Wednesday, December 12, 2018

URSP Student Enya Calibuso Examines Child- and School-Level Predictors of School Mobility in Middle School Students

Text Box: Enya Calibuso
UNIV 495
11 – 20 – 18 
Tacoma, Washington; Eglin Air Force Base, Florida; Latina, Italy; Eagle River, Alaska; Dumfries, Virginia; Johannesburg, South Africa; Orono, Maine; and Alexandria, Virginia – What do all of these cities and countries have in common? Well they’ve catalyzed my research interests. Growing up in a military family, having moved 8 times, lived on 3 continents, and attended 9 schools, I’ve witnessed firsthand the resilience needed in highly mobile students. Through the Psychology Honors Program and the Undergraduate Research Scholars Program, I’ve had the great opportunity to work with Dr. Adam Winsler to examine Child- and School-Level Predictors of School Mobility in Middle School Students.These research-intensive programs have equipped me with the skills (data analyses, conducting literature reviews, and preparing materials for grants and conferences), knowledge (specific to applied developmental psychology), and experience needed to further my education in hopes of attending a MD-PhD program specializing in psychiatry and French. 

On a weekly basis, my duties typically consist of revising my honors thesis. This semester, I’ve been working on editing my literature review to integrate feedback from my mentor, double its length, reorganize its structure to be in accordance with APA formatting, and add an additional theoretical framework. I’ve also begun to address the gaps in the literature, my proposed study, its methodology, and a data analysis plan to move forward. Throughout this process, I’ve discovered that predictors of increased school mobility include being African American, being in poverty, attending a low-quality school and/or center-based early care, and having a disability. Importantly, researchers attempting to claim that school mobility has adverse effects on students’ academic performance and school completion need to understand and statistically control for these pre-existing differences between movers and non-movers before analyzing outcomes.