Fuzzy Set Theory: Applications in the Social Sciences

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More About This Textbook

Overview

This book introduces fuzzy set theory to social science researchers. Fuzzy sets are categories with blurred boundaries. With classical sets, objects are either in the set or not, but objects can belong partially to more than one fuzzy set at a time. Many concepts in the social sciences have this characteristic, and fuzzy set theory provides methods for systematically dealing with them. A primary reason for not going beyond programmatic statements and rather unsophisticated uses of fuzzy set theory has been the lack of practical methods for combining fuzzy set concepts with statistical methods. This monograph takes that topic as its major focus, and provides explicit guides for researchers who would like to harness fuzzy set concepts while being able to make statistical inferences and test their models. Real examples and data-sets from several disciplines illustrate the techniques and applications, demonstrating how a combination of fuzzy sets and statistics enable researchers to analyze their data in new ways.

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Editorial Reviews

Marinano Ruiz Espejo
"I think that the book is a simple and accessible introduction to the theory of fuzzy sets and it includes many examples, formulae, figures, and tables which illustrate its contents. The presentation of the book and the academic content are very careful, which make for pleasant reading."
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Product Details

Meet the Author

Research Interests: Ignorance and Uncertainty, Risk, Decision Making, Statistical Methods for Psychological Research, Social Dilemmas, Fuzzy Logic, Philosophy and Social Psychology of Science

Teaching:

Introductory Graduate Statistics, Linear Models, Generalized Linear Models, Psychometrics, Item Response Theory

Research:

My research is focused on the intersection of statistics and behavioral science. I have done work in a number of areas.

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Table of Contents

Series Editor’s Introduction
Acknowledgments
1. Introduction
2. An Overview of Fuzzy Set Mathematics
2.1 Set Theory
2.2 Why Fuzzy Sets?
2.3 The Membership Function
2.4 Operations of Fuzzy Set Theory
2.5 Fuzzy Numbers and Fuzzy Variables
2.6 Graphical Representations of Fuzzy Sets
3. Measuring Membership
3.1 Introduction
3.2 Methods for Constructing Membership Functions
3.3 Measurement Properties Required for Fuzzy Sets
3.4 Measurement Properties of Membership Functions
3.5 Uncertainty Estimates in Membership Assignment
4. Internal Structure and Properties of a Fuzzy Set
4.1 Cardinality: The Size of a Fuzzy Set
4.2 Probability Distributions for Fuzzy Sets
4.3 Defining and Measuring Fuzziness
5. Simple Relations Between Fuzzy Sets
5.1 Intersection, Union, and Inclusion
5.2 Detecting and Evaluating Fuzzy Inclusion
5.3 Quantifying and Modeling Inclusion: Ordinal Membership Scales
5.4 Quantified and Comparable Membership Scales
6. Multivariate Fuzzy Set Relations
6.1 Compound Set Indexes
6.2 Multiset Relations: Comorbidity, Covariation, and Co-Occurrence
6.3 Multiple and Partial Intersection and Inclusion
7. Concluding Remarks
References
Index
About the Authors
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