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Methods for sensitivity analyses address potential bias from unobserved confounders, illustrated through a summer school reading intervention evaluation.",{"@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/the-design-of-clustered-observational-studies-in-education/590009/",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/the-design-of-clustered-observational-studies-in-education/590009.png","ImageObject",300,407,{"name":42,"@type":43},"Cart","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-19","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},"What is the main goal of the proposed framework for clustered observational studies (COSs)?","Question",{"text":62,"@type":63},"To provide a design and critique framework for COSs that emulates a target randomized trial using the counterfactual model.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Why is understanding the assignment mechanism central in COS design?",{"text":67,"@type":63},"Because causal conclusions depend on how sites or groups were selected into treatment, and this affects potential selection bias and sensitivity to confounding.",{"name":69,"@type":60,"acceptedAnswer":70},"How do the methods handle uncertainty and possible unobserved confounders?",{"text":71,"@type":63},"They recommend best practices for statistical uncertainty in estimates and use sensitivity analyses to evaluate whether conclusions are robust to bias from unmeasured confounders.","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},590009,1792190646,{"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":144,"read_time":145},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","AERA Open  \nJuly-September 2020, Vol. 6, No. 3, pp. 1–14 DOI: 10. 1177/2332858420954401  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2020. [http://journals.sagepub.com/home/ero](http://journals.sagepub.com/home/ero)  \nThe Design of Clustered Observational Studies in Education  \nLindsay C. Page  \nUniversity of Pittsburgh  \nMatthew A. Lenard  \nHarvard University  \nLuke Keele  \nUniversity of Pennsylvania  \nClustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practices for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in a large U.S. school district.  \nKeywords: causal inference, hierarchical/multilevel data, observational study, optimal matching  \nIntroduction  \nThe effectiveness of educational interventions often is evaluated using clustered randomized trials (CRTs) in which random assignment occurs at the group rather than individual level (Hedges & Hedberg, 2007; Raudenbush, 1997) . Given the natural groupings of students within classrooms and schools as well as the practical and political challenges of individual-level random assignment in educational settings, CRTs are a common tool for drawing causal inferences about educational policies, practices, and innovations.  \nWhen an intervention is randomly assigned, differences in outcomes between treated and untreated groups can be causally attributed to the intervention. However, randomized trials, even with group-level assignment, are not always feasible. In such cases, researchers must turn to observational analyses. One alternative to a CRT is a clustered observational study (COS) . In a COS, treatment assignment occurs at the group level but through some uncontrolled process.  \nAlthough the literature on observational studies for deriving causal inferences when treatment selection occurs at the individual level is well developed (Rubin, 2007, 2008), the same is not true regarding COSs. For COSs, the literature remains underdeveloped, with no consensus on best practices. This is surprising in the context of educational research, where treatment selection often occurs at the group level. In this article, we outline the key considerations and steps for  \ndesigning and conducting a COS. We highlight differences between COSs and observational studies with individuallevel treatment selection, and we propose a framework for the design of COSs. In doing so, we review aspects of study design for observational studies and highlight how standard principles must be altered to handle clustered treatment assignment.  \nOur framework employs the counterfactual model for causal inference. We begin by advocating that investigators design COSs following the principle of target trial emulation—that is, according to the cluster randomized trial that they ideally would have conducted. Although the concept of target trial emulation is not new, we highlight considerations unique to the COS context and associated hierarchical data.  \nNext, we discuss the importance of understanding the process through which sites were selected into treatment. We discuss why cluste","cbCaisKauHmnqJrQ","https://ap.wps.com/l/cbCaisKauHmnqJrQ","pdf",597756,14,"English","# Introduction\n# Designing Clustered Observational Studies (COS)\n## Target trial emulation\n## Assignment mechanisms and key assumptions\n# Statistical adjustment and matching methods\n## Matching designed for COSs\n# Combining regression models with matching\n# Statistical uncertainty\n# Sensitivity analyses and unobserved confounding\n# Example: summer school reading intervention","[{\"question\":\"What is the main goal of the proposed framework for clustered observational studies (COSs)?\",\"answer\":\"To provide a design and critique framework for COSs that emulates a target randomized trial using the counterfactual model.\"},{\"question\":\"Why is understanding the assignment mechanism central in COS design?\",\"answer\":\"Because causal conclusions depend on how sites or groups were selected into treatment, and this affects potential selection bias and sensitivity to confounding.\"},{\"question\":\"How do the methods handle uncertainty and possible unobserved confounders?\",\"answer\":\"They recommend best practices for statistical uncertainty in estimates and use sensitivity analyses to evaluate whether conclusions are robust to bias from unmeasured confounders.\"}]","The Design of Clustered Observational Studies in Education | PDF",1792068598,35]