Non-White, No More

Effect Coding as an Alternative to Dummy Coding With Implications for Higher Education Researchers

Datos Bibliográficos

ID23364423
AutoresMatthew J Mayhew (0000-0002-1720-1162), Matthew Mayhew (0000-0003-0061-3731), Jeffrey S Simonoff (0000-0002-1496-608X)
Año2015
Volumen56
Número2
Páginas170-175
Fecha de publicación2015-03-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of college student development (JOURNAL)
Identificadores de la revistaISSN: 0897-5264 • E-ISSN: 1543-3382
EditorialProject MUSE (PUBLISHER • US)
DOI10.1353/csd.2015.0019
OpenAlexW2004378458
IdiomaEN
Citas recibidas55
Referencias citadas2

Non-White, No More:Effect Coding as an Alternative to Dummy Coding With Implications for Higher Education Researchers Matthew J. Mayhew (bio) and Jeffrey S. Simonoff (bio) The purpose of this article is to describe effect coding as an alternative quantitative practice for analyzing and interpreting categorical, race-based independent variables in higher education research. Unlike indicator (dummy) codes that imply that one group will be a reference group, effect codes use average responses as a means for interpreting information. This technique is especially appropriate for examining race, as such a process enables raced subgroups to be compared to each other and does not position responses of any raced group as normative—the standard against which all other race effects are interpreted. The issues raised here apply in any research context where a categorical variable without a natural reference group (e.g., college major) is a potential predictor in a regression model. THEORETICAL ORIENTATION The use of alternative codings, like effect coding, for analyzing and interpreting categorical predictors is not a new idea. According to Struik (1987), the formal operations date back to the Babylonians and their understandings of linear algebra. More recently, Fisher (1921) discussed alternative codings in his early work on the two-way analysis of variance (ANOVA). Expanding the principles articulated in these early efforts, Yates (1934) discussed alternative codings by suggesting that the interpretation of the regression coefficients for indicator variables correspond to ANOVA interpretations of deviations: An overall level, as opposed to one based on a specific group of individuals, could serve as the reference point for interpreting effects. Recently and frequently, these possibilities have been discussed in the methodology literature; examples include Cohen and Cohen (1984), Hardy (1993), Miles and Shevlin (2001), Muller and Fetterman (2002), Kleinbaum, Kupper, Nizam, and Muller (2008), Warner (2008), Rutherford (2011), and Chatterjee & Simonoff (2013). This effort draws on philosophical tenets embedded within critical paradigms for examining the raced experiences of individuals located in a particular sociohistorical and political context. Borrowing from critical race theorists (see Delgado, 1995; Solorzano, 1997), we concede that, in America, systemic oppression of individuals of color is existent, sustained, and culturally reproduced, often by forces ranging from but not limited to overt [End Page 170] hegemonic and discriminatory practices and behaviors to more subtle micro-aggressions, such as the language used to essentialize the experiences of one group over another. Even though it is often unintended, quantitative researchers who work with indicator variables as a means for comparing raced identity patterns may be using language to essentialize the experiences of one group over another. For example, researchers interested in examining effects by race might say, “When compared to White students, African American students are significantly more or less likely to _____.” Here, the language used to describe racial differences uses the White experience as the context for making meaning of the African American experience; thus, the White student experience is essentialized, becoming the norm against which the raced experiences of African American students are understood. The goal of this article is to offer an alternative practice of working with categorical variables in a way that does not use language that essentializes the experiences of any particular group. Related, this study draws on Stage’s (2007) definition of the quantitative criticalist, a researcher who “question[s] the models, measures and analytical practices of quantitative research in order to offer competing models, measures and analytical practices that better describe the experiences of those who have not been adequately represented” (p. 10). Critical quantitative research is sensitive, in particular, to the challenges of examining race. Until now, these challenges have been associated with between-group analyses—taking into account that comparative approaches to subgroup variations can both mask important differences that exist within groups as well as decontextualize the varied experiences of individual groups (for example, see Solorzano, 1997). The current effort extends ideas championed by the quantitative criticalist by problematizing the use of dummy coding in matters of understanding race and its effects on outcomes. With effect coding, there is no reference group per se, and as a result, interpretations of the effects offered would read, “Compared to an overall level, African American/Black students are...

Categorical variable · Categorization · Coding (social sciences) · Epistemology · Normative · Regression analysis · Statistics · Advanced Statistical Methods and Models · Advanced Statistical Modeling Techniques · Artificial Intelligence · Computer Science · Mathematics · Psychology · Social Psychology · Statistics Education and Methodologies