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Triangulation in Research – Types, Methods and Guide

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Triangulation

Definition:

Triangulation is a research technique that involves the use of multiple methods or sources of data to increase the validity and reliability of findings.

When triangulated, data from different sources can be combined and analyzed to produce a more accurate understanding of the phenomenon being studied. Triangulation can be used in both quantitative and qualitative research and can be implemented at any stage of the research process.

Types of Triangulation

There are many types of Triangulation in research but we are featuring only Five main types:

Data Triangulation

Data triangulation is the use of multiple sources of data to examine a research question or phenomenon. This can include using a variety of data collection methods, such as surveys, interviews, observations, and document analysis, to gain a more comprehensive understanding of the phenomenon. By using multiple sources of data, researchers can validate their findings and reduce the risk of bias that may occur when using a single method.

Methodological Triangulation

Methodological triangulation involves using multiple research methods to investigate a research question or phenomenon. This can include both qualitative and quantitative methods, or different types of qualitative methods, such as focus groups and interviews. By using multiple methods, researchers can strengthen their findings, as well as gain a more comprehensive understanding of the phenomenon.

Theoretical Triangulation

Theoretical triangulation involves using multiple theoretical frameworks or perspectives to analyze and interpret research findings. This can include applying different theoretical models or approaches to the same data to gain a deeper understanding of the phenomenon. The use of multiple theories can also help to validate findings and identify inconsistencies.

Investigator Triangulation

Investigator triangulation involves using multiple researchers to examine a research question or phenomenon. This can include researchers with different backgrounds, expertise, and perspectives, to reduce the risk of bias and increase the validity of the findings. It can also help to validate the findings by having multiple researchers analyze and interpret the data.

Time Triangulation

Time triangulation involves studying the same phenomenon or research question at different time points. This can include longitudinal studies that track changes over time, or retrospective studies that examine the same phenomenon at different points in the past. Time triangulation can help to identify changes or patterns in the phenomenon, as well as validate findings over time.

Triangulation Methods

Triangulation is a research technique that involves using multiple methods, sources, or perspectives to validate or corroborate research findings. Here are some common triangulation methods used in research:

Qualitative and Quantitative Methods

Triangulating between qualitative and quantitative methods involves using both types of research methods to collect data and analyze the phenomenon under investigation. This can help to strengthen the validity and reliability of the findings by providing a more comprehensive understanding of the phenomenon.

Multiple Data Sources

Triangulating between multiple data sources involves collecting data from various sources to validate the findings. This can include using data from interviews, observations, surveys, or archival records to corroborate the findings.

Multiple Researchers

Triangulating between multiple researchers involves using multiple researchers to analyze and interpret the data. This can help to ensure the findings are not biased by the perspectives of a single researcher.

Triangulating Theories

Triangulating between theories involves using multiple theoretical frameworks to analyze and interpret the data. This can help to identify inconsistencies in the findings and provide a more comprehensive understanding of the phenomenon under investigation.

Triangulating Methodologies

Triangulating between methodologies involves using multiple research methods within a single research design. For example, a study may use both qualitative and quantitative methods to investigate the same phenomenon, providing a more comprehensive understanding of the phenomenon.

Triangulating Time

Triangulating between time involves studying the same phenomenon at different points in time. This can help to identify changes in the phenomenon over time and validate the findings across time.

Triangulating Participants

Triangulating between participants involves collecting data from multiple participants with different backgrounds, experiences, or perspectives. This can help to validate the findings and provide a more comprehensive understanding of the phenomenon under investigation.

Data Collection Methods

Here are some common triangulation data collection methods used in research:

Interviews are a popular data collection method used in qualitative research. Researchers may use different types of interviews, such as structured, semi-structured, or unstructured interviews, to gather data from participants. Triangulating interviews involves conducting multiple interviews with different participants or conducting interviews with the same participants at different times to validate or corroborate the findings.

Observations

Observations involve systematically observing and recording behavior or interactions in a natural setting. Researchers may use different types of observations, such as participant observation, non-participant observation, or structured observation, to collect data. Triangulating observations involves collecting data from different observers or conducting observations at different times to validate or corroborate the findings.

Surveys involve collecting data from a large number of participants using standardized questionnaires. Researchers may use different types of surveys, such as self-administered surveys or telephone surveys, to collect data. Triangulating surveys involves collecting data from different surveys or using surveys in combination with other data collection methods to validate or corroborate the findings.

Document Analysis

Document analysis involves systematically analyzing and interpreting documents, such as government reports, policy documents, or archival records, to understand a phenomenon. Triangulating document analysis involves analyzing different types of documents or using document analysis in combination with other data collection methods to validate or corroborate the findings.

Focus Groups

Focus groups involve bringing together a group of people to discuss a specific topic or phenomenon. Researchers may use different types of focus groups, such as traditional focus groups or online focus groups, to collect data. Triangulating focus groups involves conducting multiple focus groups with different participants or conducting focus groups in combination with other data collection methods to validate or corroborate the findings.

Data Analysis Methods

Here are some common data analysis methods used in triangulation:

• Comparative analysis: Comparative analysis involves comparing data collected from different sources or methods to identify similarities and differences in the findings. This can help to identify patterns and relationships across the data and validate or corroborate the findings.
• Convergent validation: Convergent validation involves using different methods to collect data on the same phenomenon and comparing the findings to identify areas of convergence or agreement. This can help to increase the validity and reliability of the findings by providing multiple perspectives on the phenomenon.
• Divergent validation: Divergent validation involves using different methods to collect data on the same phenomenon and comparing the findings to identify areas of divergence or disagreement. This can help to identify inconsistencies in the findings and provide a more comprehensive understanding of the phenomenon.
• Complementary analysis: Complementary analysis involves using different methods to collect data on different aspects of the same phenomenon and combining the findings to provide a more comprehensive understanding of the phenomenon. This can help to identify patterns and relationships across the data and provide a more complete picture of the phenomenon.
• Triangulated verification: Triangulated verification involves using multiple methods to verify the findings. This can involve using different data collection methods, data sources, or data analysis methods to validate or corroborate the findings.
• Meta-triangulation: Meta-triangulation involves using multiple studies or research designs to triangulate the findings. This can involve combining the findings from different studies or using multiple research designs to investigate the same phenomenon, providing a more comprehensive understanding of the phenomenon.
• Member checking: Member checking involves validating the findings with the participants or stakeholders involved in the research. This can help to ensure the findings accurately reflect the experiences and perspectives of the participants and increase the credibility of the findings.
• Peer review: Peer review involves having other researchers or experts review the findings to ensure their validity and reliability. This can help to identify potential biases or errors in the data analysis and increase the credibility of the findings.
• Triangulated coding: Triangulated coding involves using different coding methods or approaches to analyze the data and identify themes or patterns. This can help to ensure the reliability and validity of the coding process and increase the credibility of the findings.
• Inter-rater reliability: Inter-rater reliability involves having multiple coders independently analyze the same data and comparing their findings to ensure consistency and agreement in the coding process. This can help to increase the reliability and validity of the findings.

How to Conduct Triangulation

Here are some general steps to conduct triangulation in research:

• Determine the research question: The first step in conducting triangulation is to determine the research question or objective. This will help to identify the types of data sources and methods needed to answer the research question.
• Select multiple data sources: Identify the multiple data sources that can be used to answer the research question. These sources may include primary data sources such as surveys, interviews, or observations, or secondary data sources such as literature reviews or existing datasets.
• Choose multiple data collection methods : Choose the multiple data collection methods that can be used to gather data from each data source. These methods may include quantitative and qualitative methods, such as surveys, focus groups, interviews, or observations.
• Collect data: Collect data from each data source using the selected data collection methods. Be sure to document the methods used to collect the data and any issues that arise during the data collection process.
• Analyze data: Analyze the data using appropriate data analysis methods. This may involve using different methods or approaches to analyze the data from each data source.
• Compare and contrast findings: Compare and contrast the findings from each data source to identify similarities and differences. This can help to validate or corroborate the findings and identify any inconsistencies or biases in the data.
• Synthesize findings: Synthesize the findings from each data source to provide a more comprehensive understanding of the phenomenon under investigation. This can involve identifying patterns or themes across the data and drawing conclusions based on the findings.
• Evaluate and report findings: Evaluate the validity and reliability of the findings and report the results in a clear and concise manner. Be sure to include a description of the triangulation process and the methods used to ensure the validity and reliability of the findings.

Applications of Triangulation

Here are some common applications of triangulation:

• Validating research findings: Triangulation can be used to validate research findings by using multiple methods, sources, or perspectives to corroborate the results. This can help to ensure that the findings are accurate and reliable and increase the credibility of the research.
• Exploring complex phenomena: Triangulation can be particularly useful when investigating complex or multifaceted phenomena that cannot be fully understood using a single method or perspective. By using multiple methods or sources, triangulation can provide a more comprehensive understanding of the phenomenon under investigation.
• Enhancing data quality: Triangulation can help to enhance the quality of the data collected by identifying inconsistencies or biases in the data and providing multiple perspectives on the phenomenon. This can help to ensure that the data is accurate and reliable and increase the validity of the research.
• Providing richer data: Triangulation can provide richer and more detailed data by using multiple data collection methods or sources to capture different aspects of the phenomenon. This can provide a more complete picture of the phenomenon and help to identify patterns and relationships across the data.
• Enhancing the credibility of the research: Triangulation can enhance the credibility of the research by using multiple methods or sources to corroborate the findings and ensure their validity and reliability. This can increase the confidence that readers or stakeholders have in the research and its findings.

Examples of Triangulation

Here are some real-time examples of triangulation:

• Mixed-methods research : Mixed-methods research is a common example of triangulation that involves using both quantitative and qualitative research methods to collect and analyze data. This approach can help to validate or corroborate the findings by providing multiple perspectives on the same phenomenon.
• Clinical diagnosis : In medicine, triangulation can be used to diagnose complex or rare medical conditions. This can involve using multiple diagnostic tests, such as blood tests, imaging scans, and biopsies, to corroborate the diagnosis and ensure its accuracy.
• Market research : In market research, triangulation can be used to validate consumer preferences or opinions. This can involve using multiple data collection methods, such as surveys, focus groups, and interviews, to ensure the validity and reliability of the findings.
• Educational research: In educational research, triangulation can be used to evaluate the effectiveness of teaching methods. This can involve using multiple data sources, such as student test scores, classroom observations, and teacher interviews, to provide a more comprehensive understanding of the teaching and learning process.
• Environmental research: In environmental research, triangulation can be used to evaluate the impact of human activities on the environment. This can involve using multiple data sources, such as satellite imagery, field observations, and interviews with local communities, to provide a more comprehensive understanding of the environmental impacts.

Purpose of Triangulation

The purpose of triangulation in research is to increase the validity and reliability of the findings by using multiple data sources and methods to study the same phenomenon. Triangulation can help to mitigate the limitations of using a single data source or method and can provide a more comprehensive understanding of the research question or objective.

By using multiple data sources and methods, triangulation can help to:

• Validate research findings: Triangulation can help to validate the findings by providing converging evidence from multiple data sources and methods. This can increase the credibility of the research and reduce the likelihood of drawing false conclusions.
• Enhance the completeness of data : Triangulation can help to enhance the completeness of data by providing multiple perspectives on the same phenomenon. This can help to capture the complexity and richness of the phenomenon and reduce the risk of bias or oversimplification.
• I dentify discrepancies and inconsistencies : Triangulation can help to identify discrepancies and inconsistencies in the data by comparing and contrasting the findings from multiple data sources and methods. This can help to identify areas of uncertainty or ambiguity and guide further investigation.
• Provide a more comprehensive understanding: Triangulation can help to provide a more comprehensive understanding of the research question or objective by integrating data from multiple sources and methods. This can help to identify patterns or relationships that may not be apparent from a single data source or method.

When to use Triangulation

Here are some situations where triangulation may be appropriate:

• When the research question is complex: Triangulation may be appropriate when the research question is complex and requires a multifaceted approach. Using multiple data sources and methods can help to capture the complexity of the phenomenon under investigation.
• When the research is exploratory: Triangulation may be appropriate when the research is exploratory and aims to generate new insights or hypotheses. Using multiple data sources and methods can help to validate the findings and reduce the risk of drawing false conclusions.
• When the research is sensitive: Triangulation may be appropriate when the research is sensitive and requires a high level of rigor and validation. Using multiple data sources and methods can help to increase the credibility and rigor of the findings and reduce the likelihood of bias or error.
• When the research is interdisciplinary: Triangulation may be appropriate when the research is interdisciplinary and requires a range of expertise and methods. Using multiple data sources and methods can help to integrate different perspectives and approaches and provide a more comprehensive understanding of the phenomenon under investigation.
• When the research is longitudinal : Triangulation may be appropriate when the research is longitudinal and aims to study changes over time. Using multiple data sources and methods can help to capture the changes and validate the findings across different time periods.

Advantages of Triangulation

Here are some advantages of using triangulation:

• Increased validity: Triangulation can help to increase the validity of research findings by providing converging evidence from multiple data sources and methods. This can help to reduce the risk of drawing false conclusions and increase the credibility of the research.
• Increased reliability: Triangulation can help to increase the reliability of research findings by reducing the likelihood of bias or error. By using multiple data sources and methods, triangulation can help to validate the findings and reduce the risk of drawing incorrect conclusions.
• Enhanced completeness of data: Triangulation can help to enhance the completeness of data by providing multiple perspectives on the same phenomenon. This can help to capture the complexity and richness of the phenomenon and reduce the risk of oversimplification.
• Better understanding of the phenomenon: Triangulation can help to provide a better understanding of the phenomenon under investigation by integrating data from multiple sources and methods. This can help to identify patterns or relationships that may not be apparent from a single data source or method.
• Increased confidence in the findings: Triangulation can help to increase the confidence in the research findings by providing multiple sources of evidence. This can help to reduce the risk of drawing false conclusions and increase the credibility of the research.

Limitations of Triangulation

Here are some limitations of using triangulation:

• Resource-intensive: Triangulation can be resource-intensive in terms of time, money, and personnel. Collecting and analyzing data from multiple sources and methods can require more resources than using a single data source or method.
• Increased complexity: Triangulation can increase the complexity of the research process by requiring researchers to integrate data from multiple sources and methods. This can make the analysis more challenging and time-consuming.
• Difficulty in comparing data: Triangulation can make it difficult to compare data collected from different sources and methods. The data may be collected using different measures or instruments, making it difficult to compare or combine the data.
• Data inconsistencies: Triangulation can also result in data inconsistencies if the data collected from different sources or methods are contradictory or conflicting. This can make it challenging to interpret the findings and draw meaningful conclusions.
• Interpretation issues: Triangulation can also create interpretation issues if the findings from different data sources or methods are not consistent or do not converge. This can lead to uncertainty or ambiguity in the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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What is Triangulation in Research: The Path to Reliable Findings

What is triangulation in research? Maximize the accuracy of your research and uncover the benefits of this method.

One word stands out as a beacon for researchers seeking reliable and solid results in the broad universe of research methodology: triangulation. This powerful technique has become more widely recognized and significant in a wide range of fields, providing a pathway to improve the validity and reliability of research findings. Triangulation enables researchers to reduce biases, increase the depth of research, and eventually reach more trustworthy and thorough conclusions by merging diverse sources of data, methodologies, and perspectives.

The goal of this article is to answer the question “what is triangulation in research?” by clarifying the concept, identifying its guiding principles, techniques, and purposes. 

What is Triangulation in Research?

Research triangulation is the process of examining a research topic or phenomenon from several angles, data sources, or methods. To improve the validity, reliability, and thoroughness of research findings, it entails merging several methodologies and information sources.

The concept of triangulation comes from surveying, where it is used to pinpoint an object’s exact location by using a number of reference points. Similar to this, triangulation in research aims to create a convergent and thorough understanding of a subject by examining it from various perspectives.

Researchers try to reduce the shortcomings and biases present in any single method or data source by using triangulation. They may employ it to cross-validate results, spot patterns or discrepancies, and develop a more comprehensive grasp of the research topic.

Types of Triangulation in Research

The overall strength and credibility of research findings are enhanced by many different types of triangulation, each of which has specific advantages. Here are some typical triangulation techniques used in research:

Data Triangulation

This type of triangulation uses a variety of sources or data types to obtain insight into a research topic. To support and validate their findings, researchers collect data using a variety of methods, including interviews, surveys, observations, and pre-existing records. The reliability and robustness of researchers’ results can be improved by merging several data sources.

Methodological Triangulation

Utilizing different methods or approaches to investigate a research question is known as methodological triangulation. To explore the same phenomenon from multiple perspectives, researchers use a variety of methodologies, including qualitative and quantitative methods, experimental and non-experimental designs, case studies, and surveys. Researchers can capture various aspects of the research topic and get a more thorough understanding of it by using complementary methodologies.

Investigator Triangulation

A research project including multiple researchers or investigators is known as investigator triangulation. The different perspectives, experiences, and biases that each researcher brings to the table can support and validate the findings of the others. The research process gets more rigorous when multiple researchers are involved as they may test each other’s assumptions and interpretations, which ultimately produces results that are more reliable.

Theoretical Triangulation

To interpret research findings, theory triangulation requires using several theoretical frameworks or perspectives. Researchers may use a range of theories to assess their data and compare their findings drawn from different perspectives. This method aids in the discovery of multiple facets or explanations for the phenomenon being investigated, enriching and deepening the analysis.

Time Triangulation

Studying a problem or phenomenon over a period of time is known as time triangulation. In order to identify changes, trends, or patterns across time, researchers examine the topic at multiple points in time. Researchers can recognize temporal fluctuations, comprehend the evolution of the phenomena, or determine long-term impacts by comparing data obtained at several time points.

Localization Triangulation

Studying a phenomenon or research subject in multiple settings or geographic locations is known as location triangulation. Researchers can take into consideration cultural, social, or environmental variables that might have an impact on the occurrence by conducting research in a wide range of settings. This method makes it easier to find general or context-specific elements that influence the study topic.

Purposes and Advantages of Triangulation in Research

Triangulation in research serves several important purposes that contribute to the overall quality and credibility of research findings. Here are three key purposes of triangulation:

Enhancing Validity

By lowering the possibility of bias and increasing the reliability of the findings, triangulation contributes to the validity of research. Researchers can confirm their findings and make sure that the conclusions are consistent by employing a variety of methods, data sources, or perspectives.

Gaining a Complete Picture

Research frequently involves complicated phenomena that can’t be fully understood by a single approach or data set. By combining different approaches, triangulation enables researchers to develop a more thorough understanding of the research topic. Researchers can discover many aspects, perspectives, or dimensions of the phenomenon they are studying by integrating different approaches, data sources, or theories. This all-encompassing approach aids researchers in presenting a more thorough and nuanced picture of the research topic.

Cross-Checking Evidence

Researchers can cross-check their data and validate their findings using multiple approaches through triangulation. Researchers may discover patterns, consistency, or discrepancies in the data by using a range of approaches or data sources. When results from multiple methods or sources are comparable, there is more reason to believe that the findings are accurate and reliable. If there are differences, on the other hand, researchers may investigate the causes and improve their interpretations. The robustness of the research findings is strengthened by this iterative process of cross-checking the evidence.

Disadvantages of Triangulation in Research

While research triangulation has many benefits, it’s vital to think about the possible disadvantages as well. A few disadvantages of using triangulation include the following:

Increased Complexity

Triangulation frequently entails the integration of many methodologies, data sources, or perspectives, which can make the research process more complex. In order to ensure coherence and compatibility between different methods, researchers must carefully plan and manage their integration. Triangulation can require additional resources and knowledge to be applied correctly. Data collection, processing, and interpretation may be difficult due to the complexity of triangulation, which necessitates that researchers carefully navigate and handle the complexities involved.

Resource Intensiveness

Triangulation may require a lot of resources in terms of funds, time, and work. Comparatively to a single-method study, performing multiple approaches or collecting data from multiple sources may take more time and effort. It can entail obtaining a larger sample size, educating researchers on different methods, or carrying out multiple types of data gathering. Research feasibility may be hindered by the need for more resources, both financial and human, especially in circumstances where those resources are scarce.

Increased Subjectivity

Researchers’ perceptions and analyses of the data might still be influenced by their own biases and perspectives, even when multiple techniques, data sources, or perspectives are combined. Findings from many sources may need to be integrated and synthesized, which could involve subjective decisions and perhaps introduce researcher bias. In order to achieve impartiality and openness throughout the triangulation process, researchers must be aware of their own biases.

Inconsistency

Triangulation can occasionally produce inconsistent findings. Researchers might find themselves dealing with discrepancies or inconsistencies among the data sources. Conflicting results might be difficult to manage and reconcile, necessitating further investigation or methodological improvement. The causes of inconsistencies must be carefully considered, and the implications must be evaluated rigorously.

Time-Consuming

As data from multiple sources or methodologies must be gathered, analyzed, and integrated, triangulation might make the research process lengthier. Triangulation may require an extensive amount of time and work, which could extend project timeframes or cause research to be completed later. Researchers must carefully examine whether the possible advantages of triangulation outweigh the related costs, taking into account the increased time and effort necessary.

When to Use Triangulation?

It is time to consider when to employ triangulation now that the key question, “What is triangulation in research?” has been addressed.

Triangulation is a valuable research approach that can be used in various situations. It is especially beneficial in the following situations:

• Confirming Findings: By utilizing a variety of methods, sources of data, or perspectives, triangulation helps validate and strengthen the reliability of the research’s findings.
• Exploring Complex Phenomena: Triangulation enables the exploration of complicated phenomena that cannot be fully understood by a single approach or data source.
• Mitigating Bias: Triangulation reduces bias by combining many methods, reducing the influence of individual biases, and increasing objectivity.
• Addressing Research Limitations: Triangulation overcomes the limitations of a single method or data source, enhancing the comprehensiveness and quality of the study.
• Enhancing Validity and Reliability: By offering convergent evidence from different approaches, triangulation increases the credibility and trustworthiness of research findings.
• Investigating Controversial or Sensitive Topics: When looking into controversial or sensitive topics, triangulation offers a more balanced and nuanced perspective. 

When selecting whether to employ triangulation, researchers should carefully assess the research’s context, objectives, available resources, and the nature of the research topic. 

Examples of Triangulation

A few examples of how triangulation can be used in research are provided below:

• Data Triangulation Example: A researcher studying how a new educational program affects student performance may gather data using a wide range of methods, including student surveys, teacher interviews, and academic record analysis. By combining these numerous data sources, the researcher can get a more thorough knowledge of the program’s effects and validate the results using a variety of data sets.
• Methodological Triangulation Example: A researcher may use both quantitative and qualitative methodologies in a study that focuses on the connection between exercise and mental health. In-depth interviews with a select group of participants could be used in combination with surveys to collect quantitative information on levels of physical activity and mental health scores. By integrating these two approaches, the researcher can gain a broader understanding of the subject, adding personal experiences and perspectives to data trends.
• Investigator Triangulation Example: Researchers involved in a project to examine how climate change affects biodiversity may have backgrounds in ecology, climatology, and social sciences, among other fields. Each researcher contributes their special knowledge and perspectives, working together to collect and examine data from multiple perspectives. They can cross-validate their findings by combining their knowledge and expertise, resulting in a thorough grasp of the intricate interplay between climate change and biodiversity.

Data Collection Methods for Triangulation

Here are some common data collection methods used for triangulation:

• Surveys: Using questionnaires or structured interviews, surveys collect data from a sizable sample of individuals in a consistent manner. Surveys can offer quantitative information that can be statistically evaluated and coupled with various sources of qualitative information.
• Interviews: Open-ended discussions take place with participants during interviews in order to collect comprehensive qualitative data. Insightful opinions and nuanced information that may not be gathered through other means can be obtained through interviews.
• Observations: Direct observation and documentation of behaviors, interactions, or occurrences happening in their natural environments are known as observations. With the use of this method, real-time and context-specific data may be collected to give a thorough grasp of the research topic.
• Document Analysis: Document analysis is the process of extracting pertinent information from existing documents, such as reports, articles, or archive records. To supplement primary data sources, this method can offer historical context, supplementary data, or additional perspectives. 

Data Analysis Methods for Triangulation

Here are a few often employed triangulation techniques for data analysis:

• Comparative Analysis: Comparative analysis involves comparing and contrasting several data sources or methods of analysis in order to identify trends, patterns, or contradictions.
• Integration of Findings: Integration is the process of compiling data from multiple sources or methodologies into a single dataset that can then be analyzed. 
• Cross-Validation: Cross-validation involves comparing results collected from different data-gathering methods or sources in order to assess the consistency and dependability of findings. 

7 Steps on How to Conduct Triangulation

To conduct triangulation in research, follow these steps:

• Define Research Objectives: Clearly define the research objectives and questions to determine the purpose and scope of the triangulation approach.
• Select Data Collection Methods: Choose appropriate data collection methods that align with the research objectives and allow for complementary data collection. Consider the strengths and limitations of each method.
• Gather Data: Implement the selected data collection methods to gather relevant data from multiple sources or perspectives. Ensure data collection procedures are consistent, reliable, and ethical.
• Organize and Analyze Data: Organize and analyze the collected data using suitable methods and techniques. Apply comparative analysis, integration of findings, and cross-validation to identify patterns and ensure reliability.
• Interpret Findings: Compare and contrast the results from different data sources or methods to interpret the findings. Look for convergence or divergence to draw meaningful conclusions.
• Reflect on Limitations: Acknowledge and address the limitations or challenges associated with triangulation, such as bias or resource constraints. Reflect on their potential impact on research outcomes.
• Communicate Results: Clearly communicate the triangulated findings, highlighting the strengths, limitations, and implications of the research. Present the results comprehensively and transparently, acknowledging the sources of data and the analytical processes employed.

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The use of triangulation in qualitative research

Affiliations.

• 1 School of Nursing, McMaster University, Hamilton, Ontario.
• 2 School of Nursing and Department of Oncology, McMaster University.
• 3 School of Nursing and the Department of Clinical Epidemiology and Biostatistics, McMaster University.
• 4 School of Nursing, McMaster University.
• 5 Department of Oncology, Faculty of Health Sciences, McMaster University, Canada.
• PMID: 25158659
• DOI: 10.1188/14.ONF.545-547

Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.

Keywords: focus groups; in-depth individual interviews; qualitative research; triangulation.

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• Volume 16, Issue 4
• Understanding triangulation in research
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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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Frequently asked questions

What is triangulation in research.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Why Researcher Triangulation Matters

Introduction

Importance of triangulation in research, types of triangulation in research, researcher triangulation in qualitative research, challenges of researcher triangulation.

Ensuring the accuracy and credibility of findings is paramount in research employing both quantitative and qualitative methods . While quantitative research often relies on standardized methods and controlled environments to generate trustworthy findings, the social world that qualitative research examines is far from a static environment, requiring a different approach to establishing research rigor.

One technique that stands out for its profound impact on the validity of qualitative research outcomes is researcher triangulation. At its core, researcher triangulation involves leveraging multiple investigators to study a particular phenomenon, each bringing their unique perspective, thereby enriching the analysis and trustworthiness of the findings.

This collaborative approach not only strengthens the integrity of the research but also paves the way for a more comprehensive understanding of the subject matter. We'll discuss the history, benefits, challenges, and contribution of researcher triangulation in this article.

When conducting or reading qualitative research , one might wonder: How can we be certain our findings truly capture the essence of the phenomena we study? How do we ensure the accuracy and robustness of our conclusions? Triangulation addresses this need for research rigor .

Triangulation, in its broadest sense, refers to the practice of employing multiple lenses, methods, or sources to scrutinize a singular research question or topic. It stems from the navigational principle of determining a single location by taking bearings from two other distinct points. Applied to research, it means that different methods or sources pointing to the same conclusion can bolster our confidence in those findings.

The primary rationale behind triangulation is to counteract the inherent limitations and subjectivities that come with any single method, data source, or theoretical perspective . For instance, while interviews may provide deep insights into an individual's perspective, they might not include other relevant aspects that the participant didn't bring up. By supplementing interview data with observational data or survey results , researchers can attain a more comprehensive and balanced understanding of their research topic .

Furthermore, triangulation fosters depth and complexity in analysis. By looking at a topic from multiple angles, researchers can uncover nuanced insights that might have been overlooked if only one method or source were utilized. This multifaceted exploration enriches the final conclusions, making them more resonant and impactful.

More broadly, triangulation serves as a validation strategy, enhancing the reliability and credibility of research findings. It safeguards against misinterpretation , providing a more holistic view that strengthens the overall integrity of the research endeavor.

There are many different approaches to establishing research rigor through triangulation, with researcher triangulation being just one approach. This section looks at some of the other common forms of triangulation that researchers employ in their research.

While it doesn't hurt to accomplish all possible forms of research triangulation, it's certainly more feasible to focus solely on certain types of triangulation over others. Which types are most appropriate, of course, will depend on your research question .

Data triangulation

Data triangulation involves using multiple data sources to study the same phenomenon. By collecting data from varied sources, researchers aim to enhance the validity and breadth of their findings. For instance, a study on employee satisfaction might gather data from interviews with employees, internal company reports, and surveys with employees' coworkers to get a comprehensive perspective.

Method triangulation

Method triangulation requires the use of multiple methods to study a single topic or phenomenon. This might involve combining qualitative and quantitative methods or using multiple qualitative methods like interviews , focus groups , and observations . Methodological triangulation ensures that the topic is examined from various angles, further validating the research outcomes.

Theory triangulation

Theory triangulation entails applying multiple theoretical perspectives for analyzing data in multiple ways. This challenges the data by viewing it through various lenses, ensuring that interpretations aren't overly influenced by a single theoretical framework . For example, a researcher studying migration patterns might employ sociological, economic, and geopolitical theories to interpret their findings.

Environmental triangulation

This form of triangulation involves assessing the research topic in various locations, conditions, or times. By studying the phenomenon in different environments or at different times, researchers can identify if certain outcomes are consistent or if they vary based on external factors. A study on classroom dynamics, for instance, might be conducted in urban, suburban, and rural schools to understand if and how the environment impacts behavior.

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Researcher triangulation, at its core, revolves around the incorporation of multiple researchers to examine a particular qualitative research topic . It is a strategy for enhancing the depth, breadth, and validity of research findings. But how exactly does it function and what advantages does it introduce?

Harnessing multiple perspectives

The beauty of qualitative research lies in its ability to capture the intricate nuances of human experiences and social phenomena. When we involve a number of researchers in the data collection and analysis process , we leverage a wealth of diverse viewpoints.

Each researcher brings with them a unique set of experiences, beliefs, and interpretative lenses. By incorporating these multiple perspectives, the research process benefits from a broadened horizon and a comprehensive analysis of data.

Diving deep into data analysis

Analyzing data in qualitative research isn't just about identifying patterns; it's about understanding the stories, emotions, and underlying factors present within the data. When multiple researchers analyze data, they inevitably shed light on aspects that might go unnoticed by a single investigator.

Researcher triangulation allows researchers to challenge each other's interpretations, raise questions, and foster deeper insights. The combined efforts of multiple researchers ensure a thorough and layered exploration of the data, further solidifying the study's conclusions.

Balancing researcher subjectivities

Every researcher, regardless of their expertise, carries unique perspectives shaped by their life experiences, educational background, and even their cultural upbringing. One primary advantage of researcher triangulation is that it acts as a counterbalance. When multiple researchers are engaged in a study, the individual subjectivities of any one person is less likely to dominate.

Instead, the collaborative approach ensures a more well-rounded interpretation of findings. Through discussions and debates, they collectively shape a more balanced outcome that is likely to resonate with more people.

Enhancing reliability and validity

In the realm of research, reliability and validity hold paramount importance. Researcher triangulation contributes significantly to these aspects. By having multiple researchers independently analyze data and then compare their findings, any inconsistencies can be identified and addressed.

This rigorous process can strengthen the reliability of the research by permitting an assessment of the extent to which multiple researchers agree on the findings. Moreover, the converging insights from different researchers can bolster the study's validity, as it reflects a consensus derived from varied viewpoints.

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While researcher triangulation is widely lauded for its contributions to the rigor and validity of qualitative research , it's essential to recognize that it isn't without its challenges. From logistics to the intricacies of interpersonal dynamics, navigating the waters of multiple perspectives requires care.

Managing differing interpretations

At the heart of qualitative research lies interpretation, which is inherently subjective. When multiple researchers analyze data , they may arrive at varying conclusions based on their individual perspectives, experiences, and biases. While this diversity of opinion is one of the strengths of researcher triangulation, it also poses a challenge. Aligning different interpretations or deciding which perspective holds more weight can be complex, often leading to debates and extended discussions.

Time and resource intensiveness

Engaging multiple researchers naturally means investing more time and resources. Collaborative data analysis requires coordination, additional meetings, and discussions. In some cases, there may be a need for training sessions to ensure that all researchers are on the same page in terms of methodology and approach. This increased investment can extend the duration of the research project and demand more resources, both in terms of finances and effort.

Potential for conflicts

With multiple individuals involved, there's always the potential for disagreements and conflicts. Differences in research philosophies, interpretations, or even personal dynamics can lead to disputes. Resolving these disagreements amicably while ensuring the integrity of the research can be challenging. It requires clear communication, mutual respect, and often, compromise.

Ensuring consistency in data collection

If researcher triangulation is applied at the data collection stage , maintaining consistency becomes vital. Different researchers might have varying techniques or styles of interviewing, observing, or interacting with participants. These differences, if not managed, can introduce inconsistencies in the data, which could affect the study's rigor.

Maintaining coherence of the research narrative

The final research output should present a coherent narrative. With multiple voices and perspectives shaping the research, there's a risk of the findings appearing disjointed or lacking a clear thread. It's a challenge to ensure that while honoring all perspectives, the research narrative remains cohesive and easy for the reader to follow.

Balancing depth with breadth

While the involvement of multiple researchers can lead to a broader perspective, there's a potential risk of skimming the surface. With each researcher possibly focusing on different aspects, the depth of exploration into any single facet might be compromised. Striking a balance between depth and breadth becomes crucial.

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