The Interaction of Theory and Data

Summary

Theory shapes how data is collected and analyzed in at least three ways. Theoretical concepts inform how we collect data because data attempt to capture and reflect those concepts. Theory provides testable hypotheses that direct our research. Theory also helps us draw conclusions from the results of empirical research. Meanwhile, research using quantitative methods seeks to be rigorous and reproducible. Mathematical models develop the logic of a theory carefully, while statistical methods help us judge whether the evidence matches the expectations of our theories. Quantitative scholars tend to specialize in one approach or the other. The interaction of theory and data for them thus concerns how models and statistical analysis draw on and respond to one another. In the abstract, they work together seamlessly to advance scientific understanding. In practice, however, there are many places and ways this abstract process can stumble. These difficulties are not unique to rigorous methods; they confront any attempt to reconcile causal arguments with reality. Rigorous methods help by making the issues clear and forcing us to confront them. Furthermore, these methods do not ensure arguments or empirical judgments are correct; they only make it easier for us to agree among ourselves when they do.