The growth of mathematics and reading skills in segregated and diverse schools: An opportunity-propensity analysis of a national database

• Published in: Contemporary educational psychology Vol. 46; pp. 34 - 51
• Main Authors: Byrnes, James P., Miller-Cotto, Dana
• Format: Journal Article
• Language: English
• Published: San Diego Elsevier Inc 01.07.2016; Elsevier BV
• Subjects: Academic achievement; Academic Aspiration; Achievement; Age; Aspiration; Composition; Coverage; Ethnicity; Grade 3; Grade 8; Growth curves; Hierarchical Linear Modeling; Math skills; Mathematics; Mathematics education; Motivation; Multiculturalism & pluralism; Opportunities; Prior knowledge; Prior Learning; Racial diversity; Reading instruction; Reading Skills; Socioeconomic status; Teacher Characteristics; Teaching; Teaching Styles; United States > US; Growth curves; Reading skills; Racial diversity; Math skills
• ISSN: 0361-476X; 1090-2384
• DOI: 10.1016/j.cedpsych.2016.04.002

•Schools in the US remain as racially segregated as they were in 1954 when the Brown vs Board of Education Decision was made.•Schools that vary in their diversity differed significantly in terms of a variety of predictors.•These predictors collectively explained ~50% of the variance in four HLM analyses.•However, school diversity still predicted variance in growth curves even after controlling for these variables.•Differences also emerged across models for reading and math, and across models for 3rd graders and 8th graders. The goal of the present study was to continue to build and refine the Opportunity-Propensity (O-P) model of achievement by using it to explain well-known achievement disparities between schools that differ in terms of their racial and ethnic composition. The O-P model categorizes predictors into antecedent factors (e.g., family SES, parent educational aspirations), opportunity factors (e.g., content coverage and teaching style), and propensity factors (e.g., prior knowledge and motivation). To refine the model further, the authors did the following: (a) added predictors in each category that have not been examined to date; (b) used hierarchical linear modeling to explain growth in knowledge between two assessment points; (c) identified similarities and differences in the models for two content areas (i.e., math and reading); and (d) identified similarities and differences in the models for two age levels (i.e., 3rd grade and 8th grade). Results showed that the combination of new and established predictors accounted for approximately 50% of the variance in the rate at which knowledge grew in both math and reading at both age levels. In addition, the variance explained by the racial and ethnic composition of schools when this variable was entered as the sole predictor of achievement was substantially reduced after antecedent, opportunity, and propensity factors were entered in subsequent models. However, the coefficients for certain school compositions remained significant even after such controls, and the models differed somewhat between content areas (math and reading), and age levels (3rd and 8th grade). Findings are discussed in terms of the implications of these results for building and refining the O-P model further.