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Intersectional InequalityRace, Class, Test Scores, and Poverty$
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Charles C. Ragin and Peer C. Fiss

Print publication date: 2016

Print ISBN-13: 9780226414379

Published to Chicago Scholarship Online: September 2017

DOI: 10.7208/chicago/9780226414546.001.0001

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From Variables to Fuzzy Sets

From Variables to Fuzzy Sets

(p.61) Four From Variables to Fuzzy Sets
Intersectional Inequality

Charles C. Ragin

Peer C. Fiss

University of Chicago Press

Chapter 4 details the construction of the fuzzy sets we use in the set-theoretic analyses presented in chapters 5-8. Fuzzy sets capture the degree of membership of cases in sets, with scores ranging from 0 to 1. Scores must be calibrated in a manner that is consistent with the conceptualization and labeling of the set in question (e.g., degree of membership in the set of respondents with “high” test scores). The fuzzy sets we create assess degree of membership in various respondent and family background characteristics. To ensure the robustness of our findings, we utilize multiple calibrations of key variables. For example, we calibrate separately degree of membership in the set of respondents with high-income parents and degree of membership in the set of respondents with low-income parents, recognizing the fact that many respondents have low membership scores in both sets.

Keywords:   crisp sets, fuzzy sets, calibration, multiple calibration, set labels, set-theoretic relations, asymmetry, qualitative anchors, set negation, macro-conditions

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