Seton Hall University
Predicting New Jersey High School Proficiency Test Results in Mathematics and Language Arts Using Community Demographic Data
Abstract
dc:description.abstract<p>This study examined the relationship between the 2013 New Jersey High School Proficiency Assessment (HSPA) Language Arts and Mathematics scores and school level data related to family human capital and community social capital found in the extant literature to influence student achievement on high-stakes standardized assessments. School level data included percentage of families in poverty for 12 months, percentage of lone-parent households (total), and percentage of population with some college. Final analysis utilized hierarchical linear regression and the application of a mathematical algorithm found in extant literature. The study incorporated New Jersey high schools that (a) tested more than 25 students in the 11<sup>th</sup> grade, (b) had valid 2013 NJ HSPA results in the Language Arts and Mathematics sections, and (c) complete census data existed for the communities they served. The final sample size included 168 towns in the state of New Jersey. The final Language Arts model was able to accurately predict 125 of the 168 (74%) passing rates within 4 percentage points, factoring for the model’s standard error of the estimate. The final Mathematics model was able to accurately predict 131 of the 168 (78%) passing rates within 8 percentage points, factoring for the model’s standard error of the estimate.</p>
Degree
thesis:*- Name thesis:degree_name
- PhD Higher Education Leadership, Management, Policy
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Education Leadership, Management and Policy
- Year dc:date.available
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Lynch, Christopher D.
- Contributors dc:contributor
-
- Christopher H. Tienken, Ed.D.
- Gerard Babo, Ed.D.
- Eunyoung Kim, Ph.D.
Subjects
dc:subject × 9Identifiers
dc:identifier.*- Repository record dc:identifier
- https://scholarship.shu.edu/dissertations/2121
- OAI identifier oai:identifier
- oai:scholarship.shu.edu:dissertations-3172