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Triangulation in Research – Types, Methods and Guide

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Triangulation is a vital approach in research, used to enhance the credibility and validity of findings by combining multiple methods, data sources, theories, or investigators. It allows researchers to cross-verify information, identify discrepancies, and provide a more comprehensive understanding of the phenomenon under study. This article explores the concept of triangulation, its types, methods, and a practical guide to implementing it effectively.

Triangulation

Triangulation in research refers to the practice of using multiple approaches to study a single phenomenon. By leveraging different perspectives, it minimizes biases, reduces the likelihood of errors, and strengthens the reliability of the results. This approach is especially useful in qualitative research, where subjectivity and context play significant roles.

For instance, when studying employee satisfaction, triangulation might involve using surveys (quantitative data), interviews (qualitative data), and observation to verify findings.

Importance of Triangulation in Research

• Enhances Credibility: By validating findings through multiple sources or methods, triangulation ensures that results are trustworthy and accurate.
• Reduces Bias: Combining diverse approaches minimizes the influence of individual biases or limitations inherent in a single method.
• Provides Comprehensive Insights: Triangulation captures the complexity of a phenomenon by integrating varied perspectives.
• Improves Rigor: It strengthens the methodological framework, making research more robust and defensible.

Types of Triangulation

1. data triangulation.

This type involves using different data sources to examine the same phenomenon.

• Example: Collecting data from multiple demographics, such as different age groups or geographic regions, to study consumer preferences.
• Purpose: To ensure findings are not dependent on a single source or sample.

2. Methodological Triangulation

Here, researchers use multiple research methods to explore the same research question.

• Example: Combining surveys (quantitative) and focus groups (qualitative) to study employee engagement.
• Purpose: To validate results by comparing findings across different methods.

3. Investigator Triangulation

This approach involves multiple researchers or observers analyzing the same data or phenomenon.

• Example: Two psychologists analyzing the same interview transcripts to ensure consistent interpretation of responses.
• Purpose: To reduce individual biases and increase reliability.

4. Theoretical Triangulation

Different theoretical frameworks or perspectives are applied to interpret the same data.

• Example: Using both behaviorism and constructivism to analyze learning outcomes in education research.
• Purpose: To provide a multi-dimensional understanding of the phenomenon.

5. Environmental Triangulation

This type examines the same phenomenon in different settings or contexts.

• Example: Studying team collaboration in both virtual and in-person environments.
• Purpose: To explore the influence of contextual factors on findings.

Methods for Implementing Triangulation

Identify research objectives.

Clearly define the research questions or objectives. This helps determine the appropriate types and methods of triangulation needed to achieve credible results.

Select Appropriate Data Sources

Use diverse sources to collect data. For example:

• Surveys and interviews for perspectives.
• Archival data for historical context.
• Observations for real-time insights.

Combine Multiple Methods

Integrate quantitative and qualitative methods to cover different aspects of the research question. For example, statistical analysis can reveal patterns, while qualitative methods explore underlying reasons.

Involve Multiple Researchers

Collaborate with co-investigators to analyze data independently and compare findings. This reduces the risk of bias in interpretation.

Employ Multiple Theories

Apply complementary or contrasting theories to interpret data, allowing for a richer understanding of the research problem.

Compare Findings

Analyze and cross-verify data from different methods, sources, or perspectives. Highlight similarities to strengthen conclusions and investigate discrepancies to uncover deeper insights.

Examples of Triangulation in Research

Healthcare research.

Study: Examining patient satisfaction with telehealth services.

• Data Triangulation: Use patient feedback, healthcare provider assessments, and service usage data.
• Methodological Triangulation: Combine surveys, in-depth interviews, and patient case studies.
• Outcome: A well-rounded view of telehealth effectiveness and areas for improvement.

Education Research

Study: Exploring student engagement in online learning environments.

• Theoretical Triangulation: Apply both cognitive load theory and self-determination theory.
• Environmental Triangulation: Analyze engagement in synchronous and asynchronous settings.
• Outcome: Insights into how teaching methods and platforms influence engagement.

Market Research

Study: Understanding consumer behavior toward eco-friendly products.

• Methodological Triangulation: Use surveys to gather preferences, focus groups for detailed opinions, and sales data for actual behavior.
• Investigator Triangulation: Involve marketing analysts and psychologists to interpret findings.
• Outcome: Comprehensive strategies to enhance product appeal.

Advantages of Triangulation

• Improved Validity: Reduces reliance on a single method or perspective, ensuring findings are well-substantiated.
• Deeper Insights: Integrates multiple dimensions of data for richer and more nuanced interpretations.
• Enhanced Reliability: Minimizes errors by verifying data consistency across different approaches.
• Flexibility: Applicable across disciplines and adaptable to various research designs.

Limitations of Triangulation

• Resource-Intensive: Requires additional time, effort, and resources to implement multiple methods or involve multiple investigators.
• Complex Analysis: Combining and interpreting data from diverse sources can be challenging.
• Potential Conflicts: Inconsistent results across methods or theories may complicate conclusions.

Practical Guide to Using Triangulation

• Define the Problem Clearly Ensure the research problem is well-articulated to identify appropriate triangulation strategies.
• Plan for Triangulation Early Incorporate triangulation into the study design to avoid ad-hoc adjustments.
• Choose Complementary Approaches Select methods, theories, or data sources that align with the research objectives and provide diverse insights.
• Document the Process Record how triangulation was implemented, including details about methods, sources, and analysis, to enhance transparency and replicability.
• Interpret Discrepancies Carefully Use inconsistencies to uncover additional layers of understanding rather than dismissing them as errors.
• Report Findings Thoroughly Present how triangulation contributed to the reliability and depth of the study’s conclusions.

Triangulation is a powerful tool for enhancing the credibility, validity, and richness of research findings. By incorporating multiple methods, data sources, theories, or investigators, it addresses potential biases and captures the complexity of the studied phenomenon. While implementing triangulation can be resource-intensive, its benefits in producing robust and reliable research far outweigh its challenges. Whether in healthcare, education, or business, triangulation ensures that research findings are not only accurate but also actionable and insightful.

• Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research . Sage Publications.
• Flick, U. (2018). An Introduction to Qualitative Research . Sage Publications.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods: Integrating Theory and Practice . Sage Publications.
• Denzin, N. K. (1978). The research act: A theoretical introduction to sociological methods. McGraw-Hill .
• Bryman, A. (2016). Social Research Methods . Oxford University Press.

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Triangulation in Qualitative Research: A Comprehensive Guide [2024]

Uncover the power of triangulation in qualitative research. Learn what triangulation in qualitative research is, the 4 types of triangulation, and the 3 main methods of data collection in triangulation. Discover the definition of triangulation, the purpose of data triangulation and the best way to achieve it. Explore examples of studies using triangulation and its limitations. Dive into researcher triangulation and find the best tool for conducting it effectively. Enhance the validity and credibility of your qualitative case study research with our comprehensive guide on triangulation.

If you’re a qualitative researcher, you’re probably used to having a mountain of data from interviews, observations, and documents to make sense of. But how can you be sure that your findings are credible and trustworthy? Your best friend is data triangulation.

In this comprehensive guide, we'll dive deep into the world of triangulation in qualitative research. We'll explore:

What is Triangulation in Qualitative Research?

What are the 3 main methods of data collection in triangulation, how to conduct researcher triangulation in qualitative data analysis.

So grab a cup of coffee, put on your researcher hat, and let's get started!

In qualitative research, triangulation is the method that helps researchers build a strong case for their findings. Just like a detective who gathers evidence from multiple sources to solve a complex mystery, a researcher using triangulation draws upon various data points, methods, and perspectives to paint a more complete picture of the topic at hand.

So, what is triangulation in qualitative research? At its core, triangulation involves using different approaches to study the same research question. By collecting data from diverse sources, employing multiple methods, or even collaborating with other researchers, you can cross-check your findings and ensure that your conclusions are well-supported. This process of looking at your research from different angles helps to enhance the credibility and validity of your results, giving your audience greater confidence in your work.

What are the 4 Types of Triangulation?

In qualitative research, triangulation is a powerful tool for enhancing the credibility and validity of findings. 

It involves using multiple methods, sources, or perspectives to corroborate and validate research results. By combining different types of triangulation, researchers can paint a more comprehensive and accurate picture of the phenomenon under study. 

So, what are the four main types of triangulation used in qualitative research?

• Data Triangulation: This type of triangulation involves using multiple data sources to cross-verify findings. For example, a researcher studying user behavior might collect data through interviews, surveys, and observations. By comparing and contrasting data from these different sources, the researcher can identify consistent patterns and themes, strengthening the validity of their conclusions.
• Investigator Triangulation: Investigator triangulation involves using multiple researchers to independently analyze the same data set. Each researcher brings their unique perspective and expertise to the analysis, helping to minimize individual biases and ensure a more objective interpretation of the data. This approach is particularly useful in large-scale or complex research projects.
• Theory Triangulation Theory triangulation involves analyzing data through the lens of different theoretical frameworks. By considering multiple theories or explanations for a phenomenon, researchers can gain a more nuanced understanding of the data and identify potential gaps or limitations in existing theories. This type of triangulation encourages researchers to think critically about their findings and consider alternative interpretations.
• Methodological Triangulation: Methodological triangulation involves using multiple research methods to study the same phenomenon. For example, a researcher might combine interviews with participant observation or use both qualitative and quantitative methods to analyze data. By employing different methodological approaches, researchers can capture different aspects of the phenomenon and strengthen the validity of their findings.

Each type of triangulation offers unique benefits and can be used strategically to address specific research questions or challenges. However, it's important to note that triangulation is not a magic bullet. It requires careful planning, rigorous execution, and critical reflection to yield meaningful insights.

As you embark on your own qualitative research journey, consider how you might incorporate these different types of triangulation into your study design. By leveraging the power of triangulation, you can enhance the trustworthiness of your findings and contribute to a deeper understanding of the complex phenomena you're investigating.

What is the Purpose of Data Triangulation in Qualitative Research?

The primary purpose of data triangulation is to increase the confidence in your findings. By collecting data from multiple sources, you can:

1. Confirm the accuracy and consistency of your information

2. Identify any potential biases or limitations in individual data sources

3. Uncover new insights or perspectives that you might have missed

4. Strengthen the overall validity and trustworthiness of your research

Limitations of Triangulation

While triangulation is a powerful tool in qualitative research, it's not without its limitations. Here are a few things to keep in mind:

1. It's time-consuming: Collecting and analyzing data from multiple sources and using different methods can be a time-intensive process. Running a single method is time-consuming enough—this requires multiple methods or perspectives stitched together!

2. Conflicting findings can arise: Sometimes, triangulation can lead to conflicting or inconsistent findings, which can be challenging to reconcile and interpret. 

‍ 3. It's not a guarantee of validity: While triangulation can increase the confidence in your findings, it's not a foolproof method for ensuring validity. You still need to exercise critical thinking and judgment when interpreting the results.

Despite these limitations, triangulation remains a valuable tool for enhancing the credibility and trustworthiness of qualitative research. It's all about finding the right balance and using triangulation judiciously to strengthen your findings.

What is an Example of Triangulation in an Experiment?

To make the concept crystal clear, let’s walk through an example of triangulation in qualitative research.

Imagine you're conducting an experimental study on the effectiveness of a new educational software. To get a comprehensive understanding of the intervention's impact, you could use the following triangulation approach:

- Collect quantitative data on student performance using standardized tests

- Conduct focus groups with students to gather their perceptions and experiences of the intervention

- Observe classroom interactions to assess the implementation and reception of the intervention

By triangulating the quantitative and qualitative data, you can gain a more nuanced understanding of the intervention's effectiveness and identify potential factors influencing its success or failure.

When it comes to triangulation in qualitative research, collecting data from multiple sources is key. But what are the three main methods researchers use to gather this data? Let's dive in and explore each one.

• Interviews: Interviews are a staple in qualitative research, and for good reason. They allow researchers to gain deep insights into participants' experiences, opinions, and beliefs. In the context of triangulation, conducting interviews with different stakeholders or experts can provide a well-rounded understanding of the topic at hand.
• Observations: Observing participants in their natural environment can reveal insights that might not come up in an interview setting. This method of data collection is particularly useful for triangulation, as it allows researchers to compare what participants say with what they actually do. For instance, when investigating how users interact with a product, observing them in action can provide a more accurate picture than relying solely on self-reported data. Tools like Hotjar provide screen recordings for digital products that help you observe at scale. 
• Document Analysis: Don't overlook the power of existing documents and artifacts in your triangulation efforts. Analyzing relevant documents, such as industry reports, customer feedback, or journal articles, can provide valuable context and corroborate findings from other data sources. This method is especially handy when dealing with complex or historical topics, where direct observation or interviews may not be feasible.

By using these three methods in combination, researchers can paint a more comprehensive picture of the phenomenon they're studying. But how do you put this into practice? Here are a few tips:

• Plan ahead: Before starting data collection, consider which methods will best address your research questions and how they can complement each other.
• Be systematic: Develop clear protocols for each method to ensure consistency and rigor in your data collection process.
• Iterate as needed: As you collect and analyze data, be open to adapting your approach based on emerging insights or challenges.

Remember, triangulation isn't about conducting research in silos. It's about weaving together different strands of data to create a stronger, more resilient understanding of your research topic. 

Researcher triangulation, also known as investigator triangulation  involves multiple researchers independently analyzing the same data to reduce the individual biases and increase the reliability of findings. Here's a step-by-step guide to conducting researcher triangulation:

1. Assemble your dream team: Bring together a diverse group of researchers with different backgrounds, expertise, and perspectives to analyze the data. The more diverse your team, the more likely you are to uncover new insights and identify potential blind spots.

2. Set the ground rules: Establish a common analytical framework to ensure everyone is on the same page. This includes agreeing on research questions, coding schemes, and analytical procedures.

3. Divide and conquer: Have each researcher independently review and code the data, identifying themes, patterns, and key findings.

4. Compare notes: Bring the team back together to compare and discuss their individual analyses. This is where the magic happens! Identify areas of convergence and divergence, and explore the reasons behind any differences in interpretation.

5. Put the pieces together: Work collaboratively to resolve any discrepancies or disagreements in the findings. The goal is to reach a consensus or, if that's not possible, document the reasons for differing interpretations.

6. Align on your final results: Synthesize the findings from the individual analyses into a cohesive, comprehensive report that reflects the collective insights of the research team.

What are the Best Tools for Researcher Triangulation?

Now that we know the ins and outs of triangulation—what are the right tools to have handy?

Here are the top 3 tools that can help streamline your triangulation process and take your qualitative research to the next level:

1. Looppanel

About the product: Looppanel is a comprehensive research analysis platform designed to make researcher triangulation a seamless and efficient process. With features like real-time collaboration, AI-assisted tagging, and built-in data organization, Looppanel empowers research teams to work together, analyze data, and synthesize findings with ease.

Free Trial: Looppanel offers a 14-day free trial, giving you and your team the opportunity to explore its features and see how it can enhance your researcher triangulation process firsthand.

Pricing : Looppanel's pricing plans start at $30 per month for individual researchers and scale up to meet the needs of larger teams and organizations. They offer flexible plans to accommodate different research requirements and budgets.

Customer Quote:

“It used to take us 2 weeks to analyze a project. Now it takes 2 days.” - Karthik, User Researcher

Rating on G2: Looppanel boasts an impressive 4.7 out of 5 stars on G2, with users praising its user-friendly interface, robust feature set, and exceptional customer support.

About the product: NVivo is a powerful qualitative data analysis software that offers a range of features to support researcher triangulation. With tools for data organization, coding, querying, and visualization, NVivo helps research teams collaborate, analyze data, and uncover insights.

Free Trial: NVivo offers a 14-day free trial, allowing you to explore its features and see how it can support your researcher triangulation process.

Pricing: The starting price for NVivo varies depending on the type of license and subscription model. For a perpetual academic license, the cost ranges from $849 to $1249, while a cloud-based subscription is priced at $99 per user per year.

"Nvivo is the most powerful software for managing and analysing many types of qualitative data, including text, audio, images, or even videos. The best feature for me is the embedded transcription service." - Dr. James K., Research Manager

Rating on G2: NVivo has a strong rating of 4.1 out of 5 stars on G2, with users commending its comprehensive feature set, flexibility, and ability to handle large and complex qualitative datasets.

3. Excel or Miro

Strapped for budget?

Push comes to shove, you can always use good old excel or Miro patched together with other free tools to run data triangulation. This is what your free toolkit might look like:

• Zoom / GMeet to run interviews or focus groups and record them
• Take notes in a Google Doc. You may be able to use their free transcription feature too! If you don’t have access to a transcript, try to get a good note-taker for your moderated sessions.
• Google forms for surveys. These will output the data directly into an excel sheet for you.
• Excel for coding or Miro for affinity mapping. This will allow you to see themes and patterns across calls.
• PPT or Doc for your report. 

Not sure how to run affinity mapping? Check out the ultimate guide here .

Both Looppanel and NVivo offer powerful tools to support researcher triangulation, but they cater to slightly different needs. Looppanel's user-friendly interface and AI-assisted features make it a great choice for teams looking for a more streamlined and intuitive platform, while NVivo's advanced features and flexibility make it well-suited for more complex research projects.

If you’re really strapped for $, excel can always be frankensteined with a number of free tools to get the job done.

Ultimately, the choice between these tools will depend on your team's specific needs, budget, and research goals. Whichever one you choose, you can be confident that you're investing in a platform that will help you take your researcher triangulation to the next level and uncover rich, credible insights.

And there you have it, folks! A comprehensive guide to triangulation in qualitative research. By now, you should be equipped with the knowledge and tools to become a true triangulation detective. Remember, triangulation is all about looking at your research question from multiple angles, using different sources, methods, and perspectives to uncover the truth.

So go forth, my fellow qualitative researchers, and triangulate with confidence! Your research will thank you for it.

Is triangulation the same as mixed methods?

While triangulation and mixed methods research share some similarities, they're not the same thing. Triangulation refers to using multiple data sources, methods, or researchers within a single study to enhance the credibility and validity of the findings. Mixed methods research, on the other hand, involves combining both quantitative and qualitative approaches in a single study or series of studies to gain a more comprehensive understanding of the research problem.

What is investigator triangulation in thematic analysis?

Investigator triangulation in thematic analysis involves having multiple researchers independently analyzing the same qualitative data, identifying themes and patterns. The researchers then compare their findings, looking for areas of agreement and disagreement. This process helps to reduce individual biases and increase the reliability of the thematic analysis.

What is the difference between data triangulation and method triangulation?

Data triangulation involves collecting data from multiple sources (like interviews, observations, and documents) to cross-verify findings and identify consistencies or discrepancies. Method triangulation, on the other hand, involves using multiple data collection methods (such as surveys, interviews, and focus groups) to gather information and compare the results. While both types of triangulation aim to enhance the credibility and validity of the findings, they differ in their focus on the sources of data (data triangulation) or the methods used to collect the data (method triangulation).

How do you use  Triangulation for your Qualitative Research Case Study?

When creating a qualitative research case study, triangulation can help ensure the robustness and credibility of your findings. 

By collecting data from multiple sources (like interviews, observations, and documents), using different methods (such as surveys or focus groups), and even involving multiple researchers in the analysis process, you can strengthen the validity of your case study findings and draw more reliable conclusions.

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• Roberta Heale 1 ,
• Dorothy Forbes 2
• 1 School of Nursing, Laurentian University , Sudbury, Ontario , Canada
• 2 Faculty of Nursing , University of Alberta , Edmonton, Alberta , Canada
• Correspondence to : Roberta Heale School of Nursing, Laurentian University, Sudbury, ON, Canada P3E2C6; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2013-101494

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The term ‘triangulation’ originates in the field of navigation where a location is determined by using the angles from two known points. 1 Triangulation in research is the use of more than one approach to researching a question. The objective is to increase confidence in the findings through the confirmation of a proposition using two or more independent measures. 2 The combination of findings from two or more rigorous approaches provides a more comprehensive picture of the results than either approach could do alone. 3

Methodological triangulation is the most common type of triangulation. 2 Studies that use triangulation may include two or more sets of data collection using the same methodology, such as from qualitative data sources. Alternatively, the study may use two different data collection methods as with qualitative and quantitative. 4 “This can allow the limitations from each method to be transcended by comparing findings from different perspectives….” 4

Triangulation is often used to describe research where two or more methods are used, known as mixed methods. Combining both quantitative and qualitative methods to answer a specific research question may result in one of the following three outcomes: (1) the results may converge and lead to the same conclusions; (2) the results may relate to different objects or phenomena but may be complementary to each other and used to supplement the individual results and (3) the results may be divergent or contradictory . Converging results aim to increase the validity through verification; complementary results highlight different aspects of the phenomenon or illustrate different phenomenon and divergent findings can lead to new and better explanations for the phenomenon under investigation. 3

Examples of triangulation, or mixed methods, are as varied as there are research studies. Nurses’ attitudes about teamwork may be collected through a survey and focus group discussion. A study to explore the reduction of blood pressure through a nutritional education programme may include a review of participant adherence to the diet changes through daily logs along with a series of BP readings. In every case, the researchers link and compare different methods related to a single research question.

Although regarded as a means to add richness and depth to a research inquiry, there are several criticisms of the use of triangulation in research. Triangulation assumes that the data from two distinct research methods are comparable and may or may not be of equal weight in the research inquiry. In addition, when two or more data sets have convergent findings, there must be caution in interpretation since it may simply mean that each of the data sets is flawed. Others 3 question whether the term triangulation has any meaning when it is so broadly defined, mixed methods is preferred. In spite of these criticisms, triangulation is generally considered to promote a more comprehensive understanding of the phenomenon under study and to enhance the rigour of a research study.

• ↵ The Institute of Navigation . (n.d.). Getting to the point. http://www.ion.org/satdiv/education/lesson6.pdf
• Tashakkori A ,
• Williamson GR

Competing interests None.

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Triangulation In Research: The Basics

Data, methodology, investigator and theoretical triangulation

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | August 2024

Subjectivity and bias are two sneaky culprits you need to watch out for whenever you’re undertaking research. Thankfully, triangulation is one powerful weapon you can use to fend off these little monsters. In this post, we’ll unpack triangulation in simple terms.

Research Triangulation 101

• What is triangulation in research?
• Data triangulation
• Methodological triangulation
• Researcher triangulation
• Theoretical triangulation
• Key takeaways

What (exactly) is triangulation?

Despite the fancy name, triangulation simply means using multiple methods , data sources, or even researchers to enhance the credibility of a study’s findings . In other words, to reduce the impact of subjectivity and bias.

The underlying idea is that by approaching the research question from multiple angles , you, as the researcher, can gain a more holistic view of the situation . In other words, triangulation helps ensure your results aren’t skewed by a single method, source, or perspective.

As we alluded to, there are a few different types of triangulation at your disposal. Typically, triangulation methods fall into one of four categories:

So, let’s unpack each of these to understand the options you have at your disposal.

What is data triangulation?

As the name suggests, this approach involves using different sources of data within one study . For example, if you were researching people’s opinions about a political event, you might collect data at different times, from different places, or from different groups of people.

Let’s look at a practical example.

Suppose you’re investigating public opinions about a political event. Instead of relying on a single source of data, like a survey conducted at one moment in time – you might broaden your scope. For example, you could collect data from different locations, at various times, or even from different groups of people. By doing so, you’re not just capturing a snapshot of opinions but rather building a richer, more nuanced picture that reflects how perspectives might change over time or differ between communities.

The key benefit of data triangulation is that it allows you to develop a more well-rounded and holistic perspective . This is especially valuable when researching complex social issues, where opinions and experiences can vary widely depending on factors like geography, demographics, or time. By integrating multiple data sources, you can cross-validate your findings, reduce the impact of biases, and ultimately enhance the credibility and depth of your research.

Need a helping hand?

What is methodological triangulation?

As the name suggests, this approach involves using multiple methods to collect and analyse data .The idea here is to leverage the strengths of different methods while offsetting their individual weaknesses, ultimately leading to a more robust and comprehensive understanding of the research topic.

 Let’s look at a practical example.

Imagine you’re studying the impact of a new educational policy. Instead of relying solely on interviews with teachers (a primary data source), you might also analyse existing policy documents or academic studies on the topic (secondary data sources). Additionally, you could combine qualitative methods, such as focus groups with quantitative methods, like surveys or statistical analysis. 

The core idea behind methodological triangulation is that no single method can capture all aspects of a complex issue . Each method has its own set of strengths and limitations. Therefore, by using multiple methods, you can cross-check your results, fill in gaps that one method might leave, and achieve a more balanced and well-rounded view of the subject matter. 

What is investigator triangulation?

Investigator triangulation, also known as researcher triangulation, is an approach that involves multiple researchers in the data collection and interpretation process. The primary goal here is to reduce the influence of individual bias and enhance the overall credibility of the research findings.

In practical terms, investigator triangulation allows each researcher to bring their own perspective, expertise, and interpretation to the table, which can significantly enrich the analysis. For instance, while one researcher might focus on certain patterns in the data, another might pick up on different nuances or trends that could otherwise be overlooked.

As you can probably guess, the collaborative approach inherent to investigator triangulation not only helps in cross-checking findings but also in uncovering different angles and insights that a single researcher might miss. In essence, investigator triangulation reinforces the idea that “two heads are better than one.” 

What is theoretical triangulation?

Intimadating names aside, theoretical triangulation simply means using multiple theories or theoretical frameworks to interpret the same data set. This method allows you to view your findings from different theoretical angles , which naturally deepens your analysis.

Let’s consider an example.

Imagine you’re studying student motivation at a local college. Instead of relying on just one theory to explain your data, you could apply both Self-Determination Theory (SDT) and Expectancy-Value Theory (EVT).

At a basic level, SDT examines the balance between intrinsic and extrinsic motivations—how students are driven by internal desires versus external rewards. On the other hand, EVT focuses on how students’ expectations of success and the value they place on a task influence their motivation. Therefore, by using both of these theories, you can explore student motivation from two different perspectives, which might reveal insights that one theory alone could not provide.

If this sounds a bit abstract, don’t worry! The key takeaway here is that theoretical triangulation allows you to apply different lenses to the same data , leading to a more comprehensive and nuanced understanding of the phenomena you’re studying. This approach is particularly useful in complex research areas where no single theory can fully explain the observed outcomes.

Bringing it all together…

To recap, the four types of triangulation we’ve looked at are:

While each of these triangulation methods is useful on its own, it’s even better to combine them . Of course, this is quite a time-consuming undertaking, but doing so can help you significantly reduce the level of subjectivity and bias within your analysis. So, be sure to carefully consider your options when designing your study.

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Chapter 28: Triangulation

Tess Tsindos

Learning outcomes

Upon completion of this chapter, you should be able to:

• Understand the definition of triangulation.
• Describe the four types of triangulation.
• Understand how to conduct triangulation.
• Identify the strengths and limitations of triangulation.

What is triangulation?

Triangulation is the combination or blending of more than one participant group, researcher, theory and/or method in the same research. Its purpose is to understand the phenomenon under study 1 by determining consistency, or ‘truth’. 1 Triangulation can be used to demonstrate the rigour, validity and credibility of research findings. 2 While the purpose of triangulation is not to confirm results, but rather to understand differences, it can be difficult to explain inconsistent results when discussing the research undertaken.

There are four main types of triangulation 2 :

• T heoretical triangulation is the use of more than one theory to guide the research process. For example, a researcher might analyse data on family violence by applying feminist and critical theory, and they might also apply structural functionalist theory (see Section 1) when examining family violence as part of a complex system. By applying different theories, the data is able to be interrogated through theoretical lenses, which can lead to deeper understanding of the findings and greater nuance than a single theory might support.
• R esearcher triangulation is the use of multiple (two or more) researchers to collect and / or analyse data. The researchers may have different disciplinary backgrounds and experiences, and will also bring their professional and personal interpretations to the data. For example, research approaches to consumer and community involvement (or patient and public involvement) might advocate for patients to be involved in the analysis of data, to include patient perspectives in the interpretation of the data. In a study developing a ‘BroSupPORT’ portal and examining issues facing men with prostate cancer, 3 researchers found that health professionals were not sure that a Patient Reported Outcome comparator tool would be helpful in prompting health-seeking behaviour, but participants with prostate cancer welcomed such a tool. Focusing a patient lens on data in this study was important because it was able to highlight differences between perspectives of health professionals and patient participants. If only health professionals had been consulted the tool would not have been considered helpful and would have been ruled out as an option for the portal.
• Methodological triangulation is the use of multiple (two or more) methods to collect and analyse data . The data collection methods might include focus groups, interviews, photovoice, observations, field notes and more. In essence, it is bringing together the various methods used to collect data and can provide a more nuanced explanation of results. Methodological triangulation can include quantitative methods to support or harmonise results. Using quantitative and qualitative methods together enables the research to answer the questions of ‘what’ and ‘why’ (see Chapter 11: Mixed Methods). The BroSupPort portal study 3 is a good example of methodological triangulation because it used a combination of workshops, interviews and focus groups to collect data.
• Data triangulation uses more than one data source and / or method of analysis to interrogate the data. Data sources may include interviews with people in a range of roles in an organisation, rather than only those in one particular role. Data analyses might include data from both inductive and deductive perspectives. Data triangulation might also include different data sources, such as qualitative (e.g. interviews) and quantitative (e.g. surveys). In the BroSupPORT portal study 3 data were gathered at workshops, focus groups and interviews. Surveys, mind maps, River of Life activities and problem trees (in printed form), along with field notes taken at each workshop, were used to collect data. A range of techniques was used to analyse the data including, but not limited to, descriptive content analysis.

Table 28.1 provides examples of the four main types of triangulation. Other types of triangulation, such as ‘time’ and ‘space’ 3 , are not covered in this chapter because they are used less often.

Table 28.1: Examples of triangulation

How to conduct triangulation.

How triangulation is conducted depends on the type of triangulation.

• Theoretical triangulation requires an introduction to each theory and can be written as a literature review. The theories are described and then compared, to elicit inferences that will form the basis of data interpretation. For example, a feminist theory will inform data collection in such a way that girls and women (and women’s marginalised groups) will be deliberately sought out and included in the research study. Analysis would include a focus on gender identity, patriarchal oppression, diversity of culture and background, and would seek to demonstrate women’s points of view through a feminist lens. If, for example, a study is about women patients, the data collection and analysis would focus on how or whether women are represented in the data, and how women are medically treated by healthcare practitioners. Women’s own perspectives would be sought and analysed, to understand their perspectives.
• Researcher triangulation is often described in the type of data being analysed, and can often be read in the researcher’s statement of positionality or in the reflexivity section of a journal paper or report 9 . Some forms of thematic analysis (not reflexive thematic analysis) requires more than one investigator to read, re-read, code and re-code interviews or focus groups. When it is not a requirement of the method of analysis, triangulation should still be considered, in order to address concerns about the rigour, validity and credibility of findings of a single researcher. Including more than one researcher and participant can leads to greater divergence and the potential for nuanced findings.
• Methodological triangulation is used often in the literature. A decision is made about how to conduct the research, on the basis of the research question or aim. Often in mixed methods research, a qualitative component seeks to answer the question, ‘Why?’ and the quantitative component seeks to test a hypothesis or answer the question, ‘What?’. However, many qualitative methods might be included, such as interviews, focus groups, newspaper clippings, to answer the research question(s). When using methodological triangulation, the researcher is looking to expand their understanding of the findings. For example, if a survey and interviews are the mixed methods used in a study, the researcher would seek to compare and contrast the findings of both methods, to gain a comprehensive understanding of the phenomenon, and then would describe how the findings support or diverge in answering the research question(s). Thus, a study exploring barriers and enablers in the implementation of the 6-PACK falls prevention program 10 incorporated a cluster randomised control trial, economic and program evaluations, and surveys and focus groups. The findings were triangulated and results suggested that regular, practical face-to-face education and training for nurses were key to successful falls prevention program implementation in acute hospitals, as were provision of equipment; audit, reminders and feedback; leadership and champions; and the provision of falls data .
• Data triangulation involves using and analysing more than one participant group. It is often considered an aspect of methodological triangulation because different methods usually involve more than one source of data. Data collection needs to be well-defined and conducted. Once the data from all participant groups has been examined, the findings are compared and contrasted to assist in answering the research question(s).

It’s important to remember that triangulation can involve more than one type of triangulation, and this is often the case with mixed-methods research. For example, in mixed-methods research, methodological, investigator and data triangulation may be used to demonstrate the full findings of the research. While Table 28.1 has listed each type separately, examining some of the example papers will show that there is more than one type of triangulation in the studies. Strict adherence to only one triangulation type can make researching the phenomenon more difficult.

Advantages and challenges of triangulation

Comparing and contrasting theories, data sources, methods and data analyses can ensure strong reliability and validity in research results. However, this can also be time-consuming and resource-intensive. Attention needs to be paid to the nuances of the research, to provide holistic explanations. There are times when triangulation may not be considered necessary, and this also needs to be understood when addressing the research question. For example, if the purpose of the research is to develop a new theory, there may be no need to include more than one method, data point or theoretical foundation.

Triangulation is the use of more than one data source, investigator, theory or method in the same research. There are four main triangulation types: each provides a means for examining the research from different perspectives and for ensuring the rigour, validity and credibility of findings.

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Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Tess Tsindos is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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