## Interpreting Quantitative DataHow do quantitative methods help us to acquire knowledge of the real world? What are the `do's' and `don'ts' of effective quantitative research? This refreshing and accessible book provides students with a novel and useful resource for doing quantitative research. It offers students a guide on how to: interpret the complex reality of the social world; achieve effective measurement; understand the use of official statistics; use social surveys; understand probability and quantitative reasoning; interpret measurements; apply linear modelling; understand simulation and neural nets; and integrate quantitative and qualitative modelling in the research process. Jargon-free and written with the needs of students in mind, the book will be required reading for students interested in using quantitative research methods. |

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### Contents

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 |

a first pass | 21 |

Contingency and method retroduction and retrodiction | 25 |

Conclusion | 27 |

The Nature of Measurement What We Measure and How We Measure | 29 |

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 |

Linear Modelling Clues as to Causes | 112 |

Statistical models | 113 |

partial correlation and path analysis | 116 |

Working with latent variables making things out of things that dont exist anyhow | 117 |

State space | 32 |

Classification | 34 |

Sensible and useful measuring | 37 |

Conclusion | 41 |

The States Measurements The Construction and Use of Official Statistics | 44 |

The history of statistics as measures | 45 |

Official and semiofficial statistics | 49 |

Social indicators | 52 |

Tracing individuals | 56 |

Secondary data analysis | 57 |

Conclusion | 58 |

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 |

Probability and Quantitative Reasoning | 79 |

Objective probability versus the science of clues | 80 |

Single case probabilities back to the specific | 84 |

Understanding Head Start | 88 |

Probabilistic reasoning in relation to nonexperimental data | 90 |

Randomness probability significance and investigation | 92 |

Conclusion | 93 |

Interpreting Measurements Exploring Describing and Classifying | 95 |

Multilevel models | 120 |

Statistical black boxes Markov chains as an example | 122 |

Loglinear techniques exploring for interaction | 123 |

Conclusion | 128 |

Coping with Nonlinearity and Emergence Simulation and Neural Nets | 130 |

Simulation interpreting through virtual worlds | 131 |

Microsimulation projecting on the basis of aggregation | 133 |

Multiagent 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 |

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 |

Action theories imply action | 164 |

166 | |

171 | |

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### Common terms and phrases

actual aggregate analysis analytic induction approach argue argument assertion basis Bateson causal cause Chapter character Cilliers classification cluster coding complex systems components conception consider construct context contingency table data set derived describe Desrosieres discussion dynamic emergence essentially example experimental exploration exploratory frequentist gender grounded theory hermeneutic household hypothesis icons implications important individual inference interaction interpretation issue kind knowledge Latent Variable Linear Model loglinear mathematical means Measured Variable mechanisms methods nature neural net neural nets neural network non-linear null hypothesis parameters population positivism problem procedures programme qualitative random randomized controlled trial ratio scale real complex systems realism reality reification relation relationship representation retrodiction sample significance simulation single social indicators social science social world South Shields specific SPSS statistical structure taxonomy techniques things tion trajectories understanding validity variate traces whole words

### Popular passages

Page 5 - Then they learned to confirm exactly— to confirm a few things, each under specific circumstances. As they emphasized exact confirmation, their techniques inevitably became less flexible. The connection of the most used techniques with past insights was weakened. Anything to which a confirmatory procedure was not explicitly attached was decried as "mere descriptive statistics," no matter how much we had learned from it.