Chapter 11. Quantitative Data Analysis

  1. Peter G. Miller2,
  2. John Strang3,4 and
  3. Peter M. Miller5
  1. Jim Lemon,
  2. Louisa Degenhardt,
  3. Tim Slade and
  4. Katherine Mills

Published Online: 16 FEB 2010

DOI: 10.1002/9781444318852.ch11

Addiction Research Methods

Addiction Research Methods

How to Cite

Lemon, J., Degenhardt, L., Slade, T. and Mills, K. (2010) Quantitative Data Analysis, in Addiction Research Methods (eds P. G. Miller, J. Strang and P. M. Miller), Wiley-Blackwell, Oxford, UK. doi: 10.1002/9781444318852.ch11

Editor Information

  1. 2

    School of Psychology, Deakin University, Victoria, Australia

  2. 3

    National Addiction Centre Institute of Psychiatry, Kings College London, UK

  3. 4

    South London and Maudsley NHS Foundation Trust, London, UK

  4. 5

    Center for Drug and Alcohol Programs, Medical University & South Carolina, Charleston, SC, USA

Author Information

  1. National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia

Publication History

  1. Published Online: 16 FEB 2010
  2. Published Print: 2 APR 2010

ISBN Information

Print ISBN: 9781405176637

Online ISBN: 9781444318852

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Keywords:

  • quantitative data analysis;
  • quantification of information - agreeing on techniques for mapping observations on numeric scales;
  • imagining data – planning the study;
  • ‘ready to drink’ (RTD) beverages;
  • levels of measurement - ‘Nominal’ variables carrying no quantitative information;
  • collecting data and gathering measurements;
  • manipulating data;
  • principal component analysis of beverage ‘liking’ ratings;
  • ratings for white RTD - for males and females;
  • choice of correction procedure - consulting comprehensive reference like Toothaker (1993)

Summary

This chapter contains sections titled:

  • Introduction

  • Imagining data – planning the study

  • Collecting data – gathering the measurements

  • Organising data – structuring the measurements

  • Describing data – what do the data look like?

  • Manipulating data

  • Relationships within the data

  • Interpreting relationships within the data

  • Conclusion and exercises

  • References