Set Relations in Social Research
Evaluating Their Consistency and Coverage
Bibliographic Data
| ID | 7971099 |
|---|---|
| Authors | C C Ragin (0000-0001-7931-5408, University of Arizona, corresponding author) |
| Year | 2006 |
| Volume | 14 |
| Issue | 3 |
| Pages | 291-310 |
| Publication date | 2006-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1093/pan/mpj019 |
| OpenAlex | W2150935468 |
| Language | EN |
| Citations received | 385 |
| References cited | 15 |
Because of its inherently asymmetric nature, set-theoretic analysis offers many interesting contrasts with analysis based on correlations. Until recently, however, social scientists have been slow to embrace set-theoretic approaches. The perception was that this type of analysis is restricted to primitive, binary variables and that it has little or no tolerance for error. With the advent of “fuzzy” sets and the recognition that even rough set-theoretic relations are relevant to theory, these old barriers have crumbled. This paper advances the set-theoretic approach by presenting simple descriptive measures that can be used to evaluate set-theoretic relationships, especially relations between fuzzy sets. The first measure, “consistency,” assesses the degree to which a subset relation has been approximated, whereas the second measure, “coverage,” assesses the empirical relevance of a consistent subset. This paper demonstrates further that set-theoretic coverage can be partitioned in a manner somewhat analogous to the partitioning of explained variation in multiple regression analysis
Binary relation · Consistency (knowledge bases · Data mining · Discrete mathematics · Econometrics · Fuzzy logic · Fuzzy set · Measure (data warehouse · Relation (database · Relevance (law · Set (abstract data type · Variation (astronomy · Computer Science · Mathematics · Qualitative Comparative Analysis Research · Artificial Intelligence · Theoretical Computer Science
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| Unique citing works | 385 |
|---|---|
| Citations per year | 19,25 |
| Citation span | 2006 - 2026 (21) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 382 |