Theses and Dissertations

Date of Award

2026

Document Type

Dissertation

Degree Name

Doctor of Education (EdD)

Department

Abraham S. Fischler College of Education and School of Criminal Justice

Advisor

Gloria Kieley

Committee Member

Jaime Arango

Committee Member

Kimberly Durham

Keywords

archival data, artificial intelligence, blind résumé review, candidate evaluation, chi-square analysis, correlational research, employment discrimination, evaluator bias, evaluator gender, gender bias, gender equity, gender gap, gender match, hiring decisions, hiring practices, human resources, information technology, interview recommendations, labor shortage, labor supply, leadership positions, LinkedIn recruitment, nonexperimental research, organizational behavior, prejudice, quantitative research, recruitment, résumé screening, role congruity theory, selection bias, similarity bias, standardized scoring rubric, systems applications products analyst, systems applications products project manager, technical leadership, technology workforce, women in technology, workforce diversity, workforce equity, workforce participation, candidate gender, employment equity, hiring professionals, information technology workforce, interview selection, leadership roles, male candidates, female candidates, occupational gender disparities, personnel selection, recruitment processes, screening-stage bias, statistical significance, structural interventions, talent acquisition, technical positions, technology sector, unconscious bias, workplace diversity, workplace inclusion, gender representation, leadership opportunities, labor market research, hiring outcomes, evaluator decision-making, employment practices, diversity initiatives, gender discrimination, organizational hiring, personnel management, recruitment bias, selection processes, technology careers, workforce development, equal employment opportunity, résumé evaluation, applicant screening, diversity and inclusion, hiring equity, professional opportunities, candidate selection, role-specific discrimination, leadership recruitment, workforce shortages, technology employment, human capital, employment research, women in leadership, IT industry, talent management, organizational equity, interview screening, diversity recruitment, hiring disparities, evaluator perceptions, employment outcomes, workforce inclusion, gender-based disparities, recruitment strategies, organizational decision-making, labor market dynamics, information technology careers, hiring recommendations, candidate assessment, personnel recruitment, diversity management, workplace equality, employee selection, occupational equity, technology leadership roles

Abstract

This applied dissertation examined whether gender preferences influence hiring decisions in the information technology (IT) sector, and whether such preferences contribute to the ongoing labor shortage in the field. Despite national and international initiatives to close the gender gap, women continue to represent only 28% of the United States IT workforce and remain significantly underrepresented in technical and leadership roles. Grounded in the Eagly and Karau (2002) Role Congruity Theory of Prejudice, this study investigated whether evaluator gender and candidate gender alignment affect interview selection outcomes at the résumé screening stage for both leadership and non-leadership IT positions.

A quantitative, nonexperimental, correlational design was chosen using archival data collected in 2023 from 146 IT hiring professionals recruited via LinkedIn. Participants evaluated four résumés: two female and two male candidates for a Systems-Applications-Products Project Manager (leadership) and a Systems-Applications-Products Analyst (non-leadership) role. Chi-square tests of independence were used to assess associations between evaluator gender, candidate gender, gender match, and interview recommendation decisions across 584 valid observations.

Results revealed a statistically significant association between gender match and interview recommendations for general interview selection, χ²(1, N = 584) = 8.78, p = .003, indicating evaluators were inclined to recommend candidates of their own gender. This gender-match effect was observed across both male and female evaluators. However, no statistically significant difference was found in interview recommendations based on gender for leadership versus non-leadership roles, χ²(1, N = 584) = 1.407, p = .236. These findings suggest that gender bias in IT hiring operates most prominently as interpersonal similarity bias at the initial screening stage rather than as role-specific discrimination. Structural interventions such as blind résumé review or a standardized scoring rubric are recommended to mitigate gender-based hiring disparities.

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