Making Social Sciences More Scientific: Need For Predictive M
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9780199534661
Description:
In his challenging new book Rein Taagepera argues that society needs more from social sciences than they have delivered. One reason for falling short is that social sciences have depended excessively on regression and other statistical approaches, neglecting logical model building. Science is not only about the empirical 'What is?' but also very much about the conceptual 'How ...
In his challenging new book Rein Taagepera argues that society needs more from social sciences than they have delivered. One reason for falling short is that social sciences have depended excessively on regression and other statistical approaches, neglecting logical model building. Science is not only about the empirical 'What is?' but also very much about the conceptual 'How ...
Description:
In his challenging new book Rein Taagepera argues that society needs more from social sciences than they have delivered. One reason for falling short is that social sciences have depended excessively on regression and other statistical approaches, neglecting logical model building. Science is not only about the empirical 'What is?' but also very much about the conceptual 'How should it be on logical grounds?' Statistical approaches are essentially descriptive, while quantitatively formulated logical models are predictive in an explanatory way. Why Social Sciences Are Not Scientific Enough contrasts the predominance of statistics in today's social sciences and predominance of quantitatively predictive logical models in physics. It shows how to construct predictive models and gives social science examples. Why Social Sciences Are Not Scientific Enough is useful to students who wish to learn the basics of the scientific method and to all those researchers who look for ways to do better social science.
Table of Contents:
Preface; Foreword; PART I. THE LIMITATIONS OF DESCRIPTIVE METHODOLOGY; 1. Why Social Sciences Are Not Scientific Enough; 2. Can Social Science Approaches Find the Law of Gravitation?; 3. How to Construct Predictive Models: Simplicity and Non-Absurdity; 4. Example of Model Building: Electoral Volatility; 5. Physicists Multiply, Social Scientists Add--Even when It Doesn't Add up; 6. All Hypotheses Are Not Created Equal; 7. Why Most Numbers Published in Social Sciences Are Dead on Arrival; PART II. QUANTITATIVELY PREDICTIVE LOGICAL MODELS; 8. Forbidden Areas and Anchor Points; 9. Geometric Means and Lognormal Distributions; 10. Example of Interlocking Models: Party Sizes and Cabinet Duration; 11. Beyond Constraint-Based Models: Communication Channels and Growth Rates; 12. Why We Should Shift to Symmetric Regression; 13. All Indices Are Not Created Equal; PART III. SYNTHESIS OF PREDICTIVE AND DESCRIPTIVE APPROACHES; 14. From Descriptive to Predictive Approaches; 15. Recommendations for Better Regression; 16. Converting from Descriptive Analysis to Predictive Models; 17. Are Electoral Studies a Rosetta Stone for Parts of Social Sciences?; 18. Beyond Regression: The Need for Predictive Models; References; Index
In his challenging new book Rein Taagepera argues that society needs more from social sciences than they have delivered. One reason for falling short is that social sciences have depended excessively on regression and other statistical approaches, neglecting logical model building. Science is not only about the empirical 'What is?' but also very much about the conceptual 'How should it be on logical grounds?' Statistical approaches are essentially descriptive, while quantitatively formulated logical models are predictive in an explanatory way. Why Social Sciences Are Not Scientific Enough contrasts the predominance of statistics in today's social sciences and predominance of quantitatively predictive logical models in physics. It shows how to construct predictive models and gives social science examples. Why Social Sciences Are Not Scientific Enough is useful to students who wish to learn the basics of the scientific method and to all those researchers who look for ways to do better social science.
Table of Contents:
Preface; Foreword; PART I. THE LIMITATIONS OF DESCRIPTIVE METHODOLOGY; 1. Why Social Sciences Are Not Scientific Enough; 2. Can Social Science Approaches Find the Law of Gravitation?; 3. How to Construct Predictive Models: Simplicity and Non-Absurdity; 4. Example of Model Building: Electoral Volatility; 5. Physicists Multiply, Social Scientists Add--Even when It Doesn't Add up; 6. All Hypotheses Are Not Created Equal; 7. Why Most Numbers Published in Social Sciences Are Dead on Arrival; PART II. QUANTITATIVELY PREDICTIVE LOGICAL MODELS; 8. Forbidden Areas and Anchor Points; 9. Geometric Means and Lognormal Distributions; 10. Example of Interlocking Models: Party Sizes and Cabinet Duration; 11. Beyond Constraint-Based Models: Communication Channels and Growth Rates; 12. Why We Should Shift to Symmetric Regression; 13. All Indices Are Not Created Equal; PART III. SYNTHESIS OF PREDICTIVE AND DESCRIPTIVE APPROACHES; 14. From Descriptive to Predictive Approaches; 15. Recommendations for Better Regression; 16. Converting from Descriptive Analysis to Predictive Models; 17. Are Electoral Studies a Rosetta Stone for Parts of Social Sciences?; 18. Beyond Regression: The Need for Predictive Models; References; Index
| Autor | Taagepera, Rein |
|---|---|
| Ilmumisaeg | 2008 |
| Kirjastus | Oxford University Press |
| Köide | Kõvakaaneline |
| Bestseller | Ei |
| Lehekülgede arv | 272 |
| Pikkus | 234 |
| Laius | 234 |
| Keel | English |
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