Making Social Sciences More Scientific The Need for Predictive Models

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  • Format: Hardcover
  • Copyright: 2008-09-15
  • Publisher: Oxford University Press

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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.

Author Biography

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

List of Figuresp. xiii
List of Tablesp. xv
The Limitations of Descriptive Methodology
Why Social Sciences Are Not Scientific Enoughp. 3
Can Social Science Approaches Find the Law of Gravitation?p. 14
How to Construct Predictive Models: Simplicity and Nonabsurdityp. 23
Example of Model Building: Electoral Volatilityp. 34
Physicists Multiply, Social Scientists Add-Even When It Does Not Add Upp. 52
All Hypotheses Are Not Created Equalp. 71
Why Most Numbers Published in Social Sciences Are Dead on Arrivalp. 82
Quantitatively Predictive Logical Models
Forbidden Areas and Anchor Pointsp. 95
Geometric Means and Lognormal Distributionsp. 120
Example of Interlocking Models: Party Sizes and Cabinet Durationp. 130
Beyond Constraint-Based Models: Communication Channels and Growth Ratesp. 139
Why we should Shift to Symmetric Regressionp. 154
All Indices Are Not Created Equalp. 176
Synthesis of Predictive and Descriptive Approaches
From Descriptive to Predictive Approachesp. 187
Recommendations for Better Regressionp. 199
Converting from Descriptive Analysis to Predictive Modelsp. 215
Are Electoral Studies a Rosetta Stone for Parts of Social Sciences?p. 225
Beyond Regression: The Need for Predictive Modelsp. 236
Referencesp. 241
Indexp. 249
Table of Contents provided by Ingram. All Rights Reserved.

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