Tracing and Linking Contextual and Psychological Factors to STEM Career Choice

Michigan
Causal
Science
Engineering
Math
Cognitive Science
Research Methods
Other
Pre-K
Elementary
Middle
Secondary
Higher Education
School Personnel
Informal Settings
Principal Investigator: 
Jacquelynne Eccles
Co-Investigator: 
Barbara Schneider
Project Overview
Background & Purpose: 

The goals of this project are to:

  1. investigate the role that student in- and out-of-school experiences play in the formation of STEM and non-STEM related interests, task valuing, and self-efficacy;
  2. relate motivational beliefs to STEM related educational and occupational aspirations; to examine the differences in the educational and career paths of those choosing STEM from those who do not; and
  3. look within STEM and chart the pathway between those who enter engineering and physical sciences from those who enter the biological and social sciences.

Results will increase our knowledge of (a) whether experiences with STEM related activities in- and out-of-school during the elementary and secondary school years influence STEM-related occupational/educational planning and choices during adolescence, and (b) whether the associations between these experiences and educational/occupational outcomes vary across gender, race/ethnicity, socioeconomic status, and across different personality profiles. 

Setting: 

University of Michigan, Ann Arbor, Michigan

Research Design: 

The project uses a longitudinal and comparative research design and will generate evidence that is causal [quasi-experimental and statistical modeling]. We are doing secondary data analyses on longitudinal studies of adolescents in the USA using two data sets: 1) the Childhood and Beyond Study (CAB) and 2) the Alfred P. Sloan Study of Youth and Social Development (SSYSD). Both longitudinal datasets include extensive measures of the experiences children and adolescents have with STEM-related and non-STEM activities and courses, as well as extensive measures of self-efficacy, affective experiences, interests, educational and career aspirations, and educational course choices. The analysis and modeling of the data will make use of such techniques as structural equation modeling (SEM), hierarchical linear modeling (HLM), and person-centered techniques, such as cluster analyses, latent class analyses, and life trees.

Findings: 

Findings will be posted as they become available.

Other Products: 

This project expects to produce new ways of coding Beeper data, journal articles, and policy reports on the implications of our findings for teacher training.

On the Web: 
Gender & Achievement Research Program (GARP)