Interpreting quantitative data / David Byrne.
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Year of Publication: | 2002 |
Language: | English |
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Physical Description: | x, 176 p. :; ill. |
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Table of Contents:
- Machine generated contents note: Introduction 1
- 1 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
- Models and measures: a first pass 21
- Contingency and method - retroduction and retrodiction 25
- Conclusion 27
- 2 The Nature of Measurement: What We Measure and
- How We Measure 29
- Death to the variable 29
- State space 32
- Classification 34
- Sensible and useful measuring 37
- Conclusion 41
- 3 The State's Measurements: The Construction and
- Use of Official Statistics 44
- The history of statistics as measures 45
- Official and semi-official statistics 49
- Social indicators 52
- Tracing individuals 56
- Secondary data analysis 57
- Sources 57
- Conclusion 58
- 4 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
- 5 Probability and Quantitative Reasoning 79
- Objective probability versus the science of clues 80
- Single case probabilities - back to the specific 84
- Gold standard - or dross? 84
- Understanding Head Start 88
- Probabilistic reasoning in relation to non-experimental data 90
- Randomness, probability, significance and investigation 92
- Conclusion 93
- 6 Interpreting Measurements: Exploring, Describing and Classifying 95
- 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
- 7 Linear Modelling: Clues as to Causes 112
- Statistical models 113
- Flowgraphs: partial correlation and path analysis 116
- Working with latent variables - making things out of things
- that don't exist anyhow 117
- Multi-level models 120
- Statistical black boxes - Markov chains as an example 122
- Loglinear techniques - exploring for interaction 123
- Conclusion 128
- 8 Coping with Non-linearity and Emergence: Simulation and
- Neural Nets 130
- Simulation - interpreting through virtual worlds 131
- Micro-simulation - projecting on the basis of aggregation 133
- Multi-agent 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
- 9 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
- Down with: 162
- Up with: 163
- Action theories imply action164.