In regression (a common statistical practice used in social science research) we often attempt to predict the outcome of a given dependent measure (the DV) based on what we know about other measured variables theoretically related to the DV (the IVs). This common regression method has one problem though: We are predicting values for data that we have already collected. What if we were to engage in actual prediction? That is, what if we attempted to predict the values of a DV that is unknown? How might we do this and what would be the benefit?
This was a fascinating talk presented by Liz Page-Gould of the University of Toronto at the Future of Social Psychology Symposium!