Lent, Robert W., Lopez, Frederick G., Sheu, Hung-Bin, & Lopez Jr., Antonio M.
University Affiliation: University of Maryland; University of Houston; Arizona State University
Email: boblent@umd.edu
Research Question:
To extend prior research on SCCT in the context of STEM fields by examining the theory's potential for understanding the interests and choices of students in the computing disciplines, including two groups of students that remain substantially underrepresented in computing (i.e., women and African Americans).
Published: Yes
Journal Name or Institutional Affiliation: Journal of Vocational Behavior
Journal Entry: Vol. 78, No. 2, Pp. 184-192
Year: 2011
Findings:
- The SCCT model offered good overall fit to the data in the larger sample.- The social cognitive model generally provided adequate fit to the data across two academic year cohorts, gender, institutional setting, racial/ethnic groups (European and African Americans), and educational level (beginning and advanced undergraduates).
- Interests were well predicted by self-efficacy, and intentions to persist in computing were directly linked to self-efficacy, interests, and supports and barriers. In addition to their direct relations topersistence intentions, supports and barriers produced indirect paths to intentions via self-efficacy.
- Racial/ethnic group analyses yielded roughly comparable findings across the two groups based on the more conservative of the two criteria for assessing difference in model fit. Using the more liberal criterion, the authors did find a few differences in factor loadings and parameter estimates. In particular, they observed that the path from self-efficacy to outcome expectations was somewhat larger in the European American than in the African American sample, which could reflect the possibility that the outcome expectations of African American students are informed to a greater degree by considerations other than self-efficacy.
- Outcome expectations, however, did not contribute uniquely to the predictive model.
- The reasons for these inconsistencies in the predictive utility of outcome expectations are unclear. One possibility is that their outcome expectations measure did not adequately capture the most compelling types of expected outcomes that would be most likely to spark interest and persistence in computing.