Mixed methods research involves more than just aggregating data from separate qualitative and quantitative studies. Researchers collect data within a single study, either sequentially or concurrently, and combine the data throughout the research process. Examples of data collected include structured observations, interviews, surveys and government statistics.
Mixed methods research works well under certain types of conditions, such as when data collected from different sources complement each other; when quantitative or qualitative research alone cannot answer the questions; or when data from one method helps researchers understand data collected from another. Mixed methods research doesn't replace qualitative or quantitative research, but ideally maximizes the strengths and minimize the weaknesses of each type.
Even with the additional challenges, mixed methods research still offers benefits. Qualitative and quantitative data often complement each other, and this type of research addresses a broader range of questions. Also, the strengths of one method sometimes compensate for the weaknesses of another, and mixed methods research provides insights that don't appear when researchers use a single method.
Because mixed methods research involves procedures still under development, and multiple types of data collection and analysis, it presents certain challenges. Researchers may not be familiar with both qualitative and quantitative research, so a team of researchers may be required. Also, the different methods need to be combined appropriately, which requires more time and money.
Mixed methods research designs fall under two broad categories: sequential and concurrent. With sequential designs, researchers collect and analyze data using one method before moving onto the next. With concurrent designs, collection and analysis of multiple types of data occurs in parallel. Subcategories exist based upon the type of theory used, with priority given to the data collected and when the data analysis and integration occurs.