Big data methods in the social sciences
Big data methods are attractive to the social sciences and all the sciences because they can address data sets where the number of variables can far exceed the number of cases being analyzed. Generally speaking, big data methods seek to detect stable and potentially complex clusters and/or predictions in the data, while also taking aggressive steps not to capitalize on chance in doing so. On this latter point, big data methods often perturb some combination of the sample, the variables, and the model to ensure that solutions are robust, where traditional statistical methods rarely do so, with some exceptions (e.g. cross validation approaches, latent variable models)
Current Opinion in Behavioral Sciences