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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.
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.