Interpreting quantitative data
By: David Byrne.
Publisher: SAGE
Published: 2002
Place of publication: London; Thousand Oaks, Calif
Language: eng
Number of pages: 176
Subjects: Research.; Methodology.; Social sciences -- Statistical methods.
Notes: Includes bibliographical references (p. [166]-170) and index.
Table of contents: title: Machine generated contents note: Introduction 1; title: 1 Interpreting the Real and Describing the Complex:; title: Why We Have to Measure 12; title: Positivism, realism and complexity 14; title: Naturalism - a soft foundationalist argument 17; title: There are no universals but, nevertheless, we can know 19; title: Models and measures: a first pass 21; title: Contingency and method - retroduction and retrodiction 25; title: Conclusion 27; title: 2 The Nature of Measurement: What We Measure and; title: How We Measure 29; title: Death to the variable 29; title: State space 32; title: Classification 34; title: Sensible and useful measuring 37; title: Conclusion 41; title: 3 The State's Measurements: The Construction and; title: Use of Official Statistics 44; title: The history of statistics as measures 45; title: Official and semi-official statistics 49; title: Social indicators 52; title: Tracing individuals 56; title: Secondary data analysis 57; title: Sources 57; title: Conclusion 58; title: 4 Measuring the Complex World: The Character of Social Surveys 61; title: Knowledge production - the survey as process 63; title: Models from surveys - beyond the flowgraph? 66; title: Representative before random - sampling in the real world 72; title: Conclusion 77; title: 5 Probability and Quantitative Reasoning 79; title: Objective probability versus the science of clues 80; title: Single case probabilities - back to the specific 84; title: Gold standard - or dross? 84; title: Understanding Head Start 88; title: Probabilistic reasoning in relation to non-experimental data 90; title: Randomness, probability, significance and investigation 92; title: Conclusion 93; title: 6 Interpreting Measurements: Exploring, Describing and Classifying 95; title: Basic exploration and description 96; title: Making sets of categories - taxonomy as social exploration 99; title: Can classifying help us to sort out causal processes? 105; title: Conclusion 110; title: 7 Linear Modelling: Clues as to Causes 112; title: Statistical models 113; title: Flowgraphs: partial correlation and path analysis 116; title: Working with latent variables - making things out of things; title: that don't exist anyhow 117; title: Multi-level models 120; title: Statistical black boxes - Markov chains as an example 122; title: Loglinear techniques - exploring for interaction 123; title: Conclusion 128; title: 8 Coping with Non-linearity and Emergence: Simulation and; title: Neural Nets 130; title: Simulation - interpreting through virtual worlds 131; title: Micro-simulation - projecting on the basis of aggregation 133; title: Multi-agent models - interacting entities 135; title: Neural nets are not models but inductive empiricists 139; title: Models as icons, which are also tools 141; title: Using the tools 142; title: Conclusion 143; title: 9 Qualitative Modelling: Issues of Meaning and Cause 145; title: From analytic induction through grounded theory to computer; title: modelling - qualitative exploration of cause 147; title: Coding qualitative materials 150; title: Qualitative Comparative Analysis (QCA) - a Boolean approach 154; title: Iconic modelling 157; title: Integrative method 159; title: Conclusion 160; title: Conclusion 162; title: Down with: 162; title: Up with: 163; title: Action theories imply action164.