[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-590152-105":59,"doc-detail-590152-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","using-longitudinal-student-mobility-to-identify-at-risk-students","Using Longitudinal Student Mobility to Identify At-Risk Students","","The article uses administrative data from Massachusetts, Missouri, and Washington to examine how student mobility relates to educational outcomes across K–12 schooling. It distinguishes structural mobility from nonstructural mobility, defined as school changes not tied to normal transitions, and aggregates longitudinal measures of these events. Findings show that repeated nonstructural moves are a strong indicator of low test performance and reduced graduation likelihood, supporting low-cost identification through state longitudinal data systems.",{"@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/using-longitudinal-student-mobility-to-identify-at-risk-students/590152/",{"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/using-longitudinal-student-mobility-to-identify-at-risk-students/590152.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","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},"How does the study define structural versus nonstructural student mobility?","Question",{"text":112,"@type":113},"Structural mobility occurs when a move happens due to the schooling system’s normal structure (e.g., transitions between school levels). Nonstructural mobility is any school change for other reasons, such as residential moves tied to instability.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What relationship does the study find between nonstructural mobility and student outcomes?",{"text":117,"@type":113},"Nonstructural mobility is strongly associated with student risk status. Students with multiple nonstructural school changes score lower on high school test performance and show lower average graduation rates than students with no such moves.",{"name":119,"@type":110,"acceptedAnswer":120},"Why does the article emphasize longitudinal measures for identifying at-risk students?",{"text":121,"@type":113},"The paper argues that cumulative mobility history provides a stronger signal than cross-sectional information. It also claims longitudinal mobility contains risk information not captured by persistent poverty indicators like free and reduced-price lunch eligibility.","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},590152,1792636766,{"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. 1–13 DOI: 10. 1177/23328584211071090  \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)  \nUsing Longitudinal Student Mobility to Identify At-Risk Students  \nDan Goldhaber  \nUniversity of Washington Seattle Campus  \nAmerican Institutes for Research  \nCory Koedel  \nUniversity of Missouri  \nUmut Özek  \nAmerican Institutes for Research  \nEric Parsons  \nUniversity of Missouri  \nWe use administrative data from three states to document the relationships between geographic mobility and student outcomes during K–12 schooling. We focus specifically on nonstructural mobility events—which we define as school changes that do not occur as the result of normal transitions between schools—and on longitudinal measures that capture these events cumulatively for students. We show that the number of nonstructural moves experienced by a student is a powerful indicator of low-test performance and graduation rates. Longitudinal information on student mobility is unlikely to be readily available to local practitioners—that is, individual districts, schools, or teachers. However, due to recent investments in longitudinal data systems in most states, this information can be made available at low cost by state education agencies.  \nKeywords: achievement, at-risk students, descriptive analysis, economics of education, educational policy, longitudinal studies  \nIntroduction  \nWe use administrative data panels in three states—Massachusetts, Missouri, and Washington—to document the link between educational outcomes and longitudinal measures of student mobility across schools. We define two types of student mobility. The first, “structural mobility,” is when a student’s natural progression through the schooling system is such that a school move occurs due to district structure (e.g., at the transition point from elementary to middle school) . The second,“nonstructural mobility,” is when a student moves for another reason (e.g., a residential move due to economic instability; see, e.g., Been et al., 2011; Desmond & Gershenson, 2016) . Our goal is to examine the extent to which repeated nonstructural mobility during primary and secondary education is useful for identifying at-risk students.  \nWe show that nonstructural mobility is strongly associated with student risk status, as measured by low academic performance and graduation rates. For example, students in all three states who switch schools (nonstructurally) three times during Grades 3 to 12 score about 20 percentile points  \nlower in the distribution of test performance in high school and are 9 to 27 percentage points less likely to graduate from high school, on average, compared with students without any nonstructural moves.  \nOur findings are in line with existing evidence showing that mobile students are more likely to be at-risk than other students (Anderson & Leventhal, 2017; Barrat & Berliner, 2013; Courtney et al., 2004; Cowen, 2017; Cutuli et al., 2013; Ingersoll et al., 1989; Mehana & Reynolds, 2004; Rumberger & Larson, 1998) . Our unique contribution is to establish that the relationships between student mobility and outcomes are much stronger when the cumulative history of mobility is considered. In this way, our work mirrors findings by Michelmore and Dynarski (2017), who perform a conceptually similar analysis of the information conveyed by a student’s history of free and reduced-price lunch (FRL) eligibility to identify persistent poverty. Like their work, our findings point to the value of using longitudinal data to provide a more complete picture of student risk than is available from cross-sectional data. Moreover, we show that longitudinal mobility captures information that is not captured by persistent poverty.  \nCreative Commons Non Commercial CC BY-N","cbCaisbawSjcpbye","https://ap.wps.com/l/cbCaisbawSjcpbye","pdf",1097165,13,"English","# Introduction\n## Student mobility definitions and research goal\n## Relationship between nonstructural mobility and student risk\n## Policy implications and use of longitudinal data\n# Summary statistics\n## Table 1: Sample state characteristics compared with the United States","[{\"question\":\"How does the study define structural versus nonstructural student mobility?\",\"answer\":\"Structural mobility occurs when a move happens due to the schooling system’s normal structure (e.g., transitions between school levels). Nonstructural mobility is any school change for other reasons, such as residential moves tied to instability.\"},{\"question\":\"What relationship does the study find between nonstructural mobility and student outcomes?\",\"answer\":\"Nonstructural mobility is strongly associated with student risk status. Students with multiple nonstructural school changes score lower on high school test performance and show lower average graduation rates than students with no such moves.\"},{\"question\":\"Why does the article emphasize longitudinal measures for identifying at-risk students?\",\"answer\":\"The paper argues that cumulative mobility history provides a stronger signal than cross-sectional information. It also claims longitudinal mobility contains risk information not captured by persistent poverty indicators like free and reduced-price lunch eligibility.\"}]","Using Longitudinal Student Mobility to Identify At-Risk Students | PDF",1792068925,33]