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Theory Development

3. Causal Complexity

Experiment vs. Nature

It might seem that the solution to the problem of complexity is to simplify by constructing experiments in which the effects of other causes can be neutralized by random assignments of treatments for levels of the causes in question, so that fundamental relationships can be identified. But this strategy has not proved successful. Not only are there few usable results of this kind even in experimental psychology, once the relationships are taken out of the laboratory and applied to causally complex actual situations, they fail to predict successfully as a result of interferences from other causes.

The primary alternative to this method is the identification of patterns of relationships between variables. Normally this is a matter of identifying a correlation or statistical relationship in the data, though usually with a significant degree of error or unaccounted-for variation. In many science contexts, such as engineering, the same kind of empirically-based modeling of predictive relationships is standard practice, and often for the same reason, there is an absence of theories which allow for prediction. Ordinarily in these cases, which involve physical magnitudes, the relevant casual relationships are reasonably well understood and the relationships are estimated from data collected from experiments designed to isolate the relationships in question.

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In social science, the data are almost never experimental, though there are some exceptions. Typically the available data has been collected either for other purposes or as part of a standard package of statistical measures, such as the data collected for the census, or collected in relation to some specific public policy concern, such as the question of the efficacy of early childhood education programs.