The Role of Mixed Methods in Improved Cookstove Research

Figures & data

Table 1. Methods used to investigate common factors in relation to ICS uptake

	Household air pollution and stove use monitoring	Time activity diary	Survey	Qualitative interviews	Focus groups	Observation
Contextual factors
Household composition		✓	✓			✓
Gender roles		✓	✓	✓	✓	✓
Socioeconomic status			✓	✓		✓
Culture and tradition		✓		✓	✓	✓
Fuel and technology characteristics
Household PM and CO	✓
Personal CO/PM2.5	✓
Stove use	✓	✓	✓	✓	✓	✓
Stove maintenance		✓	✓	✓	✓	✓
Type and quality of biomass	✓	✓	✓	✓	✓
Fuel savings		✓	✓	✓	✓	✓
Stove characteristics			✓	✓	✓
Stove performance	✓		✓	✓	✓
Presence of smoke		✓	✓	✓	✓	✓
User behavior
Stove stacking	✓	✓	✓	✓	✓	✓
Saving time		✓	✓	✓	✓	✓
Gender roles		✓	✓	✓	✓	✓
Willingness to buy			✓	✓	✓
Satisfaction with stove			✓	✓	✓
Health outcomes
Symptoms			✓	✓	✓	✓
Risk of disease outcomes^a	✓
Disease outcomes^b			✓			✓
Burns			✓			✓
Health services use			✓	✓	✓

^aBased on published exposure response functions that describe relation between PM2.5 exposure and relative risk of five disease conditions (Burnett et al., 2014).

^bFor example, reported diagnosed cases.

Table 2. Study design considerations in a mixed-methods study

Study design model	Priority	Implementation	Analysis
Exploratory (also known as instrument design)	Priority qualitative	Sequential with qualitative first	Connecting
Convergent	Equal	Concurrent	Embedding or merging
Transformative	Equal, priority quantitative, or qualitative	Concurrent or sequential	Merging, connecting, or embedding
Explanatory	Priority quantitative	Sequential with quantitative first	Connecting
Nested (also known as embedded)	Priority quantitative or qualitative	Concurrent or sequential	Embedding

Note. Adapted from Creswell and colleagues (2004) and Creswell and Clark (2011).

Table 3. Examples of case units in an improved cookstove mixed-methods study

Case unit	Focus of study aim
Household	To identify trends in individual households over time and with different stoves
Maximum/minimum PM reductions	To identify contextual factors associated with maximum and minimum reduction in PM2.5
Stoves	To identify contextual factors that differ between stoves
Specific stove: maximum and minimum reductions	To identify maximum/minimum reductions associated with individual stove use
Focus on an identified theme	To explore specific themes identified in the analysis

Table 4. Triangulation protocol using a converging coding matrix

1. Identify cases (households with greatest and least reduction in PM2.5).

2. Data extraction: for each household, list together qualitative and quantitative findings that address the research question(s) of interest.

3. Convergence coding: develop a convergence coding matrix based on the specific research questions. The themes form the rows of the matrix, and the different levels of convergence, the columns.

4. Convergence assessment: for each case, complete the matrix, which sorts findings from each data source into relevant segments that address the research question. Identify whether findings suggest convergence, complementarity, discrepancy, or dissonance.

5. Researcher comparison: multiple researchers review each case and clarify findings. Explicitly looking for evidence of dissonance between findings is a key part of the process. This could be dissonance between cases, or between different data sources.

6. Group comparison: compare findings between the two groups of cases (high PM2.5 reduction versus low PM2.5 reduction).

7. Feedback: feedback of results to research team and stakeholders for review and clarification.

8. Identify further research questions: consider further triangulation by returning to larger data set and/or examining other case groups within the study; or additional data sources (in this example, focus group findings).

Note. Adapted from Farmer and colleagues (2006).

Creswell, J. W. & Clark, V. L. P. (2011). Designing and conducting mixed methods research. Thousand Oaks, CA: Sage.

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Farmer, T., Robinson, K., Elliott, S. J. & Eyles, J. (2006). Developing and implementing a triangulation protocol for qualitative health research. Qualitative Health Research, 16, 377–394.

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