Methods For Macrosociological Research by Krishnan Namboodiri

Methods For Macrosociological Research

byKrishnan Namboodiri

Hardcover | June 20, 1994

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Strategies of analysis for macrosociological data have long relied on questionable methodology which sacrifices information in exchange for summary measures.
This practical book provides the means to handle distributions and compositional data without forfeiting the information from which they derive. Real-world data and illustrations are featured throughout the text, and for easy reference, each chapter contains a summary and bibliography.
For further ease, this handy reference is divided into three sections. Part I discusses comparative analysis of distributions, compositions, sequences of rates and other vectors. Part II studies the interdependence of the parts which together make up the whole. Part III investigates the aggregative analysis of such parts in both static and dynamic terms.

Key Features
* Chapters organized around substantive, current issues
* Illustrations based on real data
* Bibliographic notes guide readers to further readings
* Concluding remarks at chapter end focus attention on topics for additional study

Details & Specs

Title:Methods For Macrosociological ResearchFormat:HardcoverDimensions:293 pages, 9 × 6 × 0.79 inPublished:June 20, 1994Publisher:Academic PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0125133456

ISBN - 13:9780125133456

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Table of Contents

Introduction.
Comparative Analysis of Distributions, Compositions, and Vectors:
Distributions with Unordered Categories.
Distributions with Ordered Categories and Vectors with Ordered Elements.
Ordinal Analysis of Distributions (Vectors).
Relational Nature of Social Life:
Networks.
The Input-Output Framework.
An Application of the Input-Output Model.
A Look at Parts:
Education as a Component of a Larger Social System.
Households as Study Objects.
Postscript.
Bibliography.
Author Index.
Subject Index.

From Our Editors

This practical book provides the means to handle distributions and compositional data without forfeiting the information from which they derive.