Making Social Sciences More Scientific: The Need for Predictive Models

by Rein Taagepera

Overview

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. Making Social Sciences More Scientific 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. Making Social Sciences More Scientific 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.

Product Details

ISBN-13:	9780199534661
Publisher:	Oxford University Press
Publication date:	09/15/2008
Edition description:	New Edition
Pages:	232
Product dimensions:	9.10(w) x 6.00(h) x 0.90(d)

About the Author

Rein Taagepera has B.A.Sc. in engineering physics plus M.A. in physics from University of Toronto and Ph.D. in solid state physics plus M.A. in international relations from University of Delaware. After 6 years of industrial research at DuPont Co., he has taught political science at University of California, Irvine since 1970 and also at University of Tartu, Estonia since 1992. He ran third in Estonia's presidential elections 1992, and was in 2001 the founding chair of a political party that later won the elections. He has over 100 research articles in electoral studies, comparative politics, Baltic area studies, Finno-Ugric linguistics, and physics. His books include Seats and Votes (with Matthew Shugart), The Baltic States: Years of Dependence 1940-1990 (with Romuald Misiunas), The Finno-Ugric Republics and the Russian State, and and Predicting Party Sizes (Oxford University Press).

Table of Contents

Preface
Foreword, R Duncan Luce
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