2018 - An Advisor Like Me: Does Gender Matter?

Attribution: Kato, Takao & Song, Yang
Researchers: Takao KatoYang Song
University Affiliation: Colgate University
Email: tkato@colgate.edu
Research Question:
- The effects of gender congruence in the student-adviser relationship on three key student outcomes: (i) retention; (ii) grades; and (iii) post-graduation career outcomes.
Published: Yes
Journal Name or Institutional Affiliation: IZA Institute of Labor Economics
Journal Entry: 11575
Year: 2018
Findings:

– Gender congruence in the student-adviser relationship is particularly helpful for academically weak students and students without STEM-orientation.
– Gender congruence has no significant impact on students with STEM-orientation regardless of whether their high-school GPAs are below or above the median.
– For students without STEM orientation, gender congruence helps students with below-median high school GPA improve their student outcomes both on the extensive and intensive margins, while helping students with above-median high school GPA improve their outcomes only on the extensive margin.
-The authors find that gender congruence in the student-adviser relationship has a positive and significant effect on the odds of retention and on cumulate GPA upon graduation.
– The authors uncover that much of the gender congruence effect
on the extensive margin tends to be concentrated in the freshman and sophomore years, while the gender congruence effect on the intensive margin is less immediate and shows up only in cumulative GPA upon graduation.
– Student-adviser gender congruence is found to work differently for students with different backgrounds and interests.

– Gender congruence has no significant impact on students with STEM-orientation regardless of whether their high-school GPAs are below or above the median.

Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: Career OutcomesFemaleGenderGPAHigher EducationLabor MarketRetentionSTEMTeacherRegions: UnknownMethodologies: QuantitativeResearch Designs: Administrative DataAnalysis Methods: Fixed Effect Models Sampling Frame:14,678 students and 345 academic advisors at a selective liberal arts university
Sampling Types: Non-Random - PurposiveAnalysis Units: StudentTeacherData Types: Quantitative-Longitudinal
Data Description:

– The authors use unique administrative data from a selective liberal arts university (LiberalArtsU) with around 750 students in each cohort
– The data contain information on student demographics before entering college, course outcomes, and advisers each term for every student enrolled at LiberalArtsU from the fall semester of 1996 to the spring semester of 2015.
– The authors supplemented the administrative data with biographical data on every teaching faculty member at LiberalArtsU
who taught at least one course from the fall semester of 1996 to the spring semester of 2015, which they collected from his/her online homepage and other websites.

* The authors’ identification strategy assumes that the assignment of academic advisers to first-year students at LiberalArtsU is random in gender, conditional on first-year students’ selection of
FSEM courses.

Ivs: Gender of student, gender of randomly-assigned academic advisor

Dvs: Retention, Cumulative GPA, post-graduation career outcomes ( employed, enrolled in a graduate school, or neither, plus some employer, industry, and salary information).

Theoretical Framework:
Relevance:Gender and STEM
Archives: K-16 STEM Abstracts