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It defines quantile regression, highlights advantages over ordinary least squares (OLS), and compares the two approaches in detail. The paper demonstrates how quantile regression can estimate intervention effects with education data from a large-scale field experiment, including settings with dropouts. Estimating quantile treatment effects at different points clarifies predictor impacts across the outcome distribution, including tail regions.",{"@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-quantile-regression-to-estimate-intervention-effects-beyond-the-mean/601915/",{"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-quantile-regression-to-estimate-intervention-effects-beyond-the-mean/601915.png","ImageObject",300,407,{"name":92,"@type":93},"CatatanPagi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is quantile regression and why is it useful compared with OLS?","Question",{"text":112,"@type":113},"Quantile regression models effects at different quantiles of the outcome distribution. It is advantageous over OLS because mean estimates can be distorted by outliers and skewness.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper compare ordinary least squares (OLS) and quantile regression?",{"text":117,"@type":113},"It first defines quantile regression and explains its advantages vis-a-vis OLS, then provides specific comparisons between ordinary least squares and quantile regression methods.",{"name":119,"@type":110,"acceptedAnswer":120},"How is quantile regression applied to estimate intervention effects in the study?",{"text":121,"@type":113},"Using education data from a large-scale experiment, the paper demonstrates estimating intervention effects at various quantiles, including discussion of how estimation behaves when dropouts are present.","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},601915,1792451749,{"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":81,"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},962090894170,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Using Quantile Regression to Estimate Intervention Effects Beyond the Mean  \nEducational and Psychological Measurement  \n2019, Vol. 79(5) 883–910  \n􀀂 The Author(s) 2019  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/0013164419837321](DOI: 10.1177/0013164419837321)[ ](DOI: 10.1177/0013164419837321)[journals.sagepub.com/home/epm](journals.sagepub.com/home/epm)  \nSpyros Konstantopoulos 1, Wei Li2, Shazia Miller3 and Arie van der Ploeg4  \nAbstract  \nThis study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-`a-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).  \nKeywords  \nquantile regression, instrumental variables, interim assessments, achievement gap, field experiment, OLS regression  \nEmpirical quantitative analyses in education, psychology and the social sciences typically use linear statistical models such as ordinary least squares (OLS) regression, analysis of variance or covariance, or weighted linear models (e.g., multilevel) to compute either average estimates of associations between a dependent and an  \n1 Michigan State University, East Lansing, MI, USA 2University of Alabama, Tuscaloosa, AL, USA 3National Opinion Research Center, Chicago, IL, USA 4American Institutes for Research, Chicago, IL, USA  \nCorresponding Author:  \nSpyros Konstantopoulos, Michigan State University, 460 Erickson Hall, East Lansing, MI 48824, USA. Email: [spyros@msu.edu](spyros@msu.edu)  \nindependent variable or group differences in the dependent variable controlling for the other independent variables included in the model. The regression coefficients produced by such linear modeling approaches are mean estimates adjusted usually by the effects of the covariates that are present in the model, especially when observational or quasi-experimental data are analyzed. For example, in OLS regression, oneof the most commonly used modeling approaches in the social sciences, a regression coefficient indicates the ‘‘effect’’ of an independent variable x on the mean of the dependent variable y given the effects of the remaining independent variables (also called predictors) in the model. This statistical approach is often known as conditional-mean modeling (see Hao & Naiman, 2007) .  \nConsider the following example from education. Suppose a researcher is interested in whether teacher certification has an impact on student mathematics performance. In a simple regression model, one would regress mathematics scores on teacher certification which for simplicity can be coded as a binary indicator variable taking the value of 1 if a teacher is certified and the value of 0 if the teacher is not certified. The regression estimate in this case is the mean difference in mathematics scores between students who have certified teachers and students who have not. A positive and significant mean difference would suggest that students who have certified teachers have a significantly higher mean in mathematics than students who have noncertified teachers. If covariates or statistical controls such as student background, prior student ability, as well as other teacher and school characteristics were to be included in the regression model the regression estimate of the teacher certification variable would be modified to a degree, ","cbCaivYe3iNnKvQ2","https://ap.wps.com/l/cbCaivYe3iNnKvQ2","pdf",434822,28,"English","# Abstract\n## Quantile regression overview and advantages\n## Comparison with OLS regression\n## Application to intervention effects with education data\n## Quantile treatment effects under dropouts","[{\"question\":\"What is quantile regression and why is it useful compared with OLS?\",\"answer\":\"Quantile regression models effects at different quantiles of the outcome distribution. It is advantageous over OLS because mean estimates can be distorted by outliers and skewness.\"},{\"question\":\"How does the paper compare ordinary least squares (OLS) and quantile regression?\",\"answer\":\"It first defines quantile regression and explains its advantages vis-a-vis OLS, then provides specific comparisons between ordinary least squares and quantile regression methods.\"},{\"question\":\"How is quantile regression applied to estimate intervention effects in the study?\",\"answer\":\"Using education data from a large-scale experiment, the paper demonstrates estimating intervention effects at various quantiles, including discussion of how estimation behaves when dropouts are present.\"}]","Using Quantile Regression to Estimate Intervention Effects Beyond the Mean | PDF",1792093688,71]