Dr. Uma Chidambaram's Doctoral Research Finds Same-Gender Patterns in IT Résumé Screening

IT hiring professionals in a study tended to recommend candidates of their own gender for an interview, according to doctoral research by Dr. Uma Chidambaram, Executive Vice President of Houston-based ObjectWin Technology, Inc. The pattern was observed among both male and female evaluators. The study was conducted for her Doctor of Education degree at Nova Southeastern University and the dissertation was published in the university's repository in June 2026.Dr. Uma Chidambaram

The dissertation, "From Bias to Bottleneck: A Quantitative Analysis of Gender Preferences in Tech Hiring," analyzed 584 résumé-level evaluations completed by 146 IT hiring professionals. It found a statistically significant association between evaluator-candidate gender match and interview recommendations. The study's findings point to résumé screening as an important early decision point in the hiring process. The author recommends considering blind résumé review and standardized evaluation criteria as ways to help make screening more consistent.

From Bias to Bottleneck: A Quantitative Study of Gender Preferences in Technical Hiring "Discussions of hiring bias often focus on who receives a job offer," said Dr. Chidambaram. "This study examines an earlier point in the process: the decision about whether a résumé should advance to an interview. If recommendations are influenced by gender similarity, qualified candidates could be overlooked before they have an opportunity to demonstrate their skills." She added, "Blind résumé review and a consistent scoring rubric are practical approaches organizations can consider to help make evaluations more structured and equitable."

Study Examined 584 Résumé Evaluations by 146 IT Hiring Professionals

The research was conducted at Nova Southeastern University's Abraham S. Fischler College of Education and School of Criminal Justice. Gloria Kieley served as dissertation advisor, with Jaime Arango and Kimberly Durham as committee members.

The study used résumé-evaluation data collected in 2023 from 146 IT hiring professionals recruited through LinkedIn using convenience and snowball sampling. Each participant evaluated four résumés: two representing female candidates and two representing male candidates.

The résumés were created for two SAP roles: Project Manager, categorized in the study as a leadership role, and Analyst, categorized as a non-leadership role.

The design produced 584 résumé-level observations. Dr. Chidambaram used a chi-square test of independence to examine the association between evaluator gender, candidate gender, and interview recommendations. The study was quantitative, non-experimental, and correlational; it examined résumé recommendations rather than actual interview or hiring outcomes.

Evaluators Tended to Recommend Same-Gender Candidates

Across 584 résumé evaluations, the study examined whether evaluator-candidate gender match was associated with a recommendation for an interview. The results showed a statistically significant association.

Central finding

The analysis found a statistically significant association between evaluator-candidate gender match and interview recommendation: evaluators tended to recommend candidates of their own gender more often (χ2(1, N = 584) = 8.78, p = .003).

A pattern observed among both genders

The pattern was reciprocal: male evaluators tended to recommend male candidates, while female evaluators tended to recommend female candidates. The dissertation discusses this pattern as interpersonal similarity bias. The results do not establish an overall preference for one gender over another.

What the finding covers
The finding concerns résumé-screening decisions in a field where women are underrepresented. The dissertation discusses statistics indicating that women make up a minority of the U.S. IT workforce. The study examined evaluator-level screening recommendations; it did not assess how those decisions affect the gender composition of the overall IT workforce.

The analysis focused on the résumé-screening stage and whether a candidate was recommended to advance to an interview. It did not track whether candidates were actually interviewed, received offers, or were hired.

No Statistically Significant Difference Found Between the Two Role Categories

The study also examined whether interview recommendations differed between the leadership and non-leadership roles studied. It found no statistically significant difference between the two role categories (χ2 = 1.407, p = .236).

The research was informed by Role Congruity Theory, developed by Eagly and Karau, which addresses how gender stereotypes may shape evaluations of people in roles associated with particular expectations, including leadership. In this study, however, the analysis did not find a statistically significant difference in recommendations between the two role categories examined. This result does not establish that role-related bias is absent from IT hiring more broadly.

Taken together, the findings indicate a statistically significant association between evaluator-candidate gender match and interview recommendations, while the study found no statistically significant difference between the leadership and non-leadership roles examined. The results suggest that interpersonal similarity may be relevant to the screening decisions studied; they do not show that similarity is the only factor affecting hiring decisions.

Dissertation Recommends Blind Résumé Review and Standardized Scoring

The dissertation recommends two approaches that organizations may consider to support more consistent screening:

Blind résumé review: Remove names and other identifying details that may signal a candidate's gender before an evaluator reviews the résumé, where feasible.
Standardized scoring rubric: Apply the same job-related criteria to each candidate for a given role.
These are recommendations for improving screening practices; the study did not test whether either intervention reduces bias. The press release's cited prior research by Sackett and colleagues (published in the Journal of Applied Psychology in 2022) should be checked against the original source before publication, particularly the wording about structured evaluations and job-performance prediction. Journal of Applied Psychology

Study Limitations

The findings reflect résumé evaluations from a sample recruited through LinkedIn using convenience and snowball sampling. The study was correlational and examined two SAP role descriptions; its results should not be generalized automatically to all IT hiring or interpreted as proof of causation. It did not examine the subsequent interview or hiring process.

Full Dissertation Available Online

The dissertation was published in Nova Southeastern University's NSUWorks repository on June 15, 2026, and is available at https://nsuworks.nova.edu/fse_etd/1164/. This press release shares the findings with a broader audience.https://nsuworks.nova.edu/fse_etd/1164/

The dissertation provides the study's methodology, statistical analyses, conclusions, and recommendations. In brief:

It examined résumé evaluations for two SAP roles: Project Manager and Analyst.
It did not analyze actual interviews, job offers, or hiring outcomes.
Because the study was correlational, it identifies an association and does not establish causation.
Dr. Chidambaram has also written about structured and unstructured recruitment approaches in "Structured Scorecards vs. Gut-Feel Interviews."

About Dr. Uma Chidambaram

Dr. Uma Chidambaram, Ed.D., C.C.W.P., is Executive Vice President at ObjectWin Technology, Inc. She joined ObjectWin in 2003 and was appointed to her current position in 2009. She earned her Doctor of Education degree from Nova Southeastern University in 2026. Staffing Industry Analysts named Dr. Chidambaram to its Global Power 150 - Women in Staffing list in 2024 and 2025 and its Staffing 100 North America list in 2025. More information: https://umachidambaram.com/https://umachidambaram.com/

About ObjectWin Technology, Inc.

Founded in 1997 and headquartered in Houston, ObjectWin Technology, Inc. provides IT staffing, contingent workforce solutions, and IT project services to enterprise and government customers in the U.S., Canada, and India. The company is registered as a Minority Business Enterprise by the Houston Minority Supplier Development Council (HMSDC) and was recognized in the Deloitte Technology Fast 500 North

FAQs

1. What did Dr. Uma Chidambaram's research study?

The study examined whether evaluator-candidate gender match was associated with recommendations to advance résumés to interviews. Data were collected in 2023 from 146 IT hiring professionals recruited through LinkedIn using convenience and snowball sampling. Each participant evaluated four résumés for two SAP roles: Project Manager and Analyst.

2. What was the main finding?

The study found a statistically significant association between evaluator-candidate gender match and interview recommendations (χ2(1, N = 584) = 8.78, p = .003). In the résumé evaluations analyzed, evaluators tended to recommend candidates of their own gender, and this pattern was observed among both male and female evaluators. The result is consistent with interpersonal similarity bias, but it does not demonstrate an overall preference for one gender or establish causation. The study examined résumé recommendations, not the full hiring process.

3. Did the results differ between leadership and non-leadership roles?

The analysis found no statistically significant difference between recommendations for the leadership and non-leadership roles examined (χ2 = 1.407, p = .236). This means the study did not find sufficient statistical evidence of a difference between these two role categories. It does not prove that role-related bias is absent from IT hiring more broadly.

4. What does the research recommend, and where can the full study be read?

The dissertation recommends blind résumé review, where feasible, and standardized scoring criteria applied consistently to candidates for the same role. These are recommendations, not interventions tested by this study. The full dissertation, "From Bias to Bottleneck: A Quantitative Analysis of Gender Preferences in Tech Hiring," is available through NSUWorks at https://nsuworks.nova.edu/fse_etd/1164/.https://nsuworks.nova.edu/fse_etd/1164/

Media Contact
Dr. Uma Chidambaram

Houston, Texas

Email: contact@umachidambaram.com

Phone: +1 713-822-7448

Website: https://umachidambaram.com/

America for 2006-2009. More information: https://www.objectwin.com/https://www.objectwin.com/

This release was published on openPR.