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Exploring Data. An Introduction to Data Analysis for Social Scientists. Edition No. 2

  • ID: 5225083
  • Book
  • November 2008
  • 305 Pages
  • John Wiley and Sons Ltd
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The updated edition of this classic text introduces a range of techniques for exploring quantitative data. Beginning with an emphasis on descriptive statistics and graphical approaches, it moves on in later chapters to simple strategies for examining the associations between variables using inferential statistics such as chi squared. The book has been substantially revised to include the most recent approaches to data analysis, and includes step-by-step instructions on using SPSS. All these techniques are illustrated with intriguing real examples, drawn from important social research over the past three decades, designed to illuminate significant sociological and political debates.

The book shows how students can use quantitative data to answer various questions:

Is it true that the rich are getting richer and the poor are getting poorer?

Are crime rates really going down, and how can we tell?

How much alcohol do men and women really drink in an average week?

Which country in Europe has the highest average working hours?

Readers are encouraged to explore data for themselves, and are carefully guided through the opportunities and pitfalls of using statistical packages, as well as the numerous data sources readily available online.

Suitable for those with no previous experience of quantitative data analysis, the second edition of Exploring Data will be invaluable to students across the social sciences.

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Detailed Table of Contents.

List of Figures.



Part I: Single Variables.

1. Distribution Variables.

2. Numerical Summaries of Level and Spread.

3. Scaling and Standardising.

4. Inequality.

5. Smoothing Time Series.

Part II: Relationships between Two Variables.

6. Percentage Tables.

7. Analysing Contingency Tables.

8. Handling Several Batches.

9. Scatterplots and Resistant Lines.

10. Transformations.

Part III: Introducing a Third Variable.

11. Causal Explanations.

12. Three-Variable Contingency Tables and Beyond.

13. Longitudinal Data.



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Catherine Marsh
Jane Elliott
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