White Students in Urban Schools: The Unheard Voice in the Achievement Gap
This study analyzes the academic performance of White students in urban settings, relative to Minority peers, and investigates structural variables that might exacerbate or attenuate differences. The investigation is motivated by and extends qualitative work investigating the experiences of middle school students in Texas (Morris, 2006). Research on the academic achievement of impoverished urban White students is limited. The present study investigates achievement gaps that are probably a function of student demographic characteristics, and achievement equity (i.e., the influence of school-level demographic and structural characteristics on student achievement and on distributions of student achievement among schools). The use of a multilevel regression model is appropriate for this type of study. The OAT data used in this study are hierarchical in nature because students are nested within schools. The use of a multi-level model (MLM) addresses the challenge of students nested within schools. The predictor variables of Ethnicity and Poverty exhibit a strong relationship with achievement linked to both of the dependent variables of reading and math. The analysis showed a significant relationship as evidenced by student Poverty was -9.42 for reading and -7.63 for math representing the average decrease in score for impoverished students from the average mean score. Ethnicity predictor variable showed the mean score for Minority students in math was 15.54 points lower than for White students and the reading for Minority students was 9.84 points lower than for White students. The gap for impoverished White is larger than for economically disadvantaged Minority (7.34 points more than for economically disadvantaged Minority students- 22.41 points to 15.07 points) in the dependent variable of reading. This finding is consistent for the dependent variable of math also: the gap for economically disadvantaged White is larger than for impoverished Minority (6.59 points larger than for impoverished Minority students). As was hypothesized; the challenges appear to be parallel with the level of influence that is exerted on economically disadvantaged urban White students as has historically been given attention from educators, researchers and policymakers to impoverished urban Minority students. Based upon the study findings the researcher suggested several implications for practice and opportunities for future research. Early intervention for all children regardless of Ethnicity or SES status; teacher efficacy; the best teachers need to be with the most struggling students and student relationships need cultivated if students of poverty are to have an opportunity of academic success. Recommendations for further research studies are: conducting a qualitative study to be generalized over a larger group of students; extending this study to other urban districts in the state, possibly comparing with other types of school systems; developing a more comprehensive fully specified study; and extending Morris’ (2006) study researching more than one school through the use of ethnography would lead to an interesting comparison that can assist teachers and administrators in raising achievement of students of all ethnicities and socioeconomic backgrounds.
Education, Educational Leadership, Educational Theory, Education Policy, Achievement Gaps, White Poverty, Urban Schools, Hierarchical Linear Modeling (HLM)
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