What is data triangulation in qualitative research?

What is data triangulation in qualitative research?

• A. Use multiple data sources to corroborate findings
• B. Use a single data source to strengthen consistency
• C. Randomize data sources to prevent bias
• D. Focus on triangulating measurement instruments

Answer: (A)

Data triangulation means collecting information from multiple sources to see if findings converge. By checking whether themes and insights appear across different kinds of evidence—like interviews, observations, and documents—you gain stronger confidence that the results reflect reality rather than a quirk of a single source. For example, if people’s experiences discussed in interviews align with what you observe in the field and what’s written in policy documents, it supports the validity of those findings. Using multiple data sources helps mitigate biases associated with any one source and can reveal different facets of the issue, enriching interpretation. If you only relied on one source, you’d risk drawing conclusions that aren’t transferable or robust. The other options describe different ideas: relying on a single data source isn’t triangulation; randomizing data sources isn’t how triangulation works; and focusing on triangulating measurement instruments points to instrument or methodological triangulation, not data triangulation.

Data triangulation means collecting information from multiple sources to see if findings converge. By checking whether themes and insights appear across different kinds of evidence—like interviews, observations, and documents—you gain stronger confidence that the results reflect reality rather than a quirk of a single source. For example, if people’s experiences discussed in interviews align with what you observe in the field and what’s written in policy documents, it supports the validity of those findings.

Using multiple data sources helps mitigate biases associated with any one source and can reveal different facets of the issue, enriching interpretation. If you only relied on one source, you’d risk drawing conclusions that aren’t transferable or robust. The other options describe different ideas: relying on a single data source isn’t triangulation; randomizing data sources isn’t how triangulation works; and focusing on triangulating measurement instruments points to instrument or methodological triangulation, not data triangulation.