Ratio Measurement and Theoretical Inference in Social Research

Bibliographic Data

ID2554680
AuthorsJohn D Kasarda (University of North Carolina at Chapel Hill, corresponding author), Patrick D Nolan (University of South Carolina)
Year1979
Volume58
Issue1
Pages212
Publication date1979-09-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueSocial Forces (JOURNAL)
Journal identifiersISSN: 0037-7732 • E-ISSN: 1534-7605
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.2307/2577794
OpenAlexW4250870842
LanguageEN
Citations received11

This paper attempts to rectify false suspicions that have developed among some regarding ratio correlations. If researchers are theoretically concerned with relationships between ratio variables that contain common components (e.g., population density and suicide rate) or between a ratio variable and one of its components (e.g., percent urban and population size of nations) correlation analysis will not normally yield spurious results. Spuriousness becomes a problem primarily when the researcher is interested in relationships between component variables which are standardized by a common term for reasons extraneous to the hypothesis. Even here, though, it is not the empirical correlation that is spurious, but possible inferences one might draw about the relationship between the component variables from the observed correlations between the ratio-standardized variables. Assessment of suggested alternatives to ratio correlation indicates that ratio measurement sometimes provides conceptual, interpretive, and statistical advantages over alternatives critics have proposed

Component (thermodynamics) · Correlation · Econometrics · Inference · Population · Sociology · Spurious relationship · Statistical hypothesis testing · Statistical inference · Statistics · Variable (mathematics) · Computer Science · Demography · Mathematics · Psychology · Social Psychology · Urban, Neighborhood, and Segregation Studies

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