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.
