{"id":12741,"date":"2022-08-17T15:46:09","date_gmt":"2022-08-17T19:46:09","guid":{"rendered":"https:\/\/www.philasd.org\/research\/?p=12741"},"modified":"2022-08-19T10:36:54","modified_gmt":"2022-08-19T14:36:54","slug":"analysis-of-enrollment-demographics-school-climate-and-school-staffing-relationship-to-academic-performance","status":"publish","type":"post","link":"https:\/\/www.philasd.org\/research\/2022\/08\/17\/analysis-of-enrollment-demographics-school-climate-and-school-staffing-relationship-to-academic-performance\/","title":{"rendered":"An Analysis of How Enrollment Demographics, School Climate, and School Staffing are Related to Academic Performance"},"content":{"rendered":"
[vc_row][vc_column][vc_column_text]The Philadelphia Board of Education has established Goals & Guardrails<\/a> that outline what School District of Philadelphia students must know and be able to accomplish, and describe the conditions needed in each school to empower all students to succeed in and beyond the classroom.<\/p>\n

When selecting which data to use to monitor progress towards these goals, the District used prior research that identified relationships between certain data points and school-level outcomes. The selected data points reflect a broad range of specific, measurable characteristics of schools, but they can also be seen as three distinct \u201csets\u201d of data points reflecting enrollment demographics<\/em> (who are the students in our schools?), school staffing<\/em> (who are the staff working in our schools?), and school climate<\/em> (how safe and welcoming are our schools?).<\/p>\n

Using these three sets of data points, this brief’s guiding research questions are:<\/p>\n

    \n
  1. In schools serving grades K-8:<\/strong> Which sets of predictor variables [enrollment demographics, school climate, and school staffing], best predict the outcome variables [student ELA and Math performance], and, therefore, provide the best \u201csignals\u201d for identifying groups of schools with common underlying root causes?<\/li>\n
  2. In schools serving grades 9-12:<\/strong> Which predictor variables [percent of students with economic disadvantage status, percent of students with 95% attendance, and teacher years of experience], best predict the outcome variables [student ELA and Math performance], and, therefore, provide the best \u201csignals\u201d for identifying groups of schools with common underlying root causes?<\/li>\n<\/ol>\n

    Key findings:<\/strong><\/p>\n

      \n
    • A school\u2019s enrollment demographics account for over 50% of the variation in school-level ELA and Math achievement.<\/li>\n
    • Climate and staffing are significant indicators for predicting achievement in ELA and Math in both elementary and high schools.<\/li>\n<\/ul>\n[\/vc_column_text][vc_row_inner][vc_column_inner][vc_btn title=”CLICK HERE TO DOWNLOAD THE RESEARCH BRIEF” style=”gradient-custom” gradient_custom_color_1=”#0b315b” gradient_custom_color_2=”#398635″ align=”center” button_block=”true” link=”url:https%3A%2F%2Fwww.philasd.org%2Fresearch%2Fwp-content%2Fuploads%2Fsites%2F90%2F2022%2F08%2FDemographics-Climate-Staffing-Relationship-to-Academic-Performance-August2022.pdf”][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]\n<\/div>","protected":false},"excerpt":{"rendered":"

      When selecting which data to use to monitor progress towards the Board’s Goals & Guardrails, the School District of Philadelphia used prior research that identified relationships between certain data points and school-level outcomes. The selected data points reflect a broad range of specific, measurable characteristics of schools, but they can also be seen as three distinct \u201csets\u201d of data points reflecting enrollment demographics, school staffing, and school climate. This brief seeks to identify which sets of predictor variables best predict the academic outcome variables and, therefore, provide the best \u201csignals\u201d for identifying groups of schools with common underlying root causes.<\/p>\n","protected":false},"author":103956,"featured_media":12757,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[130,268,138],"tags":[325,329,321],"class_list":["post-12741","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-literacy","category-math","category-school-climate","tag-6-8","tag-9-12","tag-k-5"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/posts\/12741","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/users\/103956"}],"replies":[{"embeddable":true,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/comments?post=12741"}],"version-history":[{"count":8,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/posts\/12741\/revisions"}],"predecessor-version":[{"id":12893,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/posts\/12741\/revisions\/12893"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/media\/12757"}],"wp:attachment":[{"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/media?parent=12741"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/categories?post=12741"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.philasd.org\/research\/wp-json\/wp\/v2\/tags?post=12741"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}