Chapter 10: Mixed Methods Procedures

Positioned as the third major research movement alongside purely quantitative and purely qualitative traditions, mixed methods integrates elements from both perspectives through six defining components: simultaneous or sequential collection of open-ended and closed-ended data, intentional mixing of these datasets, structured mixed methods design procedures, visual or tabular joint displays that present integrated findings, metainferences drawn from combined analysis, and grounding in explicit theoretical or worldview frameworks. Researchers employ mixed methods for multiple justifications including generating more complete understanding of phenomena, explaining unexpected or outlier quantitative results through qualitative exploration, developing or refining measurement instruments based on qualitative insights, strengthening experimental or evaluation designs with participant perspectives, and promoting social change by centering the voices of marginalized populations. The chapter presents three core design structures: convergent design, which collects both data types concurrently, analyzes them independently, then merges results to assess confirmation or contradiction; explanatory sequential design, which begins with quantitative data collection followed by qualitative investigation to elaborate on initial findings; and exploratory sequential design, which starts with qualitative exploration used to develop new instruments or variables subsequently tested through quantitative methods. These foundational designs can be embedded within larger frameworks such as experimental interventions, case studies, participatory-social justice initiatives, or comprehensive program evaluations. Integration occurs through merging, which presents data side by side for comparison, or connecting, where one phase builds directly upon the previous phase's findings. Joint displays serve as critical analytical tools for visualizing integrated data, enabling researchers to derive metainferences that represent overarching insights transcending either data type independently. Design selection depends on research intent, whether comparing existing findings or building new understanding, and practical considerations including researcher capacity, disciplinary conventions, and temporal constraints.