| dc.description.abstract | The primary objective of this dissertation is to investigate and understand the impact neighborhood contexts may have on low-performing first-grade students’ mathematics achievement. The data for this study came from a Goal 2 Institute of Education Sciences funded randomized control trial to evaluate Math Recovery, which is a one-on-one tutoring intervention for first graders. This study focuses on first graders in 20 schools, located in five districts across two states. For each first grader in this dissertation’s dataset, there are multiple mathematics achievement data measured via three subtests of the Woodcock-Johnson III (WJ III) achievement test, student-level data (e.g., race-ethnicity, gender, families’ SES and postal zip codes). The dataset also includes data from the United States Census Bureau to measure neighborhood contextual factors.
A series of three-level hierarchical linear growth models were used to estimate each student’s growth trajectory. The growth modeling technique provides a useful framework for examining students’ change patterns. Level-1 models individual student WJ III subtest scores over time. Level-2 of the model estimates the extent to which mathematics baseline and growth varies as a function of student time-invariant characteristics and level-3 accounts for the fact that students are clustered within schools.
This study shows that across different geographical boundaries with varying observable demographic characteristics, neighborhoods play a significant and, potentially, consequential impact on the development of first graders’ mathematical achievement trajectories. More specifically, for two of the three WJ III subtests, neighborhood contextual factors impact first grader’s growth rates over time, even after controlling for an array of student demographic characteristics. | |
