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.
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.
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.
