- [UNTITLED]
- Dedication
- Preface
- Contributors
- Introduction: Doing Philosophy of Social Science
- Micro, Macro, and Mechanisms
- Mechanisms, Causal Modeling, and the Limitations of Traditional Multiple Regression
- Process Tracing and Causal Mechanisms
- Descriptive-Causal Generalizations: “Empirical Laws” in the Social Sciences?
- Useful Complex Causality
- Partial Explanations in Social Science
- Counterfactuals
- Mechanistic Social Probability: How Individual Choices and Varying Circumstances Produce Stable Social Patterns
- The Impact of Duhemian Principles on Social Science Testing and Progress
- Philosophy and the Practice of Bayesian Statistics in the Social Sciences
- Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences
- RCTs, Evidence, and Predicting Policy Effectiveness
- Bringing Context and Variability Back into Causal Analysis
- The Potential Value of Computational Models in Social Science Research
- Models of Culture
- Norms
- The Evolutionary Program in Social Philosophy
- Cultural Evolution: Integration and Skepticism
- Coordination and the Foundations of Social Intelligence
- Making Race Out of Nothing: Psychologically Constrained Social Roles
- A Feminist Empirical and Integrative Approach in Political Science: Breaking Down the Glass Wall?
- Social Constructions of Mental Illness
- Cooperation and Reciprocity: Empirical Evidence and Normative Implications
- Evaluating Social Policy
- Values and the Science of Well-Being: A Recipe for Mixing
- Index
Abstract and Keywords
This article argues that strong “descriptive-causal generalizations” are in fact quite common, at least compared to the standard belief of approximately zero. The philosophy of the special sciences has a great deal to present to those interested in empirical laws and strong generalizations in the social sciences. Social scientists do find and make strong, and empirically supported, causal generalizations. Descriptive-causal generalizations and perfect predictors are in fact the same phenomenon. It will be very hard to defeat the descriptive-causal generalizations using standard statistical strategies. A descriptive-causal generalization sometimes receives the name “empirical law.” The democratic peace is one of the most famous descriptive-causal generalizations in political science. The term “empirical law” indicates that there are multiple dimensions which scientists use to assess generalizations.
Keywords: descriptive-causal generalizations, special sciences, social scientists, standard statistical strategies, empirical law, democratic peace, political science
Gary Goertz is professor of political science at the University of Arizona. He is the author or editor of nine books and over forty articles on issues of methodology, international institutions, and conflict studies, including Necessary Conditions: Theory, Methodology, and Applications (Rowman & Littlefield, 2003), Social Science Concepts: A User’s Guide (Princeton University Press, 2006), Explaining War and Peace: Case Studies and Necessary Condition Counterfactuals (Routledge, 2007), Politics, Gender, and Concepts: Theory and Methodology (Cambridge University Press, 2008), and A Tale of Two Cultures: Contrasting Qualitative and Quantitative Paradigms (Princeton University Press, 2012).
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- [UNTITLED]
- Dedication
- Preface
- Contributors
- Introduction: Doing Philosophy of Social Science
- Micro, Macro, and Mechanisms
- Mechanisms, Causal Modeling, and the Limitations of Traditional Multiple Regression
- Process Tracing and Causal Mechanisms
- Descriptive-Causal Generalizations: “Empirical Laws” in the Social Sciences?
- Useful Complex Causality
- Partial Explanations in Social Science
- Counterfactuals
- Mechanistic Social Probability: How Individual Choices and Varying Circumstances Produce Stable Social Patterns
- The Impact of Duhemian Principles on Social Science Testing and Progress
- Philosophy and the Practice of Bayesian Statistics in the Social Sciences
- Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences
- RCTs, Evidence, and Predicting Policy Effectiveness
- Bringing Context and Variability Back into Causal Analysis
- The Potential Value of Computational Models in Social Science Research
- Models of Culture
- Norms
- The Evolutionary Program in Social Philosophy
- Cultural Evolution: Integration and Skepticism
- Coordination and the Foundations of Social Intelligence
- Making Race Out of Nothing: Psychologically Constrained Social Roles
- A Feminist Empirical and Integrative Approach in Political Science: Breaking Down the Glass Wall?
- Social Constructions of Mental Illness
- Cooperation and Reciprocity: Empirical Evidence and Normative Implications
- Evaluating Social Policy
- Values and the Science of Well-Being: A Recipe for Mixing
- Index
