Feminist Methodologies for Critical Researchers by Joey Sprague

Author:Joey Sprague
Language: eng
Format: epub
ISBN: 9780759114678
Publisher: AltaMira Press
Published: 2013-06-24T16:00:00+00:00

How Do They Organize Their Analyses?

The researchers in this sample employ a range of approaches to specifying statistical models. Some focus on direct comparisons of conventional models with models that include measures of gender and other dimensions of social inequality. Others estimate the effects of variables on socially distinct groups in separate equations.

Comparing Models with and without Controls for Gender

In some of these projects, the analysis adopts the strategy of showing how mainstream approaches are limited by their inattention to gender. Researchers build a model that includes all the conventional explanations that have been offered for why groups have different outcomes and estimate how well observed gender differences are explained by those factors. In a second stage, sex or some dimension of gender bias is added as an additional causal variable to see if it is still a significant predictor of inequality. If the effect of a gender measure is significant after controlling for an exhaustive list of other potential causes, its impact is interpreted as indicating some form of discrimination. For example, LeClere, Rogers, and Peters (1998) first used age and race, then added known health risk factors like hypertension, diabetes, and adiposity, and then individual social attributes like SES (socioeconomic status) and marital status to predict the relative risk of death from heart disease. At each stage the race difference decreased, but it actually reversed in the fourth step, when they added rates of female headship in the neighborhood to the model.

Similarly, England and her colleagues (1994, 1996) tested the limitations of conventional, gender-blind economic models explaining wage differences by specifying an equation that estimates the impact on wages of a wide array of occupational features (authority, cognitive and physical skills, discomfort or danger, economic sector, region of the country) and characteristics of the worker (education, experience). Then they added measures of gender discrimination to the equation, including the earnings impact of the proportion of women in an occupation and whether or not that occupation involves taking care of people. Bielby and Bielby (1992) first used the variables that neoclassical economists use to predict a worker’s reluctance to relocate for a job. In a second step they added indicators of gendered considerations like information about the spouse’s job and whether or not the worker subscribed to traditional gender ideology. Comparing the variance explained before and after including gender measures in the model provides clear evidence about the (in)adequacy of gender-blind explanations.

Many of these scholars look for interactions of causal factors with gender and other dimensions of social inequality. That is, they explore how discrimination is not an “add-on” to legitimate causes, but rather, whether the standards we are applying are not applied in the same ways to men and women, whites and others. Survey researchers sometimes include interaction terms within a single equation. For example, Reskin and Ross (1992) tested the significance of the interactions between sex of the manager and all of the other causal variables they used to predict wages, and kept those terms that were statistically significant in their final model.

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