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Linear regression analysis, with its many generalizations, is the predominant quantitative method used throughout the social sciences and beyond. The goal of the method is to study relations among variables. In this book, Schoon, Melamed and Breiger turn regression modeling inside out to put the emphasis on the cases (people, organizations, and nations) that comprise the variables. By re-analyzing influential published research, they reveal new insights and present a principled way to unlock a set of more nuanced interpretations than has previously been attainable. The emphasis is on intuition and examples that can be reproduced using the code and datasets provided. Relating their contributions to methodologies that operate under quite different philosophical assumptions, the authors advance multi-method social science and help to bridge the divide between quantitative and qualitative research. The result is a modern, accessible, and innovative take on extracting knowledge from data.
This article investigates whether human masculine plural noun phrases (NPs) in Spanish, which can be interpreted with an exclusively masculine or a mixed-gender meaning, are a case of balanced or unbalanced ambiguity. The results of an experiment using a sentence continuation task with oral stimuli are consistent with the claim that masculine grammatical gender biases listeners toward an exclusively masculine interpretation. The acceptance rate of continuations with the pronoun uno/una referring to a masculine plural antecedent showed that the exclusively masculine meaning of the NP is accessed more frequently and involves a lower cognitive cost than the mixed-gender interpretation. Further, this effect interacts with the stereotypicality of the noun: nouns independently established to carry a masculine stereotype are less likely to be associated with a mixed-gender interpretation. The study also found that the speakers’ attitudes toward nonsexist language predict their acceptance of the mixed-gender interpretation of masculine NPs.
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