Implicit Cognition in STEM Education (I.C.STEM)

Principal Investigator: 
Project Overview
Background & Purpose: 

How do implicit attitudes and stereotypes develop and to what extent do they influence engagement, perseverance and achievement in STEM? Theoretical and methodological innovations over the past quarter century have converged to implicate implicit mental processes in social perception and behavior. I.C.STEM is aimed at understanding the developmental course and impact of implicit attitudes and stereotypes in the STEM domain, especially as they may differentially affect the persistence of talented women and underrepresented minorities, and advance national efforts to close achievement gaps and maximize productivity and security.


Middle school and high school girls are being studied in Chicago and Charlottesville, VA; University of Virginia undergraduates; as well as Internet volunteers worldwide.

Research Design: 

This research is designed to generate causal evidence using experimental methods. Admission to YWLCS is by random lottery. We are attempting to capitalize on this system by comparing the attitudes and achievement of those who enroll with those who do not. Teachers participating via the Internet are randomly assigned to experimental conditions varying the content of case-studies. This project collects original data using implicit association measures such as the Implicit Association Test (IAT).

We focus on the indirect, nonconscious, or implicit mode of operation for stereotypes, preferential attitudes, and self-concepts. Measures of implicit cognition are proliferating and each has methodological strengths and weaknesses. Together, applied in a multimethod-multitrait design (Nosek & Smyth, 2007), implicit measures provide a powerful set of tools to examine thought and feeling outside of conscious awareness or control. Some examples of these instruments include the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998), and the Go/No-go Association Task (GNAT; Nosek & Banaji, 2001).

Explicit measures capitalize on the existing literature of well-validated measures, and parallel implicit construct assessments. They include selected math and science attitude and stereotype items from measures such as: Modified Fennema-Sherman Attitude Scales (Doepken et al., 2006); math-specific modification of Dweck’s entity/incremental theories of intelligence scale (Dweck, 2000; Good, 2006); academic items from the National Educational Longitudinal Study (NELS) and National Assessment of Educational Progress (NAEP); math and reading efficacy and attitude items from the grade-8 NICHD-SECC measurement; STEM attitude, identity, and stereotype items from Nosek et al. (2002); Test of Science-Related Attitudes (TOSRA; Fraser, 1981); and The Math Anxiety Rating Scale (Richardson & Suinn, 1972).

Math and science performance measures are selected to be age-appropriate and for comparison with nationally representative samples. Planned instruments include subtests or specific items from nationally-normed instruments such as ACT, SAT, GRE, NAEP, NELS, TIMSS, Woodcock–Johnson III.

Our general approach to testing our hypotheses with these multivariate, longitudinal data will be latent variable models of change. When at least three occasions of measurement have accrued, latent variable longitudinal methods (e.g., latent growth curve and latent difference score models) minimize effects of measurement error and allow for inclusion of incomplete data records so that no data is lost. The latter is vital, as participants who miss a measurement will not be excluded from multiple occasion analyses. Post publication of our key findings, we will make an anonymous version of the dataset available through our laboratory dataverse:


Effects of an all-girls charter school: Estimates from multilevel models of change (Singer & Willett, 2003) support our prediction that enrollment in YWLCS reduces implicit stereotyping. The estimated effect of YWLCS enrollment on initial IAT D score was -.12 points (t = -2.62, p = .0095), yielding initial estimated D score means of .18 and .06, respectively, for YWLCS-out and YWLCS-in participants. After initial measurement, implicit stereotypes of both groups gradually decreased at the same rate. That is, the estimated effect of time did not vary for the two groups, and the overall estimated effect was a -.006 change in IAT D score, i.e., weaker stereotyping, per month (p = .06). This finding is contrary to cross-sectional evidence of increasing STEM=male bias with age (Nosek et al. 2007), as both groups of girls are evidencing gradual stereotype reduction. Model estimates reveal significant residual variation in individual trajectories for implicit stereotyping and future analyses will investigate effects of age and other covariates that are not yet available.

Endorsement of “natural” explanations for STEM gender gaps is associated with greater implicit STEM=male bias: For N > 20,000 Internet volunteers, regardless of sex or whether they had pursued STEM at the college level or not, implicit science=male stereotyping was positively related to endorsement of a “nature” hypothesis for STEM gender gaps.

The largest gender gaps in implicit gender-science stereotyping occur among scientists: Male science practitioners evidence the strongest stereotypes of any male groups, while women in science evidence the weakest of any female groups. Practitioners in humanities evidence precisely the opposite pattern, women stereotyping STEM most strongly and men the least.

Publications & Presentations: 

Jost, J. T., Nosek, B. A., & Gosling, S. D. (2008). Ideology: Its resurgence in social, personality, and political psychology. Perspectives on Psychological Science, 3, 126-136.

Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV: Procedures and validity. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes: Procedures and controversies (pp. 59-102). New York: Guilford Press.

Nosek, B. A. (2007). Implicit-explicit relations. Current Directions in Psychological Science, 16, 65-69.

Nosek, B. A. (2007). Understanding the individual implicitly and explicitly. International Journal of Psychology, 42, 184-188.

Nosek, B. A., & Hansen, J. J. (2008). The associations in our heads belong to us: Searching for attitudes and knowledge in implicit evaluation. Cognition and Emotion, 22, 553-594. 

Nosek, B. A., & Smyth, F. L. (2007). A multitrait-multimethod validation of the Implicit Association Test: Implicit and explicit attitudes are related but distinct constructs. Experimental Psychology, 54, 14-29.

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social Psychology and the Unconscious: The Automaticity of Higher Mental Processes (pp. 265-292). New York: Psychology Press.

Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., Smith, C. T., Olson, K. R., Chugh, D., Greenwald, A. G., & Banaji, M. R. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36-88.

Ranganath, K. A., & Nosek, B. A. (2008). Implicit attitude formation occurs immediately, explicit generalization takes time. Psychological Science, 19, 249-254.

Ranganath, K. A., Smith, C. T., & Nosek, B. A. (2008). Distinguishing automatic and controlled components of attitudes from direct and indirect measurement. Journal of Experimental Social Psychology, 44, 386-396.

Research Design: 


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