In the late 1980s, publications emerged defining mixed methods research across disciplines.
Greene, Caracelli & Graham (1989) identified five reasons for conducting mixed methods studies:
To triangulate the data:
Collecting both quantitative and qualitative data simultaneously.
Comparing and contrasting the findings.
Merging the data.
To complement the data:
Seeking elaboration, enhancement, illustration, and clarification.
Mixing results of one method with another.
To develop the data:
Using results of the first method to develop or inform research using the other method.
To understand paradoxes or contradictions:
Following up on findings that need further explanation.
To expand the data:
Extending the breadth and range of a study.
Using different methods for different components of the study.
Types of Mixed Methods Designs
Main types of mixed methods designs:
Convergent parallel design
Explanatory sequential design
Exploratory sequential design
Embedded design
Convergent Parallel Design
Used to obtain different but complementary data on a topic.
Purpose is to combine the strengths of each type of data.
Both quantitative and qualitative data are collected simultaneously.
Example: A local community health center conducts a healthcare needs study.
Combines questionnaires, in-depth interviews, and focus groups.
Explanatory Sequential Design
A two-phased study.
Phase 1: Collection of quantitative data to address study questions.
Phase 2: Qualitative data is obtained to explain or build on the initial quantitative results.
Example: Study on the retention of older healthcare workers (Hodgkin et al., 2017).
Research Question: ‘What are the organizational and social factors that impact on the retirement intentions of health care workers who are aged 55 years and over?’
Phase 1: Participants (n=299) completed a survey.
Measures: demographic variables, retirement intentions, an effort reward imbalance measure, and a general health measure.
Phase 2: A smaller sample (n=17) participated in in-depth interviews.
Explored retention and retirement intentions to help explain some of the quantitative findings.
Exploratory Sequential Design
Begins with and prioritizes qualitative data.
Results of the qualitative stage are used to develop the quantitative stage.
Based on the premise that exploration of an issue or concept is required first.
Useful in developing theories or concepts when measures or instruments are not available.
Example: Dellemain, Hodgkin & Warburton (2017) used exploratory sequential design to develop a practice theory for rural case management.
The authors argued that although a theory on case management has been developed, there has been little research into the impact of rurality on this type of community work, particularly in the Australian context.
Design selected was a qualitative dominant, sequential, exploratory mixed-method design, the aim of which was to develop community-based rural case management practice theory.
Embedded Design
In health research, one dataset alone cannot fully explain patterns in the data.
Mixed-method approach offsets limitations by adding different types of data for a supportive secondary role.
Example: Study of the workflow and work patterns of Australian residential aged care facilities (Hodgkin, Warburton & Savy, 2012).
Research was concerned with accurately reporting and documenting the activities undertaken by the healthcare workforce (e.g. division 1 nurse, division 2 nurse, allied health practitioner and ward clerk).
Used a structured observation technique over a two-hour period to document each role and the time is taken to do each activity (quantitative data).
Supplemented by qualitative data in the form of structured interviews with key personnel.
Provided crucial contextual data to help explain contextual factors, such as staff shortages, qualifications held by staff, and facility layout affecting tasks.