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Why S ocial Sciences A re Not Scientific Enough

Why S ocial Sciences A re Not Scientific Enough. Taagepera, 2008. Chiara Francavilla 27 th September 2010. Aims. Highlight problems in social sciences studies Provide suggestions. Problems. Overuse and misuse of statistical analysis In particular linear regression.

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Why S ocial Sciences A re Not Scientific Enough

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  1. Why Social Sciences Are Not Scientific Enough Taagepera, 2008 Chiara Francavilla 27th September 2010

  2. Aims • Highlight problems in social sciences studies • Provide suggestions

  3. Problems • Overuse and misuse of statistical analysis • In particular linear regression

  4. Problems (cont.) • Prevalence of Descriptive models rather than Predictive • Reversed role of scientists and statisticians

  5. Suggestions • A better balance of methods, more model building • Directional vs quantitative prediction dx\dt ‹0 x=f(x)

  6. Suggestion (cont.) • Quantitatively predictive logical methods • More than directional • Based on logical considerations • Knowledge of conditions under which the model can/cannot apply • Introducible without numerical data

  7. Suggestions (cont.) • How to construct predictive logical models? • In general: consider broad conceptual constrains and specific problems • Guidelines: • Parsimony • Absurdity avoidance: • Contradiction avoidance

  8. Suggestions (cont.) • The more the merrier or the Occam’s razor? • Statisticians’ vsscientists’ goals • Interdependent variables

  9. References Taagepera, 2008. ‘Why social sciences are not scientific enough’ and ‘How to construct predictive models: Simplicity and Nonabsurdity’ in Making Social Sciences More Scientific, Oxford: Oxford UP Fearon and Laitin, 2003. ‘Ethnicity, Insurgency and Civil War’. American Political Science Review, 97(1)

  10. questions • Can we carry out researches in the social science field with the same instruments and methods used in the natural science one? • In referring to the Occam’s razor principle, how can we determine in advance which considerations are not essential? • The construction of a logical model before the collection of the data might influence the latter and lead to biased results. Discuss

  11. Linear regression

  12. Descriptive vs Predictive model Taagepera, 2008:7

  13. EXAMPLE: DESCRIPTIVE MODEL Number and Percentage of Countries with Ongoing Civil Wars by Year from 1945 to 1999 (Fearon and Laitin, 2003)

  14. EXAMPLE: PREDICTIVE MODEL Probability of Civil War Onset per Five-Year Period (Fearon and Laitin, 2003)

  15. Directional vs quantitative prediction Taagepera, 2008:26-27

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