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How to beat numbers and not let numbers beat you Professor Charles Pattie.

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Presentation on theme: "How to beat numbers and not let numbers beat you Professor Charles Pattie."— Presentation transcript:

1 How to beat numbers and not let numbers beat you Professor Charles Pattie

2 Why do we need numbers in social science? Why do we distrust stats – and should we? Samples Why do we need numbers in social science? Context: how common or unusual are the things we study? Trend: are they increasing or decreasing? Correlation: what are they related to, and what might influence them?

3 So why do we distrust stats? Lies, damn lies and statistics? Reliability: look at the question and the context North Korean election 2014: 100% vote for Kim Jong-Un! Common sense rules: distrust clearly biased sources!

4 So why do we distrust stats? Lies, damn lies and statistics? Accuracy: how close can we get? Sampling: a statistic is only as good as the sample it is based on An analogy from cooking: tasting a small sample tells you what the whole dish is like

5 Sampling in practice The central limit theorem: why random samples ‘work’:

6 Sampling in practice Rules of thumb – when to trust a statistic. Is the sample (more or less) random? (BIASED samples are bad news) Is the sample large enough? (Think Goldilocks – not too large, not too small) Does the question make real sense? (NB NOTHING to do with stats, EVERYTHING to do with common sense!) If there was an election tomorrow, which party would you vote for? On which day of the year are people most likely to feel unhappy? Do you think the local authority’s urban development policies are successful or not?

7 Context: Is crime out of control? Are we in the grip of a crime explosion? Discuss!

8 Context: Is crime out of control? The National Crime Survey suggests not:

9 Context: Is crime out of control? But do we believe it? Some views from BBC News ‘comment’ section:

10 Context: Is crime out of control Why are people so sceptical? Discuss! Anecdote versus evidence?

11 Trends: A world of numbers? Seeing the wood for the trees: the value of careful presentation e.g. the Gapminder project:

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17 Correlations: A different example…

18 Correlations: UKIP and the 2014 Euro- election Why did Nigel Farage launch UKIP’s 2014 European Parliament election campaign in Sheffield? Should David Cameron (and Ed Miliband and Nick Clegg) be worried about the Euro-election? Can numbers help answer these questions?

19 Why did UKIP launch their campaign here? What do YOU think? Hunches? Ideas? Might it have something to do with who votes UKIP? Evidence from British Election Study Continuous Monitoring Survey (reported in Rob Ford and Matthew Goodwin, 2014, Revolt on the Right, Routledge).

20 Why did UKIP launch their campaign here?

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24 So… what do you think?

25 Correlation: lessons of 2014 Euro- election? Should David Cameron (etc.) be worried? Maybe! YouGov poll, April 2014:

26 Correlation: lessons of 2014 Euro- election?

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28 Why the switch? Second order election theory: Euro-election a risk-free chance to vent frustrations with government

29 But is the 2 nd -order effect getting stronger (or are governments just getting less popular)?

30 Moving on…. We’ve only scratched the surface of using numbers in social science research. Where next? Getting good data Collect it yourself? Data archives and official sources e.g. The UK Data Archive (http://www.data- archive.ac.uk/)http://www.data- archive.ac.uk/ More advanced techniques and software Good introductory guides include: Ian Diamond and Julie Jeffries, 2001, Beginning Statistics: A Guide for Social Scientists, Sage. If you learn one more advanced method, learn REGRESSION! SPSS available ‘free’ to university students Help at MASH

31 Moving on…. And with that – happy analysing!


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