What is triangulation in research? (Importance explained)
By Indeed Editorial Team
Published 25 April 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
To enhance the validity of their findings, researchers employ a variety of techniques. Triangulation remains a common method used by researchers to undertake more credible studies and verify their authenticity. Learning about triangulation in data analysis can help you apply this method to improve the integrity of your own studies and professional responsibilities. In this article, we define the use of triangulation in research, explain why it's important, what its benefits are and list the types of triangulation you may come across when working or performing academic research.
What is triangulation in research?
Triangulation in research refers to the practice of using several research methodologies and viewpoints to explore a single issue. By doing this, the researcher enhances their theories and uncovers new ways of analysing or interpreting a certain event or action. Researchers use triangulation to validate and ensure the accuracy of their findings. It also aids in the prevention of confirmation bias, which happens when individuals manufacture evidence for a perspective they already believe.
Researchers frequently use triangulation to challenge and demonstrate their thorough review and analysis. Through its many methodologies and viewpoints, this strategy increases the validity of a study by checking for flaws in interpretation or assessment.
What is the purpose of triangulation in research?
For detailed analysis, it's critical to collect high-quality data. When you obtain information from one source or inquiry, determining if the data appears reliable quickly becomes challenging. If data from numerous resources or investigators coincide, you're more able to trust its accuracy. You incur bias in your study if you depend on just one data investigator. When there's only one scholar collecting data, selection bias may emerge. Similarly, relying solely on one approach puts you at a disadvantage due to the method's inherent weaknesses and limits.
What is the importance of triangulation?
In research, triangulation remains significant since it helps researchers examine and confirm their findings by evaluating their ideas alongside those of others. Triangulation remains important in research since it helps to increase a study's validity, particularly in quantitative investigations. Academics and researchers use triangulation to corroborate the findings of a study on the efficiency of medical treatments, for instance. You can achieve this by performing your own research and comparing the results to those of a third-party study.
Benefits of triangulation
The majority of sociological methodologies include biases you cannot entirely eliminate. People responding to a telephone survey, for instance, respond differently than those responding to a postal or in-person survey. These sorts of biases lead to anomalous results. While the investigator speculates about what caused an uncommon result, they hold no means of knowing if the result was abnormal based on a single source of data. It's significantly less probable that all three sets of findings reveal the same anomalous data since three distinct approaches correct each other's biases.
Triangulation help researchers by providing a range of datasets to understand various elements of a topic. It also aids in the refutation of cases where one dataset invalidates a hypothesis established by another. It helps with hypothesis confirmation when one set of findings supports another. Finally, triangulation aids in the interpretation of research findings.
Different types of triangulation
Triangulation refers to the process of adopting more than one way to collect data on the same topic. It's also a manner of ensuring the validity of research by using a variety of approaches to gather data on the same issue, which involves numerous types of samples. The different types of triangulation include the following:
Triangulation of method style
When you apply methodological triangulation, you employ several methodologies to tackle the same research subject. This remains the most prevalent type of triangulation, and it occurs when researchers combine qualitative and quantitative research approaches in one investigation or task. Methodological triangulation remains an advantageous type of triangulation because it eliminates the biases and limitations associated with relying on a single research approach.
For instance, you might integrate behavioural, survey and cognitive data into your research to create a holistic picture of what inspires individuals to cooperate. Another example may involve recruiting people to play team games in a behaviourally controlled lab experiment and keeping track of their results. You might also conduct a poll to get information on how they cooperate in their daily lives before using fMRI scans to evaluate the neurological underpinnings of collaboration.
Triangulation of investigation
Multiple researchers find themselves involved in investigator triangulation to gather, process or evaluate data separately. Typically, you might use several observers to code the activities of your participants for your behavioural data. You give them training courses and a handbook to follow so they can code behaviours in the same way to ensure accuracy.
In the same example as above, your participants may examine and write down any examples of cooperative behaviour they see in video footage of people playing team games. You double-check that their coding sheets match up to guarantee accessibility. Your observers then also re-calibrate how they code behaviours to ensure accuracy and significantly reduce experimental biases.
Triangulation of data
The technique of verifying data by comparing and contrasting results from many sources goes by the name of data triangulation. Researchers and academics use data triangulation to double-check their findings and confirm that they back up their theory. In qualitative research, data triangulation remains a typical approach that entails validating the data from those who gathered it and those who evaluated it. Other experts who specialise in a similar discipline or specialists in a separate field entirely can check and verify the findings.
Triangulation of theory
The term triangulation of theory refers to the use of various theoretical frameworks to investigate a certain occurrence or topic. A researcher, for instance, might use two distinct psychoanalytic theories to describe the same occurrence, such as cooperative and competitive dog behaviour. While one model suggests that dogs may fight for desirable food, the other approach proposes that dogs collaborate with their owners while competing for good food. Researchers develop both ideas by comparing them and filling in gaps that preclude a thorough explanation of the behaviour.
Triangulation of scales
When analysing the same issue or behaviour, scales triangulation includes using different measures to examine the validity of the scales. This remains particularly significant in quantitative research since it allows researchers to double-check their findings. For instance, when evaluating the effectiveness of pain drugs for treating chronic pain, academics might use two independent measures of chronic pain severity. By comparing the two measurements, it appears possible to improve the validity of both.
Triangulation of multiple methods
Triangulation of multiple methods refers to the process of collecting data through numerous methods. This ensures objectivity in analysis and eliminates researcher bias in study findings. It also aids in the refinement of the study by revealing any gaps or discrepancies. A researcher, for example, might use several data gathering methods to validate the findings of their study, such as face-to-face conversations or telephone interviews with various categories of people.
Triangulation of the environment
Researchers use triangulation of the environment when there's a chance of results being impacted by particular environmental conditions. This kind of triangulation uses a variety of settings and other aspects, such as the study's time and season. Environmental triangulation aims to determine which of these elements might influence the information received before adjusting those factors to see if the results remain the same.
What biases can triangulation minimise?
The issue with depending on only one form of research is that it leads to prejudice. In research, there remain numerous types of biases. Triangulation can help minimise the risk of the following biases:
Measurement bias: Measurement bias refers to a problem that arises from the way in which you collect data. To help decrease bias, such as group influence on focus group members, triangulation allows you to alternate between group-based and individual research options.
Sampling bias: Sampling bias occurs when you don't represent all of the population you're investigating or simply cover a portion of the population since it's more practical. Triangulation combines the advantages of these techniques to guarantee that you have adequate coverage.
Procedural bias: Procedural bias refers to when individuals find themselves under duress to produce information. Triangulation helps you combine brief exchanges with longer interactions, giving participants more time to think about their comments.
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