– There is a pervasive gender gap in MSC in nearly all fields, but also a great deal of variation in MSC among the STEM fields.
– The salience of MSC in predicting STEM major selection has generally become weaker over time for women (but not for men).
– For women, the salience of math self-concept has grown only in the prediction of selecting majors in math/statistics.
– For men, the predictive power of math confidence has become weaker in predicting major choice in only one field: computer science
– While students’ beliefs about their math ability are higher among those majoring in STEM fields relative to all majors, there is a great deal of variation in demonstrated math self-concept among the STEM fields, from a high in math/statistics to a low in the biological sciences.
– Though math self-concept is nearly always a significant positive predictor of students’ decisions to major in STEM, its salience in predicting major choice has fluctuated over time.
– The results suggest that women’s lower math confidence has become a less powerful explanation for their underrepresentation in STEM fields.
– A ‘‘one-size-fits-all” approach to STEM recruitment is not supported by the results of this study. Efforts to diversify STEM- both in research and in practice- need to be approached at the field level in order to best understand what attracts women and men to a particular subfield of STEM.
2015 - ‘‘But I’m Not Good at Math”: The Changing Salience of Mathematical Self-Concept in Shaping Women’s and Men’s STEM Aspirations
The authors utilize social cognitive career theory (SCCT). SCCT was deemed appropriate because: 1) SCCT organizes both person and environment variables into a model of career choice that has been empirically tested and supported; and 2) SCCT unifies conceptually related constructs such as self-concept and self-efficacy to provide a more universal framework for studying career choice and behavior.
CIRP Freshman Survey (TFS). TFS data from baccalaureate-granting institutions between 1971 and 2011, which informed analysis of the shifts over time in mathematical self-concept for STEM men and women. Among the 1305 institutions represented, the sample was comprised of 49 % private religious colleges and universities, 30 % private non-sectarian institutions, and 21 % public colleges and universities.
The analysis of forty-year trends data was supplemented by a more detailed focus on data from 5 specific years: 1976, 1986, 1996, 2006, and 2011 (regression sample). This regression sample provided insight into the specific predictive power of math self-concept in men’s and women’s STEM major selection, and was composed of approximately 353,000 students across five STEM disciplines, and 1149,000 students from non-STEM disciplines.
The five dependent variables were single indicator measures reflecting students’ intent to major in one of the following STEM fields of study, versus all other fields: biological sciences, computer science, engineering, mathematics/statistics, or physical sciences.
The key independent variable of interest was students’ self-rating of mathematical ability, which is indicated on a five-point scale comparing the student to ‘‘the average person your age’’: lowest 10 %, below average, average, above average, and highest 10 %.
The other independent variables were:
– Personal inputs: race/ethnicity, religion, political view.
– Background characteristics: family income, mother’s and father’s education, mother’s and father’s careers.
– Learning experiences: high school GPA.
– Outcome expectations: future expectations to change major field and make at least a ‘B’ average.
– Interests: self-rated math ability; goals of making a theoretical contribution to science, raising a family, and developing a meaningful philosophy of life; expectations of changing major field, and making at least a B average; leader personality (factor), scholar personality (factor), social activist personality (factor), artistic personality (factor), and status striver personality (factor); educational and extrinsic reasons forgoing to college (factors).
– Contexts proximal to choice behavior: distance of college to home, number of colleges applied to, and concerns about financing college.
– Choice goals: degree aspirations.
– Institutional variables- Undergraduate Enrollment; Student-Faculty Ratio; Institution Type as defined by University or College, Religious or Non-Sectarian, and HBCU; and Institutional Region.