- 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
- 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
- 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
Abstract and Keywords
This article, which discusses the notion of variables as variate traces; the notions of difference and continuity as control parameters; and the notion of effects as trajectories of complex systems over time, argues that the entities which matter in relation to causality are not variables but systems. Any focus on difference and change needs to be done alongside that similarity and continuity. The point about similar and different initial conditions leading to similar and different outcomes derives from the significance of path dependency in social causality. There are at least three important implications to considering effects as trajectories: classification, the ontological propensity of the case, and time. Statistical modeling based on data sets, including modeling based on longitudinal data series, is always retroductive.
David Byrne is professor of sociology and social policy in the School of Applied Social Sciences, Durham University. He has worked at the interface between the academy and the application of social science throughout his career, including a period as research director of a community development project. Publications include Beyond the Inner City (Open University Press, 1989), Complexity Theory and the Social Sciences (Routledge, 1998), Social Exclusion (Open University Press, 2009), Understanding the Urban (Palgrave-Macmillan, 2001), Interpreting Quantitative Data (Sage, 2002), and Applying Social Science (Policy Press, 2011).
Emma Uprichard is a senior lecturer in the Department of Sociology, Goldsmiths, University of London. She is particularly interested in the methodological challenge of applying complexity theory to the study of change and continuity in the social world. She has substantive research interests in methods and methodology, critical realism, cities, time and temporality, children and childhood, and food.
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