Triangulation, a methodology often employed in academic research, involves using multiple data sources, theories, methods, or investigators to gain a more comprehensive understanding of the phenomenon under study. This approach not only enhances the validity and reliability of the research but also provides a more nuanced and holistic perspective. This article discusses what triangulation is, its purpose, its four main types used in research.
Triangulation in research — In a nutshell
As you can see, working on your research question more holistically involves using triangulation in research. This is because the method has various benefits, including:
- Gathering more data from different sources, enhancing the credibility of your research
- Eliminating observer bias by using multiple researchers, techniques, and data sets
- Increasing the validity of your research
Definition: Triangulation in research
Triangulation in research is an experimental method that uses multiple sources to address one research question. These sources include data sets, processes, and researchers or investigators. In most cases, triangulation in research is used for qualitative research, while sometimes it’s applied in quantitative analysis. You can also apply methodological triangulation in mixed methods research.
Triangulation in research has different meanings depending on the type of analysis, as follows:
- Qualitative research: Conducting thorough interviews with stakeholders like parents, teachers, and students.
- Quantitative research: Running eye-tracking experiments involving multiple researchers to analyse the data.
- Mixed methods research: Conducting a quantitative survey and following it with several qualitative structured interviews.
4 types of triangulation in research
The four primary methods of triangulation in research are data, methodological, theoretical, and investigator. Let’s explain these four methods using the example below:
- Cooperation research: You’re researching what makes people act selfish instead of cooperative ways. You want to know the motivation behind these people working with others in a team setting.
Methodological triangulation
This combines different research methods to tackle the same research question. Most researchers often mix qualitative and quantitative research methods in a single study. This type of triangulation in research is primarily used when trying to avoid biases and flaws associated with using a single research method.
Example
You decide on using three methods to collect data in your study — neural, survey, and behavioural data. First, you invite participants to play team games in a controlled lab experiment while you record observations. You hand out surveys to record data about their daily lives about cooperation and use MRI scans to assess their Neural Mechanisms of Cooperation.
Data triangulation
You use different data sources to tackle your research topic in data triangulation in research. You’re free to collect data across different spaces, times, and people. This way, you can generalize your findings to other situations.
Example
You compile and analyse data from 150 UK students across six months to understand their motivation behind cooperative behaviour. You repeat the procedure with comparable samples in different worldwide regions like Japan and the US. This process allows you to collect more data and test your hypothesis using a broader scope.
Investigator triangulation
The investigator triangulation in research method uses multiple researchers or observers who process, collect, or analyse data separately. This method eliminates the risks of experimental biases, such as observer bias.
Example
You use different observers to help analyse your behavioural data and code the participants’ behaviors. You offer training sessions and a manual to ensure the code behaves the same way.
The observers review the video recordings, keep notes, and analyse cooperative behaviors. Next, you compare their code sheets to line up with each other to ensure high inter-rater reliability. Lastly, they calibrate how they intermittently code behaviors for consistency.
Theory triangulation
This method involves using more than one theoretical approach in answering research questions. Theory triangulation in research ensures that you understand a research topic from different perspectives or reconcile differences in your data. One way to conduct a theory in the research method is by testing competing hypotheses.
Example
Through triangulation in research, you ascertain that two competing motivational theories compel people to behave cooperatively:
- People cooperate to have a sense of reward
- They want to feel good.
- People collaborate to avoid guilt
- They want to avoid feeling bad.
You use fMR data to analyse whether there’s more brain activity in guilt-averse or reward-related brain areas for cooperative people.
The purpose of triangulation in research
Triangulation in research gives a more holistic approach to answering research problems and providing credible, valid, and reliable findings. This method is beneficial for your study in the following ways:
Enhances validity
Triangulation in research increases the validity of your study by combining multiple methods. Validity means that the methods used are sufficient to make measurements as intended. Since every research method has a disadvantage, incorporating techniques that account for each other’s limitations would be helpful.
Gives a complete picture
When you use multiple sources, data sets, researchers, and methods, ensure that you get a more vivid picture or understanding of your research problem fully. Multiple sources help you avoid the risks of biases or the disadvantages of using one method to conduct a study.
Cross-checks evidence
Credibility is the aspect that your data reflect reality; the more your data agree, the more credible your results will become. Knowing if your data is trustworthy when you collect it from only one source is challenging. However, when data gathered from multiple sources point toward a particular direction, you’re sure that your findings are credible.
Pros and cons of triangulation in research
Although triangulation in research is widely used and accepted for its resourcefulness, it still has some disadvantages you should be aware of. Here are the main pros and cons:
| ✓ Pros | ✗ Cons |
|
Reduces bias: Multiple data, methods, researchers, and theories remove the bias associated with using a single perspective in your study. |
Time-consuming: Working with different data sources, observers, and methods is time-consuming. It's also costly and involves working with an interdisciplinary team. |
|
Establishes validity and credibility of your research: Different methods, observers, and data enhance your research's validity and credibility. |
Inconsistency: Data from different sources and observers may contradict one another and not line up to give precise results. |
FAQs
Yes, some researchers may overestimate the values of triangulation when conducting studies.
Credibility is the confidence that your study’s results reflect reality and may be used to solve real-world issues. Triangulation in research is a method that allows you to understand your research topic completely.
Methodical triangulation in research is also called mixed methods research.
