ABSTRACT
When we hear ‘mixed methods’, we typically think of studies that combine quantitative and qualitative data. In this chapter, we put forward the idea that mixed methods are about mixing and combining various types of techniques, analyses, perspectives, data collection methods and, most of all, a variety of different types of data including much more than the qualitative/quantitative divide. We start by discussing what mixed methods means and then, using two research projects, illustrate the strengths enjoyed and the challenges encountered when mixing methods and data types. Both projects made use of large datasets and longitudinal studies, containing a mix of quantitative and qualitative data to describe and understand their subjects of study. Practical considerations placed limits on collecting primary data, making it essential to bring into the mix data collected outside of the project – by our definition, secondary data. With research in the real world, what works for one project will not necessarily work for another project, and so we have presented the projects as examples of actual practice and challenges faced rather than axioms of good practice.
