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Interpreting quantitative data

Byrne, D. S. (1947-)

Interpreting quantitative data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Interpreting quantitative dataDavid Byrne.
Author:	Byrne, D. S.
Published:	London ;SAGE,2002.
Description:	x, 176 p. :ill. ;25 cm.
Subject:	Research.
Online resource:	An electronic book accessible through the World Wide Web; click to view
ISBN:	0761962611

Interpreting quantitative data
Byrne, D. S.1947-

Interpreting quantitative data[electronic resource] /David Byrne. - London ;SAGE,2002. - x, 176 p. :ill. ;25 cm.

Includes bibliographical references (p. [166]-170) and index.

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.

Electronic reproduction.
Palo Alto, Calif. :
ebrary,
2009.

Available via World Wide Web.

ISBN: 0761962611

Nat. Bib. No.: GBA1-V1915Subjects--Topical Terms:

214540
Research.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: HA35 / .B97 2002eb

Dewey Class. No.: 300.72

Interpreting quantitative data
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504 $a Includes bibliographical references (p. [166]-170) and index.
505 8 $a 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.
533 $a Electronic reproduction. $b Palo Alto, Calif. : $c ebrary, $d 2009. $n Available via World Wide Web. $n Access may be limited to ebrary affiliated libraries.
650 0 $a Research. $3 214540
650 0 $a Methodology. $3 177620
650 0 $a Social sciences $x Statistical methods. $3 182009
655 7 $a Electronic books. $2 local. $3 214472
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