Complexity, Causality, and Control in Statistical Modeling

Social scientists using statistical models and more qualitative techniques frequently employ divergent approaches to thinking about causality. Statistical methodologies tend to draw on probabilistic understandings of causality. Qualitative research traditions, however, have advanced a sophisticated...

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Bibliographic Details
Published in:The American behavioral scientist (Beverly Hills) 2020-01, Vol.64 (1), p.55-73
Main Author: Urlacher, Brian R.
Format: Article
Language:English
Subjects:
Causality
Comparative analysis
Peacekeeping
Qualitative research
Regression analysis
Social scientists
Statistical methods
Theory formation
Traditions
Viability
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Summary:Social scientists using statistical models and more qualitative techniques frequently employ divergent approaches to thinking about causality. Statistical methodologies tend to draw on probabilistic understandings of causality. Qualitative research traditions, however, have advanced a sophisticated framework around necessary and sufficient conditions. In particular, the qualitative comparative analysis approach has embraced theory development that emphasizes equifinality and complex causal relationships. This article reviews the two traditions and explores how a causal framework grounded in necessary and sufficient conditions can be adapted to statistical models. A logistic regression analysis of major contributions to peacekeeping missions is used to illustrate both the viability of blending the two traditions as well as the potential for more sophisticated theory development and testing.
ISSN:0002-7642
1552-3381
DOI:10.1177/0002764219859641