Mixed methods research combines quantitative and qualitative methods within a single study in order to develop a more comprehensive understanding of complex research questions. There are several ways the two approaches can be integrated within a mixed methods study design, with the choice influenced by the aim of the study, the research questions, the underlying research methodology, and practical considerations such as the timing of data collection, the relative priority given to quantitative or qualitative components, and resource availability.
This page outlines the key features of four of the most commonly used mixed methods study designs.
Triangulation design involves collecting and analysing qualitative and quantitative data separately but during the same phase of the study, and then integrating the findings to compare or confirm findings. This type of study design is useful when you want to gain a more complete understanding of a research problem by examining it from different perspectives, or when you want to validate or expand insights using both qualitative and quantitative data.
Seminal authors: Creswell, Tashakkori, Teddlie, Denzin
Data collection timing: qualitative and quantitative data are collected at the same time
Common data analysis and integration methods: data are analysed separately and then integrated by comparing, contrasting, or merging results during interpretation
Example thesis: Process Oriented Guided Inquiry Learning in Australian secondary science classrooms
Embedded design involves incorporating one type of data within a larger primary research design that uses another method. The secondary dataset is used to address a supplementary question or provide additional insight that supports the main study. This type of study design is useful when a secondary method can help explain, support, or enhance the findings from the primary method.
Seminal authors: Creswell, Plano Clark, Greene, Yin
Data collection timing: data collection is centred on the primary design, while the secondary data may be collected before, during, or after the main phase of the study to support it
Common data analysis and integration methods: datasets are typically analysed separately, with the secondary data integrated during interpretation to enrich, explain, or contextualise the primary findings
Example thesis: A Mixed Method Investigation of Injuries Associated with Artisanal and Small-Scale Mining in Migori County, Kenya
Explanatory design involves first collecting and analysing quantitative data, followed by qualitative data to help explain or provide deeper insight into the quantitative results. The qualitative phase is designed to explore patterns, relationships, or unexpected findings that emerged from the quantitative analysis. This type of study design is useful when you want to better understand the reasons behind quantitative results or add depth and context to statistical findings.
Seminal authors: Creswell, Plano Clark, Tashakkori, Teddlie
Data collection timing: quantitative data is collected first, followed by qualitative data to explain or expand on the findings
Common data analysis and integration methods: quantitative data is analysed first, then qualitative analysis is used to interpret or give meaning to the results
Example thesis: A Mixed Method Investigation of Injuries Associated with Artisanal and Small-Scale Mining in Migori County, Kenya
Exploratory design involves collecting and analysing qualitative data first to explore a topic, followed by quantitative data to test, generalise, or further investigate the findings. This type of study design is useful when little is known about a topic, or when you want to develop a theory, framework, or research tool before testing it with a larger quantitative phase.
Seminal authors: Creswell, Plano Clark, Tashakkori, Teddlie
Data collection timing: qualitative data is collected first, then the findings are used to inform the design of the quantitative phase
Common data analysis and integration methods: qualitative data is analysed first to identify themes or concepts, which then guide the quantitative analysis
Example thesis: Factors associated with high turnover of Jordanian physicians in rural areas: a sequential exploratory mixed method study
If you would like to consolidate your understanding of the mixed methods study designs outlined above, you may find the following activity helpful: