4. Prediction vs. Explanation
Not only the data but the interests of social science theorizing and much of its language are generated from normative, practical, policy, or common sense concerns. Topics such as adolescent pregnancy, for example, are both policy and normative interests that require explanations– explanatory theories or models– in order to intervene in the causal process to alter outcomes. This topic is an example of the problem of the difference between prediction and explanation.
There are some good predictors of risk for adolescent pregnancy, such as smoking. Although knowing that smoking is a predictor might be useful as a means of identifying the adolescents who might be made the subject of an intervention, smoking is not a cause of pregnancy, so intervening by preventing smoking is not going to be an effective method of reducing adolescent pregnancy. This requires causal knowledge.
But the underlying causes that produce both smoking and the behavior that leads to adolescent pregnancy are far more complex, heterogeneous, and difficult to either identify or work with than the simple fact of smoking.
Moreover, this particular relationship only holds in those social contexts in which smoking has a particular meaning for the smoker and for others. In a society in which smoking was universal, or uncommon, the relationship would not hold. But it would likely also not hold or work in the same way in a context in which the social meaning of smoking– the message sent and received by the act of smoking– was different. This problem– that the underlying causal mechanisms themselves vary according to context– limits the generalizability or robustness of models and at the same time reminds us of the importance of the complex but unknown underlying causal mechanisms. This adds a complication of a different character.