With a focus on implicit bias and school discipline, Gina Gullo investigates school discipline gaps and how to best lessen related discrepancies. In her doctoral dissertation, Gina revealed the connection between school administrator's implicit bias and discipline severity for students of Color where a negative associations for students of Color predicted the relationship between student race and discipline severity in PA K-12 schools for subjective discipline, but not overall or objective discipline. In a follow-up study she continues by examining the discipline process as experienced by school administrators in an effort to better understand how, when, and why implicit bias enters this decision-making process. Future research aspirations beyond replication include exploration of school discipline gaps as related to intersectionalities including those of race, gender, special education enrollment, and LGTBQ+ identification. Although this is a lofty research direction, Gina is just beginning her career as an educational researcher and is confident that time and contributions of fellow researchers will help her to achieve such goals.
In a second research pursuit, Gina looks at the superintendency aspirations and attainment of female educational leaders. With the well-established gender biases in all leadership positions, women must find ways to navigate potentially biased career paths. One such method is the use of insider or outsider career paths, or internal promotion or external job growth. While insider career paths seem to help override bias, Gina's collaborative research indicates that women do not have the same access to insider career paths offered to men. This inhibited agency results in an imbalance between the desirability queuing of the employer and the candidate based on the postulates of job queuing and occupational sex segregation theory. If gender-biased employers have more ability to choose than female candidates, the result would be a heavily imbalanced workplace similar to that which currently exists. As such further research will focus on contributors to this decreased agency and methods of increasing female job queuing agency.
Gina is experienced in a large array of quantitative methods both parametric and non-parametric. She is also skilled with data visualization methods.
Descriptive Statistics
Chi-squared
T-tests and Z-scores
Means comparisons (e.g. ANOVA, MANOVA)
Correlational analyses
Regression (uni- and multi-variate)
Hierarchial Linear (Multilevel) Modeling
Structural Equation Modeling (developing skills)
Gina has experience with qualitative methods with a particular focus on open-ended survey item- and interview-based data (although she is versed in other data types). Some of her preferred qualitative approaches include:
Grounded Theory
Ethnography
Narrative Inquiry
Phenomology
Case Study
Gina has used mixed and multiple methods research to create more meaningful research in most of her educational research endeavors. She has experiencing using both concurrent and sequential designs, and continues to extend here mixed methodological practices.