Triangulation is a research strategy involving multiple datasets, different methods, theories, and/or investigators to address a research question. Researchers use triangulation within their methodology to enhance the validity and credibility of research findings by corroborating evidence from various sources of data and perspectives.
This approach can help mitigate research biases and provide a more comprehensive understanding of the phenomenon under study, especially when combining quantitative and qualitative approaches. While triangulation is most commonly associated with qualitative research and the collection of qualitative data, it is also frequently applied in quantitative studies.
In mixed methods research, which combines both quantitative and qualitative approaches, the use of multiple data collection methods, known as methodological triangulation, is an inherent component of the research design. The goal is to increase the validity of the findings by gathering and comparing information from diverse perspectives.
Examples: Triangulation in different types of research
Qualitative Research: In qualitative studies, triangulation can be achieved by collecting data from different groups of stakeholders. For instance, in a study exploring the perceptions of a new educational program, a researcher might conduct in-depth interviews with parents, teachers, and children.
Quantitative Research: Triangulation in quantitative research can involve using multiple methods or involving several researchers in the data analysis. For instance, in an eye-tracking experiment, a researcher might employ different analytical techniques to examine the data, such as fixation duration analysis and heat map analysis.
Mixed Methods Research: Triangulation is inherently present through qualitative and quantitative approaches. A typical example of triangulation in mixed methods research is using a quantitative survey followed by qualitative interviews. The survey data can provide a broad overview of the research problem, while the interviews offer deeper insights into participants’ experiences and perceptions.
Types of triangulation in research
Triangulation is a research strategy that involves using multiple sources, methods, theories, or investigators to study a phenomenon. There are four main types of triangulation:
Data triangulation
Data triangulation involves using multiple data sources to study a phenomenon. This includes collecting data from participants, settings, or time points. The goal is to corroborate findings and identify consistencies or inconsistencies across different data sources.
Example: Data triangulation
In a study on the impact of a new healthcare policy, a researcher collects data from patients, healthcare providers, and administrators. By comparing and contrasting the perspectives of these different stakeholders, the researcher can gain a more comprehensive understanding of the policy’s effects and identify potential areas for improvement.
Investigator triangulation
Investigator triangulation involves using multiple researchers to collect, analyze, or interpret data. This can reduce individual biases and provide more diverse perspectives on the research problem.
Example: Investigator triangulation
In a qualitative study on the experiences of first-generation college students, three researchers independently conducted interviews and analyzed the data. The researchers then compared their findings and discussed any discrepancies or insights from their perspectives.
Theory triangulation
Theory triangulation involves using multiple theoretical frameworks to interpret and explain research findings. By examining the data through different theoretical lenses, researchers can develop a more nuanced understanding of the phenomenon and identify potential areas for further investigation.
Example: Theory triangulation
In a study on the factors influencing employee turnover, a researcher analyzes the data using the job demands-resources model and the social exchange theory. By comparing the insights gained from these different theoretical perspectives, the researcher can develop a more comprehensive understanding of the complex factors contributing to employee turnover.
Methodological triangulation
Methodological triangulation involves using multiple research methods to study a phenomenon. This can include combining qualitative and quantitative approaches or using different data collection techniques within a single study.
Example: Methodological triangulation
In a study on the effectiveness of a new educational intervention, a researcher uses a mixed-methods approach. First, the researcher conducts a quantitative survey to assess changes in student performance, and then, the researcher conducts qualitative focus groups to explore students’ experiences and perceptions of the intervention.
What is the purpose of triangulation?
The purpose of triangulation in research is threefold:
To cross-check evidence
Researchers can corroborate their findings using multiple sources, methods, theories, or investigators and ensure consistent results across different approaches. This cross-checking helps to increase confidence in the validity of the research outcomes.
Example: If a researcher finds similar results when analyzing data from surveys, interviews, and observations, they can be more confident that their findings are robust and not merely an artifact of a particular method or source.
For a complete picture
Triangulation allows researchers to gain a more comprehensive understanding of the phenomenon under study. Each source, method, theory, or investigator can provide a different perspective or piece of the puzzle, contributing to a more holistic view of the research problem.
Example: In a study on patient experiences with a new healthcare service, collecting data from patients, healthcare providers, and family members can provide a more complete picture of the service’s strengths, weaknesses, and areas for improvement.
To enhance validity
Triangulation enhances the validity of research findings by reducing the impact of biases or limitations associated with any single approach. By drawing upon multiple perspectives and approaches, researchers can minimize the influence of individual biases, methodological limitations, or theoretical blind spots.
Example: If a researcher relies solely on self-reported survey data, their findings may be subject to social desirability bias. However, by combining survey data with objective measures or observations, the researcher can enhance the validity of their conclusions.
Pros and cons of researcher triangulation
Triangulation is a widely used research strategy with advantages and disadvantages similar to any approach. Here are some of the key pros and cons of triangulation in research:
| Pros | Cons |
| 1. Increased validity: Triangulation enhances the validity of research findings by corroborating evidence from multiple sources, methods, theories, or investigators. | 1. Time and resource intensive: Triangulation often requires more time, effort, and resources than a single approach. |
| 2. Comprehensive understanding: Triangulation allows researchers to gain a more holistic and nuanced understanding of the phenomenon under study. | 2. Complexity: Triangulation can add complexity to the research process, as researchers need to integrate and synthesize findings from different approaches. |
| 3. Reduced biases: Triangulation helps to reduce the impact of individual biases or limitations associated with any single approach. | 3. Skill requirements: Triangulation may require researchers to have broader skills and expertise. |
| 4. Increased confidence: When findings are consistent across different approaches or sources, researchers can have greater confidence in the robustness and trustworthiness of their conclusions. | 4. Potential for conflicting results: Triangulation may reveal inconsistencies or contradictions between different sources or methods, which can be challenging to resolve. |