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This study examines the relationship between Frontline Education’s TeacherFit instrument and the outcomes of newly hired teachers. Results show that a 1 standard deviation increase in TeacherFit scores corresponds to a meaningful rise in principal evaluation scores. However, higher TeacherFit scores are also linked to greater likelihood of teacher turnover.",{"@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/can-a-commercial-screening-tool-help-select-better-teachers/603088/",{"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/can-a-commercial-screening-tool-help-select-better-teachers/603088.png","ImageObject",300,407,{"name":92,"@type":93},"MrHarris58","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","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 does the study examine regarding TeacherFit?","Question",{"text":112,"@type":113},"It studies how scores from Frontline Education’s TeacherFit instrument relate to outcomes for newly hired teachers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are TeacherFit scores associated with teachers’ evaluation outcomes?",{"text":117,"@type":113},"A 1 standard deviation increase in an index of TeacherFit scores is associated with a 0.06 standard deviation increase in evaluation scores.",{"name":119,"@type":110,"acceptedAnswer":120},"What additional finding relates TeacherFit scores to teacher retention?",{"text":121,"@type":113},"Teachers with higher TeacherFit scores are more likely to leave their hiring schools the following year.","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},603088,1792211065,{"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":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},687212988360,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Briefs  \nEducational Evaluation and Policy Analysis September 2023, Vol. 45, No. 3, pp. 530–539 DOI: 10. 3102/01623737221131547  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© 2022 AERA. [https://journals.sagepub.com/home/epa](https://journals.sagepub.com/home/epa)  \nCan a Commercial Screening Tool Help Select Better Teachers?  \nOlivia L. Chi  \nBoston University  \nMatthew A. Lenard  \nHarvard University  \nImproving teacher selection is an important strategy for strengthening the quality of the teacher workforce. As districts adopt commercial teacher screening tools, evidence is needed to understand these tools’ predictive validity. We examine the relationship between Frontline Education’s TeacherFit instrument and newly hired teachers’outcomes. We find that a 1 SD increase on an indexof TeacherFit scores is associated with a 0.06 SD increase in evaluation scores. However, we also find evidence that teachers with higher TeacherFit scores are more likely to leave their hiring schools the following year. Our results suggest that TeacherFit is not necessarily a substitute for more rigorous screening processes that are conducted by human resources officials, such as those documented in recent studies.  \nKeywords: school/teacher effectiveness, teacher characteristics, educational policy, econometric analysis  \nIntroduction  \nAn extensive literature documents substantial variation in teacher quality, and evidence shows that teacher quality impacts students’ long-run outcomes (see Jackson et al., 2014 for a review) . An important strategy for strengthening the quality of the teacher workforce is to identify and hire the applicants who will be most effective in the classroom. Recent studies suggest that information gathered during a district’s application-screening process could be compiled to make predictions about teacher effectiveness. Goldhaber et al. (2017) find that scores from teacher selection rubrics used to rate applicants in Spokane Public Schools predict teacher valueadded to test scores and teacher retention. Using data from Washington, D.C., Jacob et al. (2018) find that applicants’ background measures (e.g.,  \nundergraduate GPA) and scores on screening measures are predictive of teachers’ evaluation scores. Bruno and Strunk (2019) use data from the Los Angeles Unified School District to show that scores from the district office’s standardized screening system are predictive of teacher impacts on test scores, evaluation scores, and attendance. Sajjadiani et al. (2019), applying machine learning techniques to data from the Minneapolis Public School District, find that the relevance of applicants’ work experience and their attributions for leaving past jobs predict teacher performance (student evaluations, observation scores, and value-added to test scores) and turnover.  \nThese recent studies demonstrate promising ways in which information from the time of application can be harnessed to select better teachers. However, in these studies, districts’  \nScreening Tool for Teacher Selection  \ncentral office human resources officials play a large role in prescreening and scoring applications. This systematic collection and scoring of applicant information for use in hiring decisions may be a barrier in some school districts. Prior research suggests that district central offices vary in how they support schools in recruiting and screening teacher applicants, and principals vary in the degree to which they use data in their hiring decisions (Cannata et al., 2017) . The teacher hiring process may even be rushed and information-poor (Liu & Johnson, 2006) .  \nTo overcome this limitation, school districts across the United States are turning to the private sector for new teacher screening tools that claim to use “big data” to help identify better teachers from the pool of teacher applicants (Flanigan, 2016; Simon, 2014) . Commercial screening instru","cbCaindYCczMOs4E","https://ap.wps.com/l/cbCaindYCczMOs4E","pdf",381357,"English","# Introduction\n# Screening Tool for Teacher Selection\n# Data and Predictive Validity\n# Results","[{\"question\":\"What does the study examine regarding TeacherFit?\",\"answer\":\"It studies how scores from Frontline Education’s TeacherFit instrument relate to outcomes for newly hired teachers.\"},{\"question\":\"How are TeacherFit scores associated with teachers’ evaluation outcomes?\",\"answer\":\"A 1 standard deviation increase in an index of TeacherFit scores is associated with a 0.06 standard deviation increase in evaluation scores.\"},{\"question\":\"What additional finding relates TeacherFit scores to teacher retention?\",\"answer\":\"Teachers with higher TeacherFit scores are more likely to leave their hiring schools the following year.\"}]","Can a Commercial Screening Tool Help Select Better Teachers? | PDF",1792095691,25]