[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-601868-105":59,"doc-detail-601868-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","estimating-true-student-growth-percentile-distributions-using-latent-regression-multidimensional-irt-models","Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models","","Student Growth Percentiles (SGPs) are used to infer student achievement growth and educator effectiveness, yet SGPs estimated from observed test scores contain substantial measurement error. This study develops a latent regression framework within multidimensional IRT models to model distributional properties of true SGPs. Using longitudinal data from 330,000+ students, the authors quantify cross-subject correlations, examine links to student characteristics, test modest accuracy improvements, and analyze teacher-level averages.",{"@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/estimating-true-student-growth-percentile-distributions-using-latent-regression-multidimensional-irt-models/601868/",{"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/estimating-true-student-growth-percentile-distributions-using-latent-regression-multidimensional-irt-models/601868.png","ImageObject",300,407,{"name":92,"@type":93},"ahduag","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What are Student Growth Percentiles (SGPs) and why are they used?","Question",{"text":112,"@type":113},"SGPs are percentile ranks of a student’s current achievement relative to peers with similar prior achievement. They are appealing because the percentile scale is intuitive and ranking against similar prior achievement is viewed as fair for evaluating progress and educator effectiveness.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why do estimated SGPs differ from true (latent) SGPs?",{"text":117,"@type":113},"Estimated SGPs are derived from observed test scores that are themselves error-prone measures of latent achievement. These errors make estimated SGPs noisy proxies for true SGPs, weakening inferences from them.",{"name":119,"@type":110,"acceptedAnswer":120},"How does the paper propose improving SGP estimation accuracy?",{"text":121,"@type":113},"The authors use a latent regression multidimensional IRT model framework to leverage distributional relationships between true SGPs and auxiliary information. They evaluate how such relationships can improve accuracy, finding gains even under optimal conditions are modest.","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},601868,1792210547,{"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":24,"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},3985747870414,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models  \nEducational and Psychological Measurement  \n2017, Vol. 77(6) 917–944  \n􀀂 The Author(s) 2016 Reprints and permissions:  \n[sagepub.com/journalsPermissions.nav](sagepub.com/journalsPermissions.nav)[ ](sagepub.com/journalsPermissions.nav)[DOI: 10.1177/0013164416659686](DOI: 10.1177/0013164416659686)[ ](DOI: 10.1177/0013164416659686)[journals.sagepub.com/home/epm](journals.sagepub.com/home/epm)  \nJ. R. Lockwood 1 and Katherine E. Castellano 1  \nAbstract  \nStudent Growth Percentiles (SGPs) increasingly are being used in the United States for inferences about student achievement growth and educator effectiveness. Emerging research has indicated that SGPs estimated from observed test scores have large measurement errors. As such, little is known about ‘‘true’’ SGPs, which are defined in terms of nonlinear functions of latent achievement attributes for individual students and their distributions across students. We develop a novel framework using latent regression multidimensional item response theory models to study distributional properties of true SGPs. We apply these methods to several cohorts of longitudinal item response data from more than 330,000 students in a large urban metropolitan area to provide new empirical information about true SGPs. We find that true SGPs are correlated 0.3 to 0.5 across mathematics and English language arts, and that they have nontrivial relationships with individual student characteristics, particularly student race/ethnicity and absenteeism. We evaluate the potential of using these relationships to improve the accuracy of SGPs estimated from observed test scores, finding that accuracy gains even under optimal circumstances are modest. We also consider the properties of SGPs averaged to the teacher level, widely used for teacher evaluations. We find that average true SGPs for individual teachers vary substantially as a function of the characteristics of the students they teach. We discuss implications of our findings for the estimation and interpretation of SGPs at both the individual and aggregate levels.  \nKeywords  \nacademic growth, ability estimation, Bayesian estimation, auxiliary information, teacher value-added  \n1 Educational Testing Service, Princeton, NJ, USA  \nCorresponding Author:  \nJ. R. Lockwood, Educational Testing Service, 660 Rosedale Rd, Princeton, NJ 08541, USA.  \nEmail: [jrlockwood@ets.org](jrlockwood@ets.org)  \nA Student Growth Percentile (SGP) is the percentile rank of a student’s current achievement among students with similar prior achievement (Betebenner, 2009) . For example, a student whose current achievement is at the 70th percentile among students matched to him/her with respect to prior achievement would have an SGP equal to 70 . Two features of this definition make SGPs appealing. First, the percentile rank scale is familiar and interpretable, and remains well-defined even if test scores are not vertically or even intervally scaled (Betebenner, 2009; Briggs & Betebenner, 2009; Castellano & Ho, 2013) . Second, ranking students against other students with similar prior achievement is perceived as more fair and relevant to evaluating both individual student progress and educator effectiveness than simply examining unadjusted achievement levels (Betebenner, 2009) . These benefits have contributed to the increasing use of SGPs in the United States.  \nHowever, recent research has demonstrated that SGPs estimated from standardized test scores suffer from large estimation errors (Akram, Erickson, & Meyer, 2013; Lockwood & Castellano, 2015; McCaffrey, Castellano, & Lockwood, 2015; Monroe & Cai, 2015; Shang, Van Iwaarden, & Betebenner, 2015) . Both the prior and current test scores used in SGP calculations are error-prone measures of their corresponding latent achievement traits due to the finite number of items on each test (Lord, 1980) . These errors combine to make es","cbCaioO21IRuNXAM","https://ap.wps.com/l/cbCaioO21IRuNXAM","pdf",262611,28,"English","# Abstract\n## Student Growth Percentiles: definition and appeal\n## Measurement error in estimated SGPs\n## Threats to inference validity\n## Latent regression multidimensional IRT framework\n## Empirical findings and implications","[{\"question\":\"What are Student Growth Percentiles (SGPs) and why are they used?\",\"answer\":\"SGPs are percentile ranks of a student’s current achievement relative to peers with similar prior achievement. They are appealing because the percentile scale is intuitive and ranking against similar prior achievement is viewed as fair for evaluating progress and educator effectiveness.\"},{\"question\":\"Why do estimated SGPs differ from true (latent) SGPs?\",\"answer\":\"Estimated SGPs are derived from observed test scores that are themselves error-prone measures of latent achievement. These errors make estimated SGPs noisy proxies for true SGPs, weakening inferences from them.\"},{\"question\":\"How does the paper propose improving SGP estimation accuracy?\",\"answer\":\"The authors use a latent regression multidimensional IRT model framework to leverage distributional relationships between true SGPs and auxiliary information. They evaluate how such relationships can improve accuracy, finding gains even under optimal conditions are modest.\"}]","Estimating True Student Growth Percentile Distributions Using Latent Regression Multidimensional IRT Models | PDF",1792093585,71]