Description
      Explanatory models are designed for testing
      hypotheses that specify how and why certain
      empirical phenomena occur. Predictive models are
      aimed at predicting new observations with high
      accuracy. An age-old debate in philosophy of
      science deals with the difference between
      predictive and explanatory goals. In mainstream
      statistical research, however, the distinction
      between explanatory and predictive modeling is
      often overlooked, and there is a near-exclusive
      focus on explanatory methodology. This focus has
      permeated into empirical research in many fields
      such as information systems, economics and, in
      general, the social sciences. We investigate the
      issue from a statistical modeling perspective. Our
      premise is that (1) both explanatory and predictive
      statistical models are essential for advancing
      scientific research; and (2) the different goals
      lead to key differences at each step of the
      modeling process.
  
      In this RIT we will explore and discuss the differences between
      explanatory and predictive modeling. We will analyze each step of the
      statistical modeling process (from problem formulation and data
      collection to model use and reporting). Students will read and
      present relevant papers. Each student will work with a real dataset
      of his/her choice to study one or more of these differences.


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