A recent study sheds light on gender bias within academia's tenure-track positions. The study, "Exploring Gender Bias in Six Key Domains of Academic Science: An Adversarial Collaboration," was published in the July 2023 Psychological Science in the Public Interest issue.
The study was conducted by Stephen J. Ceci from Cornell University, Shulamit Kahn from Boston University, and Wendy M. Williams from Cornell University.
The study analyzed data from hundreds of studies conducted between 2000-2022, and the researchers found that women and men have equal chances of receiving grant funding.
The meta-analysis of 39 studies, including data from more than 2 million applications to 27 grant agencies, found no gender bias in the acceptance of journal articles for publication.
The authors' meta-analysis of other studies, which analyzed letters written from 1990 to 2017 for psychology, physics, biology, medicine, chemistry, and geoscience, found that women are more likely than men to be hired for tenure-track positions, according to their analysis of data from the National Science Foundation and existing studies.
However, the study found evidence of gender bias in teaching evaluations. Female instructors are often unfairly judged compared to their male counterparts, as they tend to receive lower teaching evaluations from students despite being equally effective educators.
Salary analysis revealed a less than 4% unexplained gender salary gap for similar scientists, indicating that there is still room for improvement in achieving full salary equality.
The study provides valuable insights into gender bias in academia, particularly STEM.
However, it is essential to note that the study's scope is limited to tenure-track positions within academia and may only partially represent the broader landscape of STEM professions, including non-tenure-track roles, industry positions, and government jobs.
Additionally, the analysis mainly addresses biases from 2000 to 2020, excluding historical data that could provide a deeper context for understanding the evolution of gender bias over time.
