We begin with a case study of a traditional small-scale face memory experiment. The chapter deconstructs the elements that prevent this experiment from being generalizable, and poses potential ways in which we can expand this experiment into a "Big Data" experiment. The chapter discusses the replication crisis in psychology as an important motivator for broadening psychological experiments, as well as common issues with data fishing or "p-hacking." At the same time, there are limitations to our ability to run perfect Big Data experiments. The chapter describes the beneficial relationship of hypothesis-driven research and data-driven research in psychology, presenting the pros and cons of each. Finally, the chapter discusses different formats of Big Data – wide, deep, and long data.

Network studies follow an explicit form, from framing questions and gathering data, to processing those data and drawing conclusions. And data processing leads to new questions, leading to new data and so forth. Network studies follow a repeating lifecycle. Yet along the way, many different choices will confront the researcher, who must be mindful of the choices they are making with their data and the choices of tools and techniques they are using to study their data. In this chapter, we describe how studies of networks begin and proceed, the life-cycle of a network study