In this book the author challenges the position of statistical analysis as the main quantitative tool used in social sciences. Why Social Sciences Are Not Scientific Enough will be of interest to social science students, researchers, and methodologists.
In this book the author challenges the position of statistical analysis as the main quantitative tool used in social sciences. Why Social Sciences Are Not Scientific Enough will be of interest to social science students, researchers, and methodologists.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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).
Inhaltsangabe
* 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
* 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
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