Date of Award

Spring 3-13-2020

Degree Type

Dissertation

Degree Name

EdD Education Leadership, Management and Policy

Department

Education Leadership, Management and Policy

Advisor

Nicole DiCrecchio, EdD

Committee Member

Jan A. Furman, EdD

Committee Member

Dale Caldwell, EdD

Committee Member

Rita Thornton, JD, PhD

Keywords

socioeconomic factors, middle school, academic achievement, ELA, PARCC

Abstract

This quantitative study is nonexperimental, utilizing socioeconomic, community, and district data to determine what influence and level of accuracy of prediction is made on standardized testing in New Jersey. This study focuses on middle school students in Grades 6, 7, and 8 in New Jersey's traditional public schools. The study design is a quantitative correlational predictive study design. SES factors for each district were identified using both U.S. Census data and NJDOE district data. Independent variables include percentage of poverty, percentage of single-parent families, education level of parents, percentage of free and reduced price lunch. The dependent variables include: 2018 ELA Grade 6 score, 2018 ELA Grade 7 score, and 2018 ELA Grade 8 score for proficiency (Level 4 + Level 5) results. Hierarchical multiple regression analysis was used for all grade levels, testing for correlation and productiveness between all predictor variables and the student achievement scores as measured by the 2018 PARCC results for middle school students in New Jersey. The two predictor variables, bachelor’s degree or higher and free and reduced price lunch (FRPL), were both statistically significant with high correlational values in the study. Nonetheless, using all variables outlined in this study provided the greatest predictability for each grade level. This study also confirmed predictability on average 60% of the results of standardized testing utilizing readily accessible and relevant data for our middle school students. The predictability measure in this study is important, as it will provide meaningful data and information to inform decision-makers about the influence of the socioeconomic, community, and district variables.

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