ABSTRACT
This chapter provides a working knowledge of key ideas regarding how it is possible to learn from data. They include linking theory and data; using number-based and word-based data; who should contribute to data creation; using online approaches to research; and how researchers can make broadly based knowledge claims. Data is not self-explanatory, whatever type of data it is. Using data as evidence to support arguments and justify claims is a specific human act—something we deliberately do. One of the biggest decisions a researcher makes is what kind of data to work with. There are generally two types: qualitative and quantitative. The chapter lays out the uses of the two types of data and a few considerations about their use. Purposive sampling can be a hugely helpful tool for researchers. The main caveat is the importance of avoiding confirmatory selection, where people likely to agree with the researcher’s preconceptions or emerging theory are invited to participate in the research.
