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It proposes a comprehensive analytic framework combining school-level metrics for exclusionary discipline prevalence and disparity, alongside regression-based approaches assessing students’ likelihood of experiencing exclusionary discipline. Applied to New York City, the results show Black students have the highest prevalence, disproportionality, and likelihood of office discipline referrals and suspensions, with differential processing for similar infractions.",{"@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/the-dimensions-of-school-discipline-toward-a-comprehensive-framework-for-measuring-discipline-patterns-and-outcomes-in-schools/590161/",{"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/the-dimensions-of-school-discipline-toward-a-comprehensive-framework-for-measuring-discipline-patterns-and-outcomes-in-schools/590161.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main contribution of the article?","Question",{"text":112,"@type":113},"The article presents a comprehensive analytic framework for measuring school discipline patterns, including prevalence/disparity metrics and regression-based assessments of students’ likelihood of exclusionary discipline.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and context are used to apply the framework?",{"text":117,"@type":113},"The framework is applied to New York City schools using school-level metrics and regression models to evaluate discipline patterns and outcomes.",{"name":119,"@type":110,"acceptedAnswer":120},"Which student group shows the highest prevalence and disproportionality?",{"text":121,"@type":113},"Black students show the highest prevalence of exclusionary discipline and the highest disproportionality in the school-level metrics.","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},590161,1792131621,{"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":29,"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 2022, Vol. 8, No. 1, pp. 1–23 DOI: 10. 1177/23328584221083669  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2022. [https://journals.sagepub.com/home/ero](https://journals.sagepub.com/home/ero)  \nThe Dimensions of School Discipline: Toward a Comprehensive Framework for Measuring Discipline Patterns and Outcomes in Schools  \nLuis A. Rodriguez   \nRichard O. Welsh  \nNew York University  \nThe school discipline literature has expanded rapidly in recent decades, yet the conceptualization and measurement of school discipline patterns remains overlooked. In this paper, we present a comprehensive analytic framework to examine school discipline patterns that encompasses school-level metrics that capture the prevalence and disparity in exclusionary discipline and regression-based approaches that examine the likelihood that students experience exclusionary discipline. We apply the framework to New York City and, based on school-level metrics, find that Black students have the highest prevalence and the highest disproportionality. Results from regression models affirm that Black students are most likely to receive office discipline referrals and suspensions and experience differential processing of suspensions for similar categories of infractions.  \nThe findings illustrate the nuances of the disciplinary process in schools and inform the consideration of a range of available analytic tools that educational stakeholders may employ to better measure and understand exclusionary discipline.  \nKeywords: school discipline, student discipline, suspensions, disparities, equity, race, descriptive analysis, regression analyses, research methodology  \nSchool discipline is an issue of utmost importance to educational policymakers, researchers, practitioners, and stakeholders because of long-standing disparities in who receives punishment and experiences the impact of exclusionary discipline on education and long-term life outcomes. Students with disabilities, non-heterosexual youth, low-socioeconomic-status students, low-performing students, Black, Latinx, and male students experience exclusionary discipline (e.g., suspensions and expulsions) at higher rates than their peers (Shores et al., 2020; Skiba et al., 2014; Welsh & Little, 2018a, 2018b). For instance, in 2017-2018, Black students represented 15.1% of total K-12 enrollment, yet Black students represented 38.8% of expulsions, 38.2% of one or more out-of-school suspensions (OSS), and 31.4% of one or more in-school suspensions (ISS) (Office for Civil Rights, 2021) . These patterns are alarming considering the detrimental effects of exclusionary discipline on student academic performance and long-term life outcomes. Students lose millions of instructional days due to suspensions (Losen & Whitaker, 2017) . Recent studies have confirmed the adverse impact of suspensions on student achievement (Anderson et al., 2019; Lacoe & Steinberg, 2019), and some scholars have contended that the racial, income, and gender disparities in exclusionary disciplinary outcomes contribute to achievement gaps (Gregory et al., 2010; Losen et al., 2015) .  \nAs policymakers and practitioners craft solutions to racial inequality in school discipline, there is a need for a granular  \nunderstanding of the use of exclusionary discipline in schools in order to design school discipline reforms and target support within districts. Although the empirical literature on school discipline has expanded rapidly in recent decades, the conceptualization and measurement of the rates of and disparities in school discipline outcomes continues to develop (Curran, 2020; Girvan et al., 2019; Nishioka, 2017) . Prior studies examining the strengths and weaknesses of school discipline metrics have focused predominantly on disparities using risk ratios, risk differences, or raw differential representation and illustra","cbCaikKVqyMgtgDU","https://ap.wps.com/l/cbCaikKVqyMgtgDU","pdf",2407546,23,"English","# Introduction\n## Measurement gaps and need for a comprehensive framework\n# Framework and Methods\n## School-level metrics and regression-based likelihood models\n# Findings\n## Prevalence, disproportionality, and differential processing\n# Implications\n## Analytic tools for stakeholders and policy reform","[{\"question\":\"What is the main contribution of the article?\",\"answer\":\"The article presents a comprehensive analytic framework for measuring school discipline patterns, including prevalence/disparity metrics and regression-based assessments of students’ likelihood of exclusionary discipline.\"},{\"question\":\"What data and context are used to apply the framework?\",\"answer\":\"The framework is applied to New York City schools using school-level metrics and regression models to evaluate discipline patterns and outcomes.\"},{\"question\":\"Which student group shows the highest prevalence and disproportionality?\",\"answer\":\"Black students show the highest prevalence of exclusionary discipline and the highest disproportionality in the school-level metrics.\"}]","The Dimensions of School Discipline - Toward a Comprehensive Framework for Measuring Discipline Patterns and Outcomes in Schools | PDF",1792068950,58]