Mixed Methods Research
Mixed methods research combines quantitative and qualitative approaches within a single study or programme of inquiry. Rather than treating the two paradigms as incompatible, mixed methods researchers argue that combining them yields a more complete understanding of the research problem than either approach alone.
Core Designs
Creswell & Plano Clark (2018) identify several primary designs:
| Design | Structure | When to use |
|---|---|---|
| Convergent (concurrent) | QUAN + QUAL collected simultaneously, then merged | When you want different lenses on the same phenomenon at the same time |
| Explanatory sequential | QUAN → QUAL | When quantitative results need qualitative explanation (e.g., why did the treatment group improve?) |
| Exploratory sequential | QUAL → QUAN | When qualitative findings inform instrument development or hypothesis generation |
| Embedded | One strand nested within the other | When a secondary dataset supplements the primary design (e.g., interviews within an experiment) |
Upper-case (QUAN/QUAL) indicates the dominant strand; lower-case indicates the supplementary strand.
In Applied Linguistics
Mixed methods research has grown rapidly in SLA and language teaching since the 2000s. Common applications include:
- Intervention studies: quasi-experimental pre/post-test data combined with learner interviews, stimulated recall, or classroom observation to explain why the treatment worked (or did not)
- Assessment research: statistical analysis of test scores combined with think-aloud protocols to investigate test-taking processes
- Teacher development: surveys establishing patterns across a population, followed by case studies of individual teachers
- Programme evaluation: achievement data plus stakeholder perspectives
Strengths
- Compensates for the weaknesses of each approach: quantitative breadth + qualitative depth
- Triangulation across methods strengthens claims
- Provides both what happened (quantitative) and why/how (qualitative)
- Increasingly valued and expected by journal editors and funding bodies
Challenges
- Requires competence in both paradigms: statistical analysis and qualitative coding
- Time-intensive: two data sets to collect, analyse, and integrate
- Integration is the key challenge; many studies collect both types of data but fail to genuinely merge findings
- Paradigmatic tensions: some argue that positivist and interpretivist assumptions are fundamentally incompatible (the "paradigm wars")
Quality Criteria
Teddlie & Tashakkori (2009) proposed "inference quality" and "inference transferability" as mixed methods equivalents of validity and generalisability. The quality of integration, meaning how well the quantitative and qualitative strands inform each other, is the defining criterion.
Key References
- Creswell & Plano Clark (2018): Designing and Conducting Mixed Methods Research (3rd ed.)
- Teddlie & Tashakkori (2009): Foundations of Mixed Methods Research
- Hashemi & Babaii (2013): mixed methods in applied linguistics
- Riazi & Candlin (2014): mixed methods research in language teaching and testing