2014 - The High School Environment and the Gender Gap in Science and Engineering

Attribution: Legewie, Joscha, & DiPrete, Thomas A.
Researchers: Joscha LegewieThomas A. DiPrete
University Affiliation: New York University; Columbia University
Email: joscha.legewie@nyu.edu
Research Question:
Extend existing explanations for gender differences in plans of pursuing STEM degrees and examine the role of the high school context.
Published: Yes
Journal Name or Institutional Affiliation: Sociology of Education
Journal Entry: Vol. 87, No. 4, Pp. 259-280
Year: 2014
Findings:

– There is a significant positive effect of the curriculum index on intentions to major in STEM fields for girls that plan to go to college but not for boys.
– Gender segregation of extracurricular activities has a substantial negative effect on intentions to major in STEM for girls but not for boys.
– High schools’ curricula in science and math and gender segregation of extracurricular activities have large effects on the gender gap in plans to study STEM fields. While these estimated effects are large, the authors find, not surprisingly, that these two factors explain only part of the total estimated variations in school effects.
– Going to a school that supports girls’ STEM orientations reduces the gender gap by 25 percent or more, and the school’s impact is durable. Despite this sizable reduction, a substantial gender gap remains, even for students who attend schools that are supportive of girls’ STEM orientations.

* High school context and it’s effects on female students

Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: ContextCurriculumExtracurricular ActivitiesGenderHigh SchoolHigh School CompositionSTEMRegions: NationalMethodologies: QuantitativeResearch Designs: Secondary Survey DataAnalysis Methods: Descriptive StatisitcsHierarchal Logistic Regression Sampling Frame:8th Grade to High School Students
Sampling Types: Nationally RepresentativeAnalysis Units: SchoolStudentData Types: Quantitative-Longitudinal
Data Description:

Their analyses are based on two special samples from National Education Longitudinal Study (NELS). The authors combined these two special samples (NELS 88-92 and NELS High School Effectiveness Study (HSES)) and they construct a third dataset that includes the subset of students in HSES high schools who were part of the base-year NELS interview in 1988. The overall sample size is 11,270 for NELS 88-92; 9,120 for the full HSES sample; and 2,350 for the combined HSES sample.

The dependent variable is plans to major in STEM fields at the end of high school. This was measured by asking ‘Do you plan to continue your education past high school at some time in the future?’’ to determine the people who do not plan to go to college. The authors then use the intended field of study question to distinguish between STEM and non-STEM fields.

IVs: High school treatment indicators- Math and science curriculum (index based on advanced placement-level course offering in math and science) and gender segregation (gender segregation of extracurricular activities measured in terms of the index of dissimilarity for membership in 18 sport and other clubs).

Pre-high school control variables- Gender, race, region and urbanicity, occupational aspirations in science in 8th grade (binary), 8th-grade reading, math, and science test scores, GPA, self-reported English, math, science, and social studies grades from 6th to 8th grade, math/science interest, Math/science extracurricular activities.

Middle school variables- School size, socioeconomic status composition, average STEM orientation, presence of gifted programs for math and science, student-teacher ratio, and school type.

Theoretical Framework:
Relevance:STEM Interest/Pursuit/Aspirations/Intent
Archives: K-16 STEM Abstracts