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020926s2002 enka sb 001 0 eng d |
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|z2002514390
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|aGBA1-V1915
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|a0761962611(hbk.) :
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|a9781848608665 (ebook)
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|a076196262X
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|a(OCoLC)646770606
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|aCaPaEBR|cCaPaEBR
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14
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|aHA35|b.B97 2002eb
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04
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|a300.72|221
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|aLB|bLBF|cE012332|dHA35|e.B97|y2002|pBOOK|n3207
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| 100 |
1
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|aByrne, D. S.|q(David S.),|d1947-
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10
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|aInterpreting quantitative data|h[electronic resource] /|cDavid Byrne.
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|aLondon ;|aThousand Oaks, Calif. :|bSAGE,|c2002.
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| 300 |
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|ax, 176 p. :|bill. ;|c25 cm.
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|aIncludes bibliographical references (p. [166]-170) and index.
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|aMachine 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.
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|aElectronic reproduction.|bPalo Alto, Calif. :|cebrary,|d2009.|nAvailable via World Wide Web.|nAccess may be limited to ebrary affiliated libraries.
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0
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|aResearch.
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|aMethodology.
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0
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|aSocial sciences|xStatistical methods.
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|aElectronic books.|2local
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2
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|aebrary, Inc.
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40
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|uhttp://site.ebrary.com/lib/nou/Doc?id=10256800|zAn electronic book accessible through the World Wide Web; click to view
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