This volume has its origins in the rapid and staggering changes occurring in computational social research. As one of the editors of Political Analysis (an academic journal that publishes research articles in political methodology) and Analytical Methods for Social Research (a book series), I know I am witnessing amajor shift in social science research methodology. Researchers have vast (and complex) arrays of data to work with; we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly.

When I was in graduate school in the late 1980s and early 1990s, those of us who worked with survey and public opinion polling data were considered “big-N” researchers in the social sciences. When I teach introductory research methods in my graduate seminars at Caltech, I will often have students read the 1978 American Political Science Review paper by Steven J. Rosenstone and Raymond E. Wolfinger, “The Effect of Registration Laws on Voter Turnout.” Today this paper seems straightforward to students: Rosenstone and Wolfinger simply collected information on state-by-state voter registration and administrative practices, and merged that with the November 1972 U.S. Census Bureau's Current Population voting supplement, which the authors report as having more than 93,000 respondents.They then tested, using a relatively simple binary probit model, for the effects of various registration and election administration procedures on whether the survey respondent reported having voted in the 1972 federal general elections.

Most students of statistics, methodology, or econometrics today are familiar with the binary probit model and its near-cousin, binary logit. These are techniques that model the probability that an outcome is met (here, did a voter turn out in an election) based on the covariates or regressors on the right-hand side of the model. The parameters of the probit and logit model are typically fit via maximum-likelihood optimization. Today a student could use an off-the-shelf statistics software package and replicate the original Rosenstone-Wolfinger analysis, literally in the blink of an eye, on his or her laptop computer.