Abstract
The purpose of this paper is to describe effect coding as an alternative quantitative practice for analyzing and interpreting categorical, multi-raced independent variables in higher education research. Not only may effect coding enable researchers to get closer to respondents' original intentions, it allows for more accurate analyses of all race based categories.
This is a preview of subscription content, log in via an institution to check access.
Notes
A key assumption underlying the approach proposed here is that the expected response of a member of multiple groups is the unweighted average of the responses of each group. It is possible that this assumption is inappropriate in a particular circumstance.
An important advantage of the proposed approach is that researchers can use it to perform a sensitivity analysis to validate the key assumption that the expected response of a member of multiple groups is the unweighted average of the responses of each group. By using dummy variables with nonzero values that differ from variable to variable to define the effect codings, researchers can fit a model with a different weighting scheme for the effect of each race on the response of a multiracial person. Varying these weights allows the researcher to see if the results are sensitive to the values of these weights. If results are not sensitive and do not change when weights are varied, we can be confident the same results will occur regardless of the weights chosen; in other words, the weights do not matter. If the results are sensitive and change when weights are added, we cannot be confident that the adoption of the unweighted average of responses of each group is the most appropriate way of effect coding for bi- and multi-raced students; this sensitivity would indicate that there may be another systematic pattern in the way bi- or multi-raced students are responding to questions about race. In this case, further analyses of these patterns would be warranted.
References
Ballard Brown, T. (2013). No more ‘Negro’ for Census Bureau forms and surveys. The Two-Way: Breaking News from NPR. Retrieved from http://www.npr.org/blogs/thetwo-way/2013/02/25/172885551/no-more-negro-for-census-bureau-forms-and-surveys.
Cohen, J., & Cohen, P. (1984). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.), Hillsdale: Lawrence Erlbaum Associates.
Delgado, R. (1995). (Ed). Critical race theory: The cutting edge. Philadelphia: Temple University Press.
Fisher, R. A. (1921). Studies in crop variation. I. An examination of the yield of dressed grain from Broadbalk. Journal of Agricultural Science, 11, 107–135.
Groves, R. (2010). The word “Negro.” Director’s Blog. Retrieved from:http://directorsblog.blogs.census.gov/2010/01/19/the-word-negro/.
Hardy, M. A. (1993). Regression with dummy variables. Newbury Park: Sage.
Kleinbaum, D. G., Kupper, L. L., Nizam, A., & Muller, K. A. (2008). Applied regression analysis and other multivariable methods. Belmont: Thomson Brooks/Cole.
Mayhew, M. J., & Simonoff, J. S. (in press). Nonwhite, no more: Effect coding as an alternative to dummy coding with implications for researchers in higher education. Journal of College Student Development.
Miles, J., & Shevlin, M. (2001). Applying regression and correlation: A guide for students and researchers. London: Sage.
Muller, K. E., & Fetterman, B. A. (2002). Regression and ANOVA: An integrated approach using SAS software. Cary: SAS Institute.
Rutherford, A. (2011). ANOVA and ANCOVA: A GLM Approach (2nd ed.), Hoboken, NJ: Wiley.
Solorzano, D. (1997). Images and words that wound: Critical race theory, racial stereotyping, and teacher education. Teacher Education Quarterly, 24, 5–19.
Stage, F. K. (2007). Answering critical questions using quantitative data. In F. Stage (Ed.), Using quantitative data to answer critical questions (pp. 5–23). San Francisco: Jossey-Bass.
Starr, B. (2014). Army says word “Negro” OK to use. CNN. Retrieved from:http://www.cnn.com/2014/11/05/politics/army-says-word-negro-ok-to-use/.
Teranishi, R. T. (2010). Asians in the ivory tower: Dilemmas of racial inequality in american higher education. New York: Teachers College Press.
Warner, R. M. (2008). Applied statistics: From bivariate through multivariate techniques. Thousand Oaks: Sage.
Yates, F. (1934). The analysis of multiple classifications with unequal numbers in the different classes. Journal of the American Statistical Association, 29, 51–66.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mayhew, M.J., Simonoff, J.S. Effect Coding as a Mechanism for Improving the Accuracy of Measuring Students Who Self-Identify with More than One Race. Res High Educ 56, 595–600 (2015). https://doi.org/10.1007/s11162-015-9364-0
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11162-015-9364-0