Interpreting Quantitative Data

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Overview

How do quantitative methods help us to acquire knowledge of the real world? What are the 'do's' and 'don'ts' of effective quantitative research?

This refreshing and accessible book provides students with a novel and useful resource for doing quantitative research. It offers students a guide on how to: interpret the complex reality of the social world; achieve effective measurement; understand the use of official statistics; use social surveys; understand probability and quantitative reasoning; interpret measurements; apply linear modelling; understand simulation and neural nets; and integrate quantitative and qualitative modelling in the research process.

Jargon-free and written with the needs of students in mind, the book will be required reading for students interested in using quantitative research methods.

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Editorial Reviews

Booknews
If Byrne did not believe that quantitative research in the social sciences were both valid and necessary, he would write a scathing critique of the whole business. But he does so believe, and therefore confines himself to suggesting how to do it better<-- >how social scientists, as cyborgs linked to data managing and processing computers, can combine quantitative and qualitative modes of investigation as the basis for social action. He teaches graduate courses in the philosophy and methods of research, so his treatment is more conceptual than practical. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780761962625
  • Publisher: SAGE Publications
  • Publication date: 4/11/2002
  • Pages: 192
  • Product dimensions: 6.76 (w) x 9.72 (h) x 0.47 (d)

Table of Contents

Introduction
Interpreting the Real and Describing the Complex
Why We Have to Measure
The Nature of Measurement
What We Measure and How We Measure
The State's Measurement
The Construction and Use of Official Statistics
Measuring the Complex World
The Character of Social Surveys
Probability and Quantitative Reasoning
Interpreting Measurements
Exploring, Describing and Classifying
Linear Modelling
Clues as to Causes
Coping with Non-Linearity and Emergence
Simulation and Neural Nets
Qualitative Modelling
Issues of Meaning and Cause
Conclusion

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