Interpreting Quantitative Data

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SAGE, Apr 11, 2002 - Social Science - 176 pages
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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Contents

Interpreting the Real and Describing the Complex Why We Have to Measure
12
Positivism realism and complexity
14
Naturalism a soft foundationalist argument
17
There are no universals but nevertheless we can know
19
a first pass
21
Contingency and method retroduction and retrodiction
25
Conclusion
27
The Nature of Measurement What We Measure and How We Measure
29
Basic exploration and description
96
Making sets of categories taxonomy as social exploration
99
Can classifying help us to sort out causal processes?
105
Conclusion
110
Linear Modelling Clues as to Causes
112
Statistical models
113
partial correlation and path analysis
116
Working with latent variables making things out of things that dont exist anyhow
117

State space
32
Classification
34
Sensible and useful measuring
37
Conclusion
41
The States Measurements The Construction and Use of Official Statistics
44
The history of statistics as measures
45
Official and semiofficial statistics
49
Social indicators
52
Tracing individuals
56
Secondary data analysis
57
Conclusion
58
Measuring the Complex World The Character of Social Surveys
61
Knowledge production the survey as process
63
Models from surveys beyond the flowgraph?
66
Representative before random sampling in the real world
72
Conclusion
77
Probability and Quantitative Reasoning
79
Objective probability versus the science of clues
80
Single case probabilities back to the specific
84
Understanding Head Start
88
Probabilistic reasoning in relation to nonexperimental data
90
Randomness probability significance and investigation
92
Conclusion
93
Interpreting Measurements Exploring Describing and Classifying
95
Multilevel models
120
Statistical black boxes Markov chains as an example
122
Loglinear techniques exploring for interaction
123
Conclusion
128
Coping with Nonlinearity and Emergence Simulation and Neural Nets
130
Simulation interpreting through virtual worlds
131
Microsimulation projecting on the basis of aggregation
133
Multiagent models interacting entities
135
Neural nets are not models but inductive empiricists
139
Models as icons which are also tools
141
Using the tools
142
Conclusion
143
Qualitative Modelling Issues of Meaning and Cause
145
From analytic induction through grounded theory to computer modelling qualitative exploration of cause
147
Coding qualitative materials
150
Qualitative Comparative Analysis QCA a Boolean approach
154
Iconic modelling
157
Integrative method
159
Conclusion
160
Conclusion
162
Action theories imply action
164
Bibliography
166
Index
171
Copyright

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Page 5 - Then they learned to confirm exactly— to confirm a few things, each under specific circumstances. As they emphasized exact confirmation, their techniques inevitably became less flexible. The connection of the most used techniques with past insights was weakened. Anything to which a confirmatory procedure was not explicitly attached was decried as "mere descriptive statistics," no matter how much we had learned from it.

About the author (2002)

David Byrne is Emeritus Professor of Sociology and Applied Social Sciences at the University of Durham. He has published widely on the methodology of social research, for example, in Interpreting Quantitative Data (2002) and with Charles Ragin edited The SAGE Handbook of Case Based Methods (2009). His major theoretical engagement is with the deployment of the complexity frame of reference across the social sciences—see Complexity Theory and the Social Sciences: The State of the Art (with Gillian Callaghan, 2011) with a particular focus on application to policy and practice. His current research focus is on the implications of the transition to the post-industrial in welfare capitalism—Paying for the Welfare State in the 21st Century (with Sally Ruane, 2011) and Class After Industry (2018).

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