2013 - College Student Pathways to the STEM Disciplines

Attribution: Engberg, Mark E., & Wolniak, Gregory C.
Researchers: Gregory C. WolniakMark E. Engberg
University Affiliation: Loyola University Chicago; University of Chicago
Email: mengber@luc.edu
Research Question:
1) What individual and school-level factors influence students pathways to STEM fields during college? 2) What institutional factors affect students' likelihood of majoring in a STEM field in college, controlling for differences in student characteristics?
Published: Yes
Journal Name or Institutional Affiliation: Teacher College Record
Journal Entry: Vol. 115, Pp. 1-27
Year: 2013
Findings:
  1. Black and Asian students had a significantly higher proportion of students in STEM majors in comparison to White students.
  2. STEM majors were associated with a lower average SES in comparison to non-STEM majors.
  3. STEM majors had greater self-efficacy in relation to mathematics than non-STEM majors, and study more outside of the classroom.
  4. STEM majors placed a significantly higher level of importance on college affordability and financial aid in comparison with non-STEM majors.
  5. STEM majors were associated with the highest average scores in relation to their postsecondary preparation for math and science courses.
  6. Course-taking, in particular, proved to be an extremely important factor in raising STEM propensities, which highlights the need to provide students with appropriate guidance around course selection early on in their high school careers.
  7. Although no institutional effects were uncovered at the high school level, both postsecondary sector and selectivity significantly influenced propensities toward majoring in a STEM discipline.
  8. In addition, attending a postsecondary institution with inclusive selectively increased the likelihood of declaring a STEM major by 45% compared with those attending highly selective institutions.
Scholarship Types: Journal Article Reporting Empirical ResearchKeywords: Institutional FactorsPathways to STEMSTEMSTEM MajorRegions: NationalMethodologies: QuantitativeResearch Designs: Secondary Survey DataAnalysis Methods: Cross-Classified HGLMDescriptive Statistics Sampling Frame:High School to College Students
Sampling Types: Nationally RepresentativeAnalysis Units: CollegeSchoolStudentData Types: Quantitative-Longitudinal
Data Description:
  • Education Longitudinal Study of 2002. Based on data from 4,180 students attending 670 high schools and 1,050 postsecondary institutions. 2,840 students were in non-STEM majors and 1,340 were in STEM majors.
  • The dependent variable was a dichotomous measure that represented whether a student had declared a major in a STEM discipline.
  • The independent variables were demographics and socioeconomics; academic preparation, attitudes, and dispositions during high school; college choice considerations; and post-secondary experiences. The authors also included a number of institutional factors to assess the influence of different contextual and environmental effects at the secondary and post-secondary level.
  • To examine postsecondary experiences that potentially influence college major decisions, the authors included six variables that measured students’ postsecondary preparation in math and science and their engagement in academic and nonacademic pursuits. The post-secondary preparation measure was an average of two variables that asked students to rate the extent to which their high school prepared them for college-level courses in math and science. The other postsecondary variables asked students to rate the frequency with which they met faculty outside of class to discuss academics, met with an advisor about academic plans, worked on classwork at the library, used the web to access the library for coursework, and participated in extracurricular activities.
  • The high school-level variables included two sets of dummy variables to capture the sector (i.e., public, Catholic, and other private schools) and region (urban, suburban, and rural) of the high school. Additionally, we used two single-item variables that assessed the extent to which the high school helped students select majors/career pathways and the extent to which students were involved in college preparation programs.
  • The authors also included three scales derived through factor analysis to measure the overall learning environment. The first scale examined the extent to which learning was hindered based on the lack of computers, multimedia, and other technological equipment. The second scale examined the extent to which learning was hindered based on the poor condition of the building, science labs, library, and other learning spaces. The final measure was a continuous variable that assessed the percentage of math and science teachers in a particular school.
Theoretical Framework:
Relevance:STEM Entrance and Majoring in STEM
Archives: K-16 STEM Abstracts