Big data analytics are prevalent in fields like business, engineering, public health, and the physical sciences, but social scientists are slower than their peers in other fields in adopting this new methodology. One major reason for this is that traditional statistical procedures are typically not suitable for the analysis of large and complex data sets. Although data mining techniques could alleviate this problem, it is often unclear to social science researchers which option is the most suitable one to a particular research problem. The main objective of this paper is to illustrate how the model comparison of two popular ensemble methods, namely, boosting and bagging, could yield an improved explanatory model.