Interpreting Quantitative Data

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.

Preface

ix

Introduction

1

(11)

Interpreting the Real and Describing the Complex: Why We Have to Measure

12

(17)

Positivism, realism and complexity

14

(3)

Naturalism - a soft foundationalist argument

17

(2)

There are no universals but, nevertheless, we can know

19

(2)

Models and measures: a first pass

21

(4)

Contingency and method - retroduction and retrodiction

25

(2)

Conclusion

27

(2)

The Nature of Measurement: What We Measure and How We Measure

29

(15)

Death to the variable

29

(3)

State space

32

(2)

Classification

34

(3)

Sensible and useful measuring

37

(4)

Conclusion

41

(3)

The State's Measurements: The Construction and Use of Official Statistics

44

(17)

The history of statistics as measures

45

(4)

Official and semi-official statistics

49

(3)

Social indicators

52

(4)

Tracing individuals

56

(1)

Secondary data analysis

57

(1)

Sources

57

(1)

Conclusion

58

(3)

Measuring the Complex World: The Character of Social Surveys

61

(18)

Knowledge production - the survey as process

63

(3)

Models from surveys - beyond the flowgraph?

66

(6)

Representative before random - sampling in the real world

72

(5)

Conclusion

77

(2)

Probability and Quantitative Reasoning

79

(16)

Objective probability versus the science of clues

80

(4)

Single case probabilities - back to the specific

84

(1)

Gold standard - or dross?

84

(4)

Understanding Head Start

88

(2)

Probabilistic reasoning in relation to non-experimental data

90

(2)

Randomness, probability, significance and investigation

92

(1)

Conclusion

93

(2)

Interpreting Measurements: Exploring, Describing and Classifying

95

(17)

Basic exploration and description

96

(3)

Making sets of categories - taxonomy as social exploration

99

(6)

Can classifying help us to sort out causal processes?

105

(5)

Conclusion

110

(2)

Linear Modelling: Clues as to Causes

112

(18)

Statistical models

113

(3)

Flowgraphs: partial correlation and path analysis

116

(1)

Working with latent variables - making things out of things that don't exist anyhow

117

(3)

Multi-level models

120

(2)

Statistical black boxes - Markov chains as an example

122

(1)

Loglinear techniques - exploring for interaction

123

(5)

Conclusion

128

(2)

Coping with Non-linearity and Emergence: Simulation and Neural Nets

130

(15)

Simulation - interpreting through virtual worlds

131

(2)

Micro-simulation - projecting on the basis of aggregation

133

(2)

Multi-agent models - interacting entities

135

(4)

Neural nets are not models but inductive empiricists

139

(2)

Models as icons, which are also tools

141

(1)

Using the tools

142

(1)

Conclusion

143

(2)

Qualitative Modelling: Issues of Meaning and Cause

145

(17)

From analytic induction through grounded theory to computer modelling - qualitative exploration of cause

147

(3)

Coding qualitative materials

150

(4)

Qualitative Comparative Analysis (QCA) - a Boolean approach

154

(3)

Iconic modelling

157

(2)

Integrative method

159

(1)

Conclusion

160

(2)

Conclusion

162

(4)

Down with:

162

(1)

Up with:

163

(1)

Action theories imply action

164

(2)

Bibliography

166

(5)

Index

171

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Interpreting Quantitative Data

Summary

Table of Contents

Supplemental Materials