Hostname: page-component-7c8c6479df-hgkh8 Total loading time: 0 Render date: 2024-03-29T11:34:50.168Z Has data issue: false hasContentIssue false

GRace: A MATLAB-Based Application for Fitting the Discrimination-Association Model

Published online by Cambridge University Press:  28 October 2014

Luca Stefanutti*
Affiliation:
University of Padua (Italy)
Michelangelo Vianello
Affiliation:
University of Padua (Italy)
Pasquale Anselmi
Affiliation:
University of Padua (Italy)
Egidio Robusto
Affiliation:
University of Padua (Italy)
*
*Correspondence concerning this article should be addressed to Luca Stefanutti. Department FISPPA. University of Padua. Via Venezia, 8. 35131. Padua (Italy). Phone: +39–0498276687. Fax: +39–0498276600. E-mail: luca.stefanutti@unipd.it

Abstract

The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.

Type
Research Article
Copyright
Copyright © Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anselmi, P., Vianello, M., Stefanutti, L., & Robusto, E. (2013). A poisson race model for the analysis of the Implicit Association Test. TPM - Testing, Psychometrics, Methodology in Applied Psychology, 20, 249261. http://dx.doi.org/10.4473/TPM20.3.4 Google Scholar
Brendl, C. M., Markman, A. B., & Messner, C. (2001). How do indirect measures of evaluation work? Evaluating the inference of prejudice in the Implicit Association Test. Journal of Personality and Social Psychology, 81, 760773. http://dx.doi.org/10.1037/0022-3514.81.5.760 Google Scholar
Broyden, C. G. (1970). The convergence of a class of double-rank minimization algorithms. Journal of the Institute of Mathematics and its Applications, 6, 7690. http://dx.doi.org/10.1093/imamat/6.1.76 CrossRefGoogle Scholar
Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math-gender stereotypes in elementary-school children. Child Development, 82, 766789. http://dx.doi.org/10.1111/j.1467-8624.2010.01529.x Google Scholar
Fletcher, R. (1970). A new approach to variable metric algorithms. Computer Journal, 13, 317322. http://dx.doi.org/10.1093/comjnl/13.3.317 Google Scholar
Goldfarb, D. (1970). A family of variable metric updates derived by variational means. Mathematics of Computing, 24, 2326.Google Scholar
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74, 14641480. http://dx.doi.org/10.1037/0022-3514.74.6.1464 CrossRefGoogle ScholarPubMed
Greenwald, A. G., Smith, C. T., Sriram, N., Bar-Anan, Y., & Nosek, B. A. (2009). Race attitude measures predicted vote in the 2008 U. S. Presidential Election. Analyses of Social Issues and Public Policy, 9, 241253. http://dx.doi.org/10.1111/j.1530-2415.2009.01195.x Google Scholar
Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (1983). Understanding robust and exploratory data analysis. (Eds.), New York, NY: John Wiley & Sons.Google Scholar
Klauer, K. C., Voss, A., Schmitz, F., & Teige-Mocigemba, S. (2007). Process components of the implicit association test: A diffusion-model analysis. Journal of Personality and Social Psychology, 93, 353368. http://dx.doi.org/10.1037/0022-3514.93.3.353 Google Scholar
Maison, D., Greenwald, A. G., & Bruin, R. H. (2004). Predictive validity of the Implicit Association Test in studies of brands, consumer attitudes, and behavior. Journal of Consumer Psychology, 14, 405415. http://dx.doi.org/10.1207/s15327663jcp1404_9 Google Scholar
Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002). Math = male, me = female, therefore math ≠ me. Journal of Personality and Social Psychology, 83, 4459. http://dx.doi.org/10.1037//0022-3514.83.1.44 Google Scholar
Schnabel, K., Asendorpf, J. B., & Greenwald, A. G. (2008). Implicit Association Tests: A landmark for the assessment of implicit personality self-concept. In Boyle, G. J., Matthews, G., & Saklofske, D. H. (Eds.), Handbook of Personality Theory and Testing (pp. 508528). London, UK: Sage.Google Scholar
Shanno, D. F. (1970). Conditioning of quasi-Newton methods for function minimization. Mathematics of Computing, 24, 647656. http://dx.doi.org/10.2307/2004840 CrossRefGoogle Scholar
Stefanutti, L., Robusto, E., Vianello, M., & Anselmi, P. (2013). A discrimination-association model for decomposing component processes of the Implicit Association Test. Behavior Research Methods, 45, 393404. http://dx.doi.org/10.3758/s13428-012-0272-3 Google Scholar
Steffens, M. C., & Schulze König, S. (2006). Predicting spontaneous big five behavior with Implicit Association Tests. European Journal of Psychological Assessment, 22, 1320. http://dx.doi.org/10.1027/1015-5759.22.1.13 Google Scholar
Townsend, J. T., & Ashby, F. G. (1983). Stochastic modeling of elementary psychological processes. New York, NY: Cambridge University Press.Google Scholar
Uhlmann, E., Dasgupta, N., Elgueta, A., Greenwald, A. G., & Swanson, J. E. (2002). Subgroup prejudice based on skin color among Hispanics in the United States and Latin America. Social Cognition, 20, 198226. http://dx.doi.org/10.1521/soco.20.3.198.21104 Google Scholar
White, M. J., & White, G. B. (2006). Implicit and explicit occupational gender stereotypes. Sex Roles, 55, 259266. http://dx.doi.org/10.1007/s11199-006-9078-z Google Scholar
Yamaguchi, S., Greenwald, A. G., Banaji, M. R., Murakami, F., Chen, D., Shiomura, K., … Krendl, A. (2007). Apparent universality of positive implicit self-esteem. Psychological Science, 18, 498500. http://dx.doi.org/10.1111/j.1467-9280.2007.01928.x Google Scholar