Measurement in the Social Sciences
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A01=Hubert M. Blalock
advanced measurement error models
Allen M. Shinn
Alvin L. Jacobson
Author_Hubert M. Blalock
Bales Categories
C. E. Werts
Category=JHBC
causal modeling techniques
Dana Quade
Discordant Pairs
Donald R. Ploch
Epistemic Correlations
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eq_isMigrated=2
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Fechner Integrals
Guttman Scaling
H. M. Blalock
H. T. Reynolds
Jean Tuttle Warren
John Frederick Long
John L. Sullivan
K. G. JReskog
Kruskal's Gamma
latent variable estimation
Logarithmic Interval Scale
Magnitude Estimation
Measurement Error
Michael T. Hannan
Multiple Indicator Approaches
multivariate data analysis
N. M. Lalu
nonparametric statistics
Nonrandom Error
Nonrandom Measurement Error
Ordinal Hypothesis
Ordinal Measures
panel data methodology
Partial Correlation
Paul H. Wilken
Paul W. Holland
Preferential Choice Data
psychometric scaling
R. L. Linn
Random Measurement Error
Relevant Pairs
Richard Rubinson
Robert L. Hamblin
Roy G. D'Andrade
Samuel Leinhardt
Semantic Similarity Ratings
Single Indicator Models
Social Science Research
Sociometric Test
Strict Hypothesis
Thomas P. Wilson
Total Correlations
Vice Versa
Product details
- ISBN 9780202302720
- Weight: 635g
- Dimensions: 152 x 229mm
- Publication Date: 30 Jun 1974
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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Among the frustrations constantly confronting the social scientist are those associated with the general process of measurement. The importance of good measurement has long been recognized in principle, but it has often been neglected in practice in many of the social sciences. Now that the methodological tools of multivariate analysis, simultaneous-equation estimation, and causal modeling are diffused more widely into the social sciences, and now that the very serious implications of random and non-random measurement errors are being systematically investigated, it is all the more important that social scientists give top priority to the quality of their data and the clarity of their theoretical conceptualizations. The book is organized so that, one proceeds from problems of data collection to those of data analysis. It is not intended to be a complete work covering all types of measurement problems that have arisen in the social sciences. Instead, it represents a series of studies that are deemed to be crucial for the advancement of social science research but which have not received sufficient attention in most of the social sciences. The basic purpose is to stimulate further methodological research on measurement and to study the ways in which knowledge that has been accumulated in some fields may be generalized. Part I is concerned with applying scaling approaches developed in psychometrics to problems that arise in other social sciences. The focus is on finding better ways to ask questions of respondents so as to raise the level of measurement above that of simple ordinal scales. Part II focuses on multiple-indicator theory and strategies as applied to relatively complex models and to change data. In this section the emphasis shifts to how one analyzes fallible data through the construction of explicit measurement-error models. Part III deals with the statistical analysis of ordinal data, including the interpretation and empirical behaviors of various ordinal measures of association.
H.M. Blalock, Jr. (1926-1991) was professor of sociology at the University of Washington, Seattle. He was recipient of the 1973 ASA Samuel Stouffer Prize, and was a Fellow of the American Statistical Association and the American Academy of Arts and Sciences, and is a member of the National Academy of Sciences.
