federal accountability provisions in higher education while many states are taking a more active role in attempting to improve colleges’ performance (Kelchen, 2018a) .  \nA large body of research has shown that innovations in K–12 and higher education adopted in one state often spread to other states through the policy diffusion process (e.g., Hearn, McLendon, & Linthicum, 2017; Lacy & Tandberg, 2014; McDermott, 2003; Mintrom, 1997; Mokher & McLendon, 2009), but distinctions in local needs and political preferences often lead states to function as policy laboratories, approaching similar policy issues in a range of different ways (Elazar, 1972; Karch, 2007). As a result, qualitative and quantitative research in education has shown a great deal of heterogeneity in how states adopt and interpret educational policies (Dougherty, Natow, Pheatt, & Reddy, 2016; Wong, Wing, Martin, & Krishnamachari, 2018) .  \nYet many quantitative analyses in both K–12 and higher education classify complex state policies into a small number of basic classifications. Numerous empirical studies examining policies that vary considerably across states, such as charter school policies and higher education funding strategies, treat such policies as being binary in nature (e.g., Finger, 2018; Wong & Shen, 2002) . Researchers often make these decisions for the purpose of methodological convenience: Quasi-experimental methods frequently used to generate causal estimates of policy impacts are easier to estimate using a binary treatment variable, and existing data sets often do not contain the detailed information required to construct a continuous treatment measure. Yet policy makers need to know how the details of state education policies affect student outcomes, as examining the simple absence or presence of a policy can obscure important details about which conditions make a","cbCaibnO7hljBwzz","https://ap.wps.com/l/cbCaibnO7hljBwzz","pdf",449045,14,"English","# Introduction\n## Data set construction for state-level education policies\n## Moving beyond binary policy measures\n## Difference-in-differences with continuous treatments","[{\"question\":\"Why do researchers need detailed education policy data instead of binary indicators?\",\"answer\":\"Binary coding of policy presence or absence can hide important details about which conditions make a policy effective or ineffective. The article emphasizes capturing dosage/percentage of funding and targeted groups.\"},{\"question\":\"What is the main purpose of the article?\",\"answer\":\"The article aims to guide education policy researchers in constructing and analyzing detailed data sets for state-level policies, using performance-based funding in public higher education as an example.\"},{\"question\":\"How does the article extend difference-in-differences analysis?\",\"answer\":\"It shows how to conduct difference-in-differences analyses with continuous treatment variables, enabling researchers to use more nuanced data and better interpret policy contexts.\"}]","How to Create and Use State-Level Policy Data Sets in Education | PDF",35]