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Triangulation in Research | Guide, Types, Examples

Published on January 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

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 and mitigate the presence of any research biases in your work.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . If you decide on mixed methods research , you’ll always use methodological triangulation.

• Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
• Quantitative research: You run an eye-tracking experiment and involve three researchers in analyzing the data.
• Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

Table of contents

Types of triangulation in research, what is the purpose of triangulation, pros and cons of triangulation in research, other interesting articles, frequently asked questions about triangulation.

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 analyzing data
• Theory triangulation: Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

We’ll walk you through the four types of triangulation using an example. This example is based on a real study .

Methodological triangulation

When you use methodological triangulation, you use different methods to approach the same research question.

This is the most common type of triangulation, and researchers often combine qualitative and quantitative research methods in a single study.

Methodological triangulation is useful because you avoid the flaws and research bias that come with reliance on a single research technique.

Data triangulation

In data triangulation, you use multiple data sources to answer your research question. You can vary your data collection across time, space, or different people.

When you collect data from different samples, places, or times, your results are more likely to be generalizable to other situations.

Investigator triangulation

With investigator triangulation, you involve multiple observers or researchers to collect, process, or analyze data separately.

They review video recordings of your participants playing team games in pairs and analyze and note down any cooperative behaviors. You check that their code sheets line up with each other to ensure high interrater reliability.

Investigator triangulation helps you reduce the risk of observer bias and other experimenter biases.

Theory triangulation

Triangulating theory means applying several different theoretical frameworks in your research instead of approaching a research question from just one theoretical perspective.

• People cooperate for a sense of reward: they cooperate to feel good.
• People cooperate to avoid guilt: they cooperate to avoid feeling bad.

Testing competing hypotheses is one way to perform theory triangulation. Using theory triangulation may help you understand a research problem from different perspectives or reconcile contradictions in your data.

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Researchers use triangulation for a more holistic perspective on a specific research question. Triangulation is also helpful for enhancing credibility and validity.

To cross-check evidence

It’s important to gather high-quality data for rigorous research. When you have data from only one source or investigator, it may be difficult to say whether the data are trustworthy.

But if data from multiple sources or investigators line up, you can be more certain of their credibility.

Credibility is about how confident you can be that your findings reflect reality. The more your data converge, or or agree with each other, the more credible your results will be.

For a complete picture

Triangulation helps you get a more complete understanding of your research problem.

When you rely on only one data source, methodology, or investigator, you may risk bias in your research. Observer bias may occur when there’s only one researcher collecting data. Similarly, using just one methodology means you may be disadvantaged by the inherent flaws and limitations of that method.

• Behavioral observations from a lab setting
• Self-report survey data from participants reflecting on their daily lives
• Neural data from an fMRI scanner during a cooperative task

It’s helpful to use triangulation when you want to capture the complexity of real-world phenomena. By varying your data sources, theories, and methodologies, you gain insights into the research problem from multiple perspectives and levels.

To enhance validity

Validity is about how accurately a method measures what it’s supposed to measure.

You can increase the validity of your research through triangulation. Since each method has its own strengths and weaknesses, you can combine complementary methods that account for each other’s limitations.

In contrast, survey data offers you more insights into everyday behaviors outside a lab setting, but since it’s self-reported, it may be biased.

Finally, fMRI data can tell you more about hidden neural mechanisms without any participant interference. But this type of data is only valuable for your research when combined with the others.

Like all research strategies, triangulation has both advantages and disadvantages.

Reduces bias

Triangulating data, methods, investigators, or theories helps you avoid the research bias that comes with using a single perspective in your research. You’ll get a well-rounded look into the research topic when you use triangulation.

Establishes credibility and validity

Combining different methods, data sources, and theories enhances the credibility and validity of your research. You’ll be able to trust that your data reflect real life more closely when you gather them using multiple perspectives and techniques.

Time-consuming

Triangulation can be very time-consuming and labor-intensive. You’ll need to juggle different datasets, sources, and methodologies to answer one research question.

This type of research often involves an interdisciplinary team and a higher cost and workload. You’ll need to weigh your options and strike a balance based on your time frame and research needs.

Inconsistent

Sometimes, the data from different sources, investigators, methods may not line up to give you a clear picture. Your data may be inconsistent or contradict each other.

This doesn’t necessarily mean that your research is incoherent. Rather, you’ll need to dig deeper to make sense of why your data are contradictory. These inconsistencies can be challenging but may also lead to new avenues for further research.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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See an example

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.

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 analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research

Triangulation can help:

• Reduce research 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 labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

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Principles, Scope, and Limitations of the Methodological Triangulation *

Principios, alcances y limitaciones de la triangulación metodológica, princípios, alcances e limitações da triangulação metodológica, maría mercedes arias valencia.

1 Nurse, Ph.D. Professor at Facultad de Enfermería, Universidad de Antioquia. Medellín (Colombia). Email: [email protected], Universidad de Antioquia, Facultad de Enfermería, Universidad de Antioquia, Medellín , Colombia, [email protected]

This article sought to collect basic and relevant information about methodological triangulation and make a first approach to the principles underlying its use, potentiality and scope, advances and limitations, and some alternative proposals to surpass them. In that sense, it is an attempt to operationalize concepts and present the procedures to conduct it rigorously. In the first place, conceptual aspects and types of triangulation are presented, and in the second place, the principles, uses and difficulties. But, beyond what must be done, an approach is made to how to do it. The assumption underlying through the article is the complementarity among methods. It is emphasized in the principle through which the nature of objects must guide the selection of the methods and of the most effective techniques to approach and account for phenomena that are socially pertinent of being studied.

El presente artículo pretende levantar información básica y relevante sobre la triangulación metodológica y hacer una primera aproximación a los principios que subyacen en su uso, su potencialidad y alcance, sus avances y limitaciones, y algunas propuestas alternativas para superarlas. En ese sentido, es un intento de operacionalizar los conceptos y presentar los procedimientos para llevarla a cabo en forma rigurosa. En primer lugar, se presentan los aspectos conceptuales y los tipos de triangulación, y en segundo lugar los principios, los usos y las dificultades. Pero, más allá del qué hacer, se hace una aproximación al cómo hacerlo. El supuesto que subyace a través del artículo es la complementariedad entre los métodos. Se enfatiza en el principio mediante el cual, la naturaleza de los objetos debe guiar la escogencia de los métodos y de las técnicas más eficaces para aproximarse y dar cuenta de los fenómenos que son pertinentes socialmente, de ser estudiados.

Este artigo tem como objetivo coletar informações básicas e relevantes sobre triangulação metodológica e fazer uma primeira aproximação aos princípios que fundamentam sua utilização, seu potencial e alcance, sua avanços e limitações, e algumas propostas alternativas para superá-los. Nesse sentido, é uma tentativa de operacionalizar os conceitos e apresentar os procedimentos para realizá-lo com rigor. Em primeiro lugar, são apresentados os aspectos conceituais e os tipos de triangulação e, em segundo lugar, os princípios, usos e dificuldades. Mas, além do que fazer, é feita uma abordagem de como fazer. A hipótese subjacente ao longo do artigo é a complementaridade entre os métodos. A ênfase é colocada no princípio pelo qual a natureza dos objetos deve orientar a escolha dos métodos e técnicas mais eficazes para abordar e dar conta dos fenômenos socialmente relevantes, se estudados.

Introduction

According to Boudon, 1 for authors, like Dilthey, Rickert, Jaspers, and Max Weber, research in social sciences follows the path of understanding and the natural sciences through explanation, although for some, especially for Weber, both procedures, although distinct, are not exclusive. The same author found false opposition between the methods of the sciences, given our condition of social beings and the specificities of the human, through the diversity of objects and limitations of the methods, to account for complex phenomena of the social reality. For this author, it is naive to evaluate the methods of the social sciences with the unified parameters of the natural sciences, given that it would not be imaginable, for example, that History could be similar to Physics.

Quantitative research is supported on a set of established logical principles and should not be imposed from the outside for the researcher. Qualitative research also obeys an implicit but less unifiable logic. 1 The nature of the object and effectiveness of the methods will guide the researcher’s reflection to approach and account for phenomena that are pertinent, socially, of being studied. It must be highlighted that the methods are not the truth, they only constitute tools, procedures, instruments and modes of putting together the theory to investigate a problem and that when used facilitate its understanding; in that sense, the methodological triangulation will be treated as research procedure.

The term triangulation comes from navigation, where, from various angles, an object is situated; in this case, a ship. Thus, triangulation constructs several appendages, namely theoretical or methodological perspectives, several views or several readings, diverse points of view to address the same research problem. As explained by Morse, the discussion among authors has dealt on the appropriations, advantages, and disadvantages of methodological triangulation. 2 The issue that has gained greater interest is the combination of qualitative and quantitative methods within the same project. Some authors have published examples of how this is carried out within a specific project, identifying the issues involved in said strategies; others have identified unsolved issues or highlight the guidelines they consider successful and the less developed in the use of methodological triangulation.

This article sought to collect basic and relevant information about methodological triangulation and make a first approach over the principles underlying its use, potentiality and scope, its progress and limitations, as well as solution alternatives.

From triangulation of indicators and variables to theoretical and methodological triangulation: conceptual aspects

What is methodological triangulation? Triangulation is a term originally used in navigation circles by taking multiple reference points to locate an unknown position. Campbell and Fiske are credited in the literature as the first to apply triangulation in research in 1959. 3 It is assumed conventionally that triangulation is the use of multiple methods to study the same object. This is the generic definition, but it is only one form of the strategy. It is convenient to conceive triangulation including varieties of data, researchers and theories, as well as methodologies. 4

Kimchi et al ., 5 assume the definition by Denzin in 1970 on triangulation in research: it is the combination of two or more theories, sources of data, research methods, in the study of a singular phenomenon. Close scrutiny reveals that the combination can be interpreted in several manners; for such, the authors start from the classification by Denzin and provide explanations about the most adequate way of performing it.

For Cowman, 3 triangulation is defined as the combination of multiple methods in studying the same object or event to better address the phenomenon researched. In turn, Morse 2 defines methodological triangulation as the use of at least two methods, usually qualitative and quantitative, to guide the same research problem. When a singular research method is inadequate, triangulation can be used for a more comprehensive approach to solve the research problem.

Multiple triangulation strategies

Denzin 4 describes four basic types of triangulation: 1) data triangulation with three subtypes of time, space and person; the person analysis, in turn, has three levels: aggregate, interactive and collective; 2) researcher triangulation that consists in using multiple observers, more than single observers of the same object; 3) theoretical triangulation that consists in using multiple perspectives, more than single perspectives in relation with the same set of objects, and 4) methodological triangulation that can imply triangulation within methods and triangulations among methods.

Data triangulation 4

Denzin 4 illustrates this type of triangulation. For the author, observers can triangulate with data sources and researchers make explicit the search for the different sources. For example, analysts can employ, in efficient manner, the same methods for a maximum theoretical advantage. Thus, for example, in studying the social meaning of death in a modern hospital it may be possible to use a standard method (like participant observation, which, in strict manner would be technical) and deliberately follow this method in as many different areas as possible.

Researchers can observe different groups within the hospital and take the family members of the dead people. Death rituals can also be examined with the same process. Other examples are deaths on the road, deaths at home, deaths at work and even deaths at play. Each represents a different area of significance with which the same generic event (death) occurs. Basically, this could be used in a comparison of dissimilar groups as a sampling strategy, but more properly reflects a triangulation strategy. Selecting different collocations systematically, researchers can discover that its concepts (like assignment of reality units) share common issues. Similarly, the constituent unit of those concepts can be discovered in its contextual situation.

Furthermore, all sociological observations report activities of people situated socially -although they are in groups or organizations or distributed in groups in a social area -. Focusing time and space as observation units recognizes their relationship with the observations of people. Observers can make a sampling of activities according to time of day, week, month or year. Likewise, they can do it with space and treat it as an analysis unit (for example, ecological analysis), or as a component of external validity. The most-common analysis unit, the social organization of people can be sampled over time and space. Those three units -time, space and person- are interrelated. Studying one demands studying the others.

Levels of person analysis. Three levels of person analysis can be treated: 4

• Aggregate analysis. It is the first level; selecting individuals for the study, not groups, or relationships, or organizations. This level of analysis is called aggregate because it does not establish social relationships among that observed. Random samples of house workers, school students, and laborers are examples of aggregate analysis of persons.
• Interactive analysis. It is the second level and is related directly with the symbolic interaction. Regarding the term interactive, a unit exists among people interacting in the laboratory or in the natural field. For example, small groups, families or aviators. Sociologists commonly associate it with participant observation; experiments in small groups and non-obstructive measurements represent this form of analysis. The unit is the interaction more than person or group; for example, face-to-face studies by Goffman, who investigated in insurers, nurses and hospital social structure, only how they interact in the generation of series of interactive episodes.
• Collective analysis. The third level, more commonly associated with the structural-functional analysis, is the collectivity. Here, the observational unit is an organization, group, community or, even, an entire society. People and their interactions are treated only according with how they reflect pressures and demands of the total collectivity.

The three levels of analysis may be illustrated by returning to the example of death in hos pital. Research guided in aggregate manner can sample simply the attitudes of the hospital staff during the process. An interactional study can examine how those attitudes are generated by the encounters of the personnel. Lastly, the researcher aimed towards the collectivity can examine how the hospital’s structural units (for example, its organizational charter, job positions) dictate certain attitudes and practices by its members.

In synthesis, any research can combine the three levels and types of data; in effect, those studies commonly recall as classical events these combinations: time, space and person are alternatively analyzed in the aggregate, interactive, and collective levels.

Researcher triangulation 4

Researcher triangulation means multiple observers are used, rather than a single one. More researchers, in effect, conduct multiple observations, although not all play equally prominent roles in the process. Delegation at work can be established by placing well-prepared individuals in crucial positions. When using multiple observers, the most skilled should be placed near to the data. Upon triangulating observers, potential bias coming from single person is removed and considerable reliability is ensured in the observations.

There are various field workers subjected to the same data. If a colleague reports the same class of observation as another, without prior consultation, trust is increased. If later, listening to the report of an observation, a colleague contributes the same, unquestionably duplicates it; that indicates that our observation techniques have some degree of reliability.

Multiple observers may not agree on what they are observing, given that each observer has unique interactional experiences with the phenomenon observed. 4 Researcher triangulation is considered present when two or more trained researchers with divergent antecedents explore the same phenomenon. It is considered to take place when; 1) each researcher has a prominent role in the study, 2) the experience of each researcher is different, and 3) the disciplinary bias of each researcher is evident in the study. This definition, as the previous classifications, was elaborated and extended by Denzin in 1989, who stated that researcher triangulation occurs when two or more skilled researchers examine the data. The concern that stands out from researcher triangulation is that different disciplinary biases are compared or neutralized through the study. Overall, this is not discernible in a research publication. Researcher triangulation is difficult to distinguish, unless the authors describe explicitly how they achieved it.

Theoretical triangulation 4

Denzin defined theoretical triangulation as an evaluation of the usefulness and being able to test rival theories or hypotheses. This definition includes tests through research, rival theories, rival hypotheses or alternative explanations of the same phenomenon. Denzin placed as example the studies by Campbell of women’s responses toward abuse, which provide an example of theoretical triangulation. Two competitive models were tested in the same sample of women. Both were used previously to explain the women’s responses. The goal was to pit them against each other in a singular study to determine which one provides the best explanatory model of the phenomenon of abuse. The data collection approached was used to measure specific concepts and variables from each model. The report published placed the objective a priori, to the test of two opposing rival theories; this component is necessary to operationalize the theoretical triangulation.

Theoretical triangulation is an element few researchers manage and end up reaching. Overall, a small group of hypotheses guides the study and the data obtained emerge not only in those dimensions, rather they may appear with value, in empirical approach materials with multiple perspectives and interpretations in mind. Data could refute the central hypothesis and various theoretical points of view can take place to determine its power and usefulness. Each strategy can allow the contribution of criticism and controversy from several theoretical perspectives. Confronting theories in the same body of data means the presence of efficient criticism, more in line with the scientific method. This last issue can be qualified by understanding, for example, that sociologists never have the same body of data; this means that a body of data of empirical materials is always socially constructed and subject to multiple interpretations.

Methodological triangulation

Triangulation of methods using two or more research methods can be made in the design or in the data collection. Two types exist, triangulation within methods and among methods. 4

Triangulation within methods is the combination of two or more data collections to approach the study of the same object; using two or more quantitative measurements of the same phenomenon in a study is an example. Including two or more qualitative approaches, like the observation and open interview to assess the same phenomenon, is also considered triangulation within methods. Observational data and interview data are coded and analyzed separately, and then compared, as a way of validating the findings.

This form is used more frequently when the observational units are seen as multidimensional. Researchers take a method (from safety) and employ multiple strategies to examine the data. A safe questionnaire can be constructed with different measurement scales for the same empirical unit. For example, in the famous case of the alienation scales, several recent investigations have used five different indices. The obvious difficulty is that only one method is employed. Observers are mistaken if they believe that five different variations on the same method generate five triangulation varieties.

Moreover, each class of data generated -interviews, questionnaires, observation and physical evidence- is potentially biased and its specificity may be threatened. Ideally, data should converge, i.e. , they should not contradict, although conserving their multiple variations.

Triangulation among methods is a more sophisticated way of combining triangulation of dissimilar methods to illuminate the same class of phenomena; it is called among methods or triangulation through methods. The rationale in this strategy is that the weaknesses of a method constitute the strengths of another; and with a combination of methods, observers reach the best of each, overcome its weakness. Triangulation among methods can take several forms, but its basic characteristic can be the combination of two or more research strategies in studying the same empirical unit or several.

With seven research methods on research design -that in a stricter sense, would be techniques, a variety of combinations can be constructed. 1 , 2 Completely triangulated research can combine them all. Besides, if the basic strategy was participant observation, researchers can employ safe interviews with field experiments, non-obtrusive methods, filming, and life stories. Most sociological research can be seen to emphasize a dominant method, with combinations of other additional dimensions.

Kimchi et al ., state in their article Denzin’s classification and add explanations about the most adequate way of conducting the triangulation. 5 In their opinion, the specificity and the step-by-step procedures to implement the triangulation should be addressed. The purpose of their work was to present operational definitions for the types of triangulation described by Denzin in an effort to clarify the triangulation and attract researchers. Based on the theoretical definitions by Denzin, these show a group of operational definitions of the types of triangulation. The definitions seek to clarify, specify, and provide indicators that research readers can use if they deem there has been triangulation. Operational definitions were made by Kimchi during a review of all the data on which 319 articles were based from six nursing research journals published during 1986 and 1987. The six journals were: Advances in Nursing Science, Image, Inter national Journal of Nursing Studies, Nursing research, Research in Nursing and Health, Wes tern Journal of Nursing Research. The following presents some operational definitions.

- Data triangulation. 5 Considered as the use of multiple data sources to obtain diverse visions about a topic for the purpose of validation. Temporal triangulation represents data collection of the same phenomenon during different points over time, as already exposed; in these studies, time is relevant. Longitudinal studies are not considered temporal triangulation because the aim of a longitudinal study is to document changes over time and the purpose of temporal triangulation is to validate the congruence of the same phenomenon through different points over time.

- Spatial triangulation. 5 It is data collection of the same phenomenon in different sites. Space must be the central variable. Studies in which data are collected in multiple sites, but do not cross, are not considered spatial triangulation. In spatial triangulation, data are collected in two or more scenarios and tests of consistency are analyzed by crossing the sites.

-Person triangulation. 5 It is data collection from, at least, two of the three levels of person: individuals, couples, families, groups or collectives (communities, organizations or societies). Researchers can collect data from individuals, couples and groups, or each of the three types. Data collection from a source is used to validate data from the other sources or a single one. Kimchi, Polivka and Stevenson set as example the work by Hutchinson who, in 1987, studied the process of dependency on recovery ward nurses on two levels. Data were collected weekly from meetings of groups of recovery nurses over one year (group level) and in selection interviews (individual level). The phenomenon of interest was the recovery process. Each data level was used to validate the findings of the other.

- Multiple triangulation. 5 This occurs when using more than one type of triangulation in analyzing the same event, contributing more comprehensive and satisfactory sense of the phenomenon 4 ; as mentioned, it is the combination of two or more types of triangulation in a study. Using triangulation within methods and researcher triangulation in a study or using triangulation within methods and among methods in a study are two examples of multiple triangulation. Kimchi et al ., give as an example the study by Wallson et al ., which combined researcher triangulation and triangulation within methods. The group represents a multidisciplinary mix of researchers and study goals reflected on distinct values from different disciplines. Triangulation within methods was evidenced by the use of three measures of stress, each used to validate the others, a psychological measure and two written tests.

Triangulation in the analysis, a more recent type of development, is the use of two or more approaches in the analysis of the same data group for validation purposes. It is conducted by comparing data analysis results, using different statistical tests or different techniques of qualitative analysis to evaluate similarly the results available. It serves to identify similar patterns and, thus, verify the findings. Use of divergent methods of data analysis for cross-validation purposes constitutes another triangulation potential. For Denzin, 4 “ the greatest goal of triangulation is to control the personal bias of researchers and cover the intrinsic deficiencies of a single researcher or a unique theory, or the same method of study and, thus, increase the validity of the results ”.

- Combination of results: Morse 2 agrees with Mitchell in that the problem of the weight of the results of each component is solved if the findings are interpreted within the context of present knowledge. Each component should fit as a piece in a puzzle. The essential is the process of informed thought, judgment, wisdom, creativity, and reflection, and includes the privilege of modifying the theory, this is the exciting part of each research project and when there is triangulation of different methods, this is particularly exciting. If contradictory results occur from the triangulation of qualitative and quantitative methods, then a group of findings is invali d or the total result of the study is inadequate, incomplete or imprecise or both. If the study was guided deductively, the theoretical map may be incorrect.

Implementing the methodological triangulation

The methodological triangulation can be classified as simultaneous or sequential. 2 The first, when using qualitative and quantitative methods at the same time. In that case, the interaction between both data groups during the collection is limited, but the findings complement each other at the end of the study. Sequential triangulation is used if the results of a method are essential to plan another method. The qualitative method is completed before implementing the quantitative method or vice versa.

Thus, according to Morse, 2 in the methodological triangulation, the key issue is if the theory, which guides the research, is developed inductively or is used deductively, as in the quantitative inquiry. From this differentiation, various types of methodological triangulation result. If the research is directed by an inductive process and the theory is developed qualitatively and is complemented through quantitative methods, the QUAL + quan notation is used to indicate simultaneous triangulation. If the project is deductive, directed by a conceptual map a priori, the quantitative methods take precedence and can be complemented with qualitative methods. In that case, the QUAN + qual notation is used . The sequential triangulation is indicated by QUAL -› quan with an inductive project, that is, when the theoretical direction is inductive and uses a qualitative foundation. Using the QUAN -› qual notation indicates a deductive approach; that is, when we follow the complete quantitative steps and the qualitative method is used to examine or explore unexpected encounters.

The purpose of the article by Morse 2 was to explore the principles underlying the use of methodological triangulation when combining qualitative and quantitative methods. Those principles are related with the consistency among the research purpose, research problem, method used, sample selection, and interpretation of the results. The author coincides with Mitchell who highlights five areas of concern: 1) difficulty to combine text and numerical data; 2) interpretation of divergent results obtained from using qualitative and quantitative methods; 3) success or not in delineating and mixing the concepts; 4) weight of the information from different data sources, and 5) difficulty of guessing the contribution of each method when the results are similar.

The first step in the quantitative-qualitative triangulation is to determine the nature of the research problem, if it is “natural” or “social”, which aims towards a primarily quantitative or qualitative approach. Characteristics of a qualitative research problem: 1) the concept under study is immature due to weak success and conspicuous theory and prior research; 2) a notion that the available theory may be inappropriate, incorrect or biased; 3) a need exists to explore and describe the phenomenon and develop theory, or 4) the nature of the phenomenon is not appropriate for quantitative measurements.

If a research problem is quantitative, the characteristics described are not applicable. Researchers can locate substantial and relevant literature about the topic, create a conceptual map, and identify hypothesis to test. In this case, the research design is comparative or correlational, experimental or quasi-experimental.

The qualitative and quantitative aspects of a research project cannot be weighed equally: besides, a project must be guided theoretically by qualitative methods incorporating a complementary quantitative component, or guided theoretically by a quantitative method incorporating a complementary qualitative component. The important point is that each method must be complete in itself, that is, all the methods used must appropriate rigor criteria. If qualitative interviews are conducted, this must be done as if this method were alone. The interviews must continue while saturation is reached, and the content analysis has to be carried out inductively, more than forcing the data within a category preconceived for the study.

Further, triangulation may be used with different objectives, among them, the following:

• Triangulation is linked by many authors with rigor and quality; in that sense, one of the expectations is to increase research rigor, 6 thus, Flick 7 highlights triangulation as “a way to promote quality in research”.
• Triangulation as verification: for Patton, 8 studies using multiple methods that analyze different types of data “provide cross validation”. A les common use of triangulation is to ensure the validity of the instruments. However, this approach should be cautious, testing an instrument before its implementation or establishing its validity during the pilot test.
• Triangulation as completeness: for Patton 8 “(…) qualitative and quantitative data can be combined fruitfully when these elucidate complementary aspects of the same phenomenon”.
• Interdisciplinarity: Flick 9 proposes the possibility of conducting a “systematic triangulation of perspectives”, which may imply “researcher triangulation as collaborative strategy”; this opens the possibility addressing at least the multi- or interdisciplinarity; as proposed by Janesick: 10 I would wish to add a fifth type: “interdisciplinary triangulation”.

In synthesis, following Molina, 11 triangulation can “(…) expand the research process to contribute to deeper and broader comprehension of the phenomenon, given that it adds “(…) rigor, amplitude, complexity, richness, and depth to any research”.

Mixed methods in research -perspective under development and emerging since the 1990s- emphasize on integrating different data sets, as highlighted by Creswell. 12 The author starts from the labels and notations exposed by Morse who was the precursor of said nomenclature and Creswell proposes it to differentiate design categories or typologies possible to apply in said methods. 12 Said combined methods “have extended rapidly through social and behavioral sciences”, as stated by Timans, Wouters, and Heilbron 13 and “have developed linked to the triangulation concept”. 12 Some authors denominate the singularly as mixed method.

The complementarity of methods

Defining qualitative research as development of theories and generation of hypothesis, and quantitative research as modification of theories and tests of hypothesis, Field and Morse have identified the complementarity of both approaches.

For Morse, 2 the biggest threat to validity is the use of inadequate or inappropriate samples. Perhaps due to reasons of convenience, researchers have sought to use the same subjects for both methods, qualitative and quantitative, although it is clearly inappropriate to exchange those samples. For example, quantitative research is based on large representative samples of the population randomly selected; adjustment of the sample is determined statistically, as well as its representativity of the whole population. In qualitative research, appropriation is in relation to how well the sample can represent the phenomenon of interest (for example, how much have the participants experienced the phenomenon and can articulate their experiences); the sample will be adequate when data saturation is enriched. Still, in light of the overall purpose of research, no reason exists (different from convenience) to use the same subjects for both samples.

Clearly, when incorporating quantitative methods within a qualitative study, the qualitative sample may be inadequate for quantitative purposes. Lack of representativity of the qualitative sample selected in purpose is inappropriate and threatens the validity. Selection of the sample through the qualitative and quantitative components of a sequential ( QUAL -› quan ) or simultaneous ( QUAL + quan ) triangulation must be independent. Because the quantitative sample is inadequate and inappropriate for quantitative purposes, researchers must design a quantitative sample for the population. However, when the quantitative method is used to add more information about the qualitative sample ( QUAL + quan ), exceptions can be made if the norms so permit, or if a comparison is available of a normal group, to interpret the results. For example, if dealing with the anxiety of the relatives in the waiting room, the anxiety scales can be interpreted with the norms available for anxiety scales.

A subsample may be used from a large quantitative sample for the qualitative component of the QUAN + qual or QUAL -› quan triangulation , but those subjects included or the incidental observations in the qualitative part must be selected according with the criterion of good participants than through random selection. Thereby, the subjects selected for the quantitative sample must have greater experience and articulation, and the observations selected must consider the best examples of the situation.

Methodological triangulation is not a term applied to ethnography when the research method includes the use of semi-structured interviews, some levels of participant observation, use of recordings, and administration of questionnaires. It is the combination of said techniques that constitutes the ethnography and what makes ethnography, ethnography. It is not the case of blending or integrating guides from both texts, qualitative and quantitative, rather, it is using appropriate strategies to maintain the validity of each method. The QUAN + qual triangulation is not only the addition of linguistic and narrative data in an experimental design; at least, the interview data must be collected and analyzed according with the assumptions and principles of the qualitative method. Similarly, incorporating one or two open questions within the quantitative survey does not make study qualitative.

Additionally, using quantitative data in a qualitative study (like frequency data to improve the description), does not constitute a quantitative study. Methodological triangulation is not a technique to use due to rapidity and convenience in the research. Well done, it will likely lengthen the duration of the project, but the gains reached in the long term are immensurable.

Methodological triangulation is not a concurrent validation technique. Although the same strategies may be used, these are implemented in a study for different motives. The purpose of the concurrent validation is to find if the results of measuring the same concept through both methods are equivalent. The purpose of simultaneous triangulation is to obtain different but complementary data on the same topic, more than replicating the results.

According to Knafl, methodological triangulation is not merely to maximize the strength and minimize the weakness of each method. If a careful approach is not made, the end result may be to broaden the weakness of each method and invalidate completely the research project. It is more a method to obtain complementary findings and contribute to the theory and development of knowledge.

Some of the controversies of methodological triangulation have emphasized on the issue of qualitative research against quantitative. This controversy advocates for the combination of methods inasmuch as it is consistent with theoretical research. Some researchers forget that research methodologies are only tools, instruments that when used facilitate understanding. Researchers should be versatile and have a repertoire of methods available. To broaden the foregoing, a summary is presented of the discussion by Cowman about the paradigms and the author’s proposal regarding triangulation. 3

Quantitative approach was the dominant paradigm from 1950 until 1990; the research approach - in turn - has been increasingly localized on the qualitative paradigm. Within the literature there is general support to separate both paradigms. However, accepting the inherent differences between the two, researchers are concerned that no isolated method can provide understanding of human beings and of their complex needs. Triangulation, as research strategy, represents the integration of two research approaches. The literature that explores its merits in research is incomplete, however, it is reported that triangulation, by reconciling the paradigmatic assumptions of quantitative and qualitative methods, provides richness and productive data. Triangulation offers a bipolar alternative and approaches the quantitative and qualitative. The qualitative-quantitative debate is still in development. It should be noted that each research perspective has several inherent differences. The quantitative approach has been associated exclusively with the dominant empirical-analytical paradigm and sees the causes of human behavior through observations that seek to be objective and collects quantifiable data. More often, research methods are associated with experimental research designs, which examine the causal relations among variables, controlled or removed from their natural scenario and observations are quantified and analyzed through statistically determined probabilities.

Quantitative research holds the methodological assumption that the social world looks at itself through objective forms of measurement. Conversely, Leininger 1985 suggests that people are not reducible to measurable objects and that they do not exist independently of their historical, social, and cultural context. The qualitative paradigm emerges from a tradition in sociology and anthropology, techniques to obtain qualitative data permit observing the world from the perspective of the subject, not the researcher. The qualitative paradigm is concerned for the value of the meaning and for the social world from which this meaning derives; through a variety of theoretical perspectives and research traditions that include phenomenology and ethnography, natural and family data are valued and serve to gain understanding of people. Differences between quantitative and qualitative approaches can be seen, even at the most basic level. The qualitative approach develops theory inductively from the data; in quantitative research, it is done deductively and its methods are encouraged primarily as a theory subjected to statistical tests, that is, falsifiable in Popperian terms.

Knowing the natural difficulties of research quantitative and qualitative methods and having identified the need to integrate the research approaches, the triangulation strategy is proposed. Cowman 3 accepts four principles underscored by Mitchell, 14 which, applied carefully, point to maximizing the validity of a particular research, incorporating the methodological triangulation: 1) the research question must be clearly focused, 2) the strengths and weaknesses of each method chosen must complement the other, 3) methods must be selected according with their relevance for the nature of the phenomenon under study, and 4) a continuous evaluation must be performed of the method selected during the course of the research to monitor if the three previous principles are being followed. These consistency elements also apply in mixed methods.

Cowman 3 also warns of possible difficulties of triangulation: in first instance, a researcher, accepting the advantages of triangulation, can lose sight of differences between the methods chosen. Danger exists in collecting large volumes of data, which - subsequently - it will not be possible to analyze or are dealt with superficially. Fielding and Fielding emphasized on the danger of taking multiple methods without using simultaneously the bias control procedure.

Moreover, triangulation provides strengths, like animation, creativity, flexibility, and depth in data collection and analysis; as indicated by Cohen and Manion, methodologists often push methods as pets because those are the only methods with which they are familiar or because they believe that their method is superior to all the rest. Reichardt and Cook suggest that it is time to stop constructing walls between methods and start building bridges.

Given that the methods need independence within a single project, the real issue in triangulation can go beyond incompatibility between different assumptions of two paradigms, as argued by several researchers. It also assumes the possible incompatibility of contrasting philosophical issues, of static and dynamic realities, of objective and subjective perspectives, of inductive and deductive approaches or of integral and particular visions. It is not the elusive mix of numerical and text data or of simultaneous considerations of antagonistic approaches of causality and non-causality. Integration of data does not occur in the analysis process, but in the union of the results of each study within a cohesive and coherent product where the confirmation or revision of the existing theory takes place. This can be achieved through adhesion to the rules and assumptions of each method in selecting the sample, purpose, method, and the contribution of the results within the research plan as a whole.

* How to cite this article: Arias Valencia MM. Principles, Scope, and Limitations of the Methodological Triangula-tion. Invest. Educ. Enferm. 2022; 40(2):e03.

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Triangulation in Qualitative Research: A Comprehensive Guide [2024]

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

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

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

What is Triangulation in Qualitative Research?

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

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

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

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

What are the 4 Types of Triangulation?

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

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

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

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

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

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

What is the Purpose of Data Triangulation in Qualitative Research?

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

1. Confirm the accuracy and consistency of your information

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

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

4. Strengthen the overall validity and trustworthiness of your research

Limitations of Triangulation

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

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

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

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

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

What is an Example of Triangulation in an Experiment?

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

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

- Collect quantitative data on student performance using standardized tests

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What are the Best Tools for Researcher Triangulation?

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

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

1. Looppanel

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

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

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

Customer Quote:

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

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

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

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

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

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

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

3. Excel or Miro

Strapped for budget?

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

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

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

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

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

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

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

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

Is triangulation the same as mixed methods?

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

What is investigator triangulation in thematic analysis?

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

What is the difference between data triangulation and method triangulation?

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

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

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

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

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Triangulation in Research | Guide, Types, Examples

Published on 8 April 2022 by Pritha Bhandari . Revised on 16 January 2023.

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 . If you decide on mixed methods research , you’ll always use methodological triangulation.

• Qualitative research: You conduct in-depth interviews with different groups of stakeholders, such as parents, teachers, and children.
• Quantitative research: You run an eye-tracking experiment and involve three researchers in analysing the data.
• Mixed methods research: You conduct a quantitative survey, followed by a few (qualitative) structured interviews.

Table of contents

Types of triangulation in research, what is the purpose of triangulation, pros and cons of triangulation in research, frequently asked questions about triangulation.

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

We’ll walk you through the four types of triangulation using an example. This example is based on a real study .

Methodological triangulation

When you use methodological triangulation, you use different methods to approach the same research question.

This is the most common type of triangulation, and researchers often combine qualitative and quantitative research methods in a single study.

Methodological triangulation is useful because you avoid the flaws and research bias that come with reliance on a single research technique.

Data triangulation

In data triangulation, you use multiple data sources to answer your research question. You can vary your data collection across time, space, or different people.

When you collect data from different samples, places, or times, your results are more likely to be generalisable to other situations.

Investigator triangulation

With investigator triangulation, you involve multiple observers or researchers to collect, process, or analyse data separately.

Investigator triangulation helps you reduce the risk of observer bias and other experimenter biases.

Theory triangulation

Triangulating theory means applying several different theoretical frameworks in your research instead of approaching a research question from just one theoretical perspective.

• People cooperate for a sense of reward: they cooperate to feel good.
• People cooperate to avoid guilt: they cooperate to avoid feeling bad.

Testing competing hypotheses is one way to perform theory triangulation. Using theory triangulation may help you understand a research problem from different perspectives or reconcile contradictions in your data.

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Researchers use triangulation for a more holistic perspective on a specific research question. Triangulation is also helpful for enhancing credibility and validity.

To cross-check evidence

It’s important to gather high-quality data for rigorous research. When you have data from only one source or investigator, it may be difficult to say whether the data are trustworthy.

But if data from multiple sources or investigators line up, you can be more certain of their credibility.

Credibility is about how confident you can be that your findings reflect reality. The more your data converge, or or agree with each other, the more credible your results will be.

For a complete picture

Triangulation helps you get a more complete understanding of your research problem.

When you rely on only one data source, methodology, or investigator, you may risk bias in your research. Observer bias may occur when there’s only one researcher collecting data. Similarly, using just one methodology means you may be disadvantaged by the inherent flaws and limitations of that method.

• Behavioral observations from a lab setting
• Self-report survey data from participants reflecting on their daily lives
• Neural data from an fMRI scanner during a cooperative task

It’s helpful to use triangulation when you want to capture the complexity of real-world phenomena. By varying your data sources, theories, and methodologies, you gain insights into the research problem from multiple perspectives and levels.

To enhance validity

Validity is about how accurately a method measures what it’s supposed to measure.

You can increase the validity of your research through triangulation. Since each method has its own strengths and weaknesses, you can combine complementary methods that account for each other’s limitations.

Finally, fMRI data can tell you more about hidden neural mechanisms without any participant interference. But this type of data is only valuable for your research when combined with the others.

Like all research strategies, triangulation has both advantages and disadvantages.

Reduces bias

Triangulating data, methods, investigators, or theories helps you avoid the bias that comes with using a single perspective in your research. You’ll get a well-rounded look into the research topic when you use triangulation.

Establishes credibility and validity

Combining different methods, data sources, and theories enhances the credibility and validity of your research. You’ll be able to trust that your data reflect real life more closely when you gather them using multiple perspectives and techniques.

Time-consuming

Triangulation can be very time-consuming and labour-intensive. You’ll need to juggle different datasets, sources, and methodologies to answer one research question.

This type of research often involves an interdisciplinary team and a higher cost and workload. You’ll need to weigh your options and strike a balance based on your time frame and research needs.

Inconsistency

Sometimes, the data from different sources, investigators, methods may not line up to give you a clear picture. Your data may be inconsistent or contradict each other.

This doesn’t necessarily mean that your research is incoherent. Rather, you’ll need to dig deeper to make sense of why your data are contradictory. These inconsistencies can be challenging but may also lead to new avenues for further 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.

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

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.

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Chapter 28: Triangulation

Tess Tsindos

Learning outcomes

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

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

What is triangulation?

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

There are four main types of triangulation 2 :

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

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

Table 28.1: Examples of triangulation

How to conduct triangulation

How triangulation is conducted depends on the type of triangulation.

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

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

Advantages and challenges of triangulation

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

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

• Patton MQ. Enhancing the quality and credibility of qualitative analysis. Health Sciences Research . 1999:34, 1189-1208.
• Denzin NK. Sociological Methods: A Source Book (2nd ed). Mcgraw-Hill: 1978.
• Shemesh B et al. Codesigning a patient support portal with health professionals and men with prostate cancer: an action research study. Health Expect . 2022:25, 1319-1331. doi /10.1111/hex.13444
• Cohen L, Manion L, Morrison K. Research M ethods in E ducation . Routledge; 2017.
• Yeh A, Trang P. Avicii’s S.O.S.: a psychobiographical approach and corpus-based discourse analysis on suicidal ideation. Psychology of Language and Communication . 2022;26(1):207-241.  doi.10.2478/plc-2022-0010
• Lundell S, Pesola U et al .  Groping around in the dark for adequate COPD management: a qualitative study on experiences in long-term care.  BMC Health Serv Res .  2020;20:1025. doi . 10.1186/s12913-020-05875-2
• McCrone L & Kingsbury M. Combining worlds: a mixed method for understanding learning spaces. Int J Qual Methods . 2023;22.  doi : 10.1177/16094069231173781
• Johnson M, O’Hara R et al  Multiple triangulation and collaborative research using qualitative methods to explore decision making in pre-hospital emergency care. BMC Med Res Methodol . 2017;17 ( 11). doi : 10.1186/s12874-017-0290-z
• Llewellyn-Beardsley J et al “Nothing’s changed, baby”: how the mental health narratives of people with multiple and complex needs disrupt the recovery framework. SSM – Ment Health. 2023;3(100221). doi : 10.1016/j.ssmmh.2023.100221
• Ayton D et al. Barriers and enablers to the implementation of the 6-PACK falls prevention program:  pre-implementation study in hospitals participating in a cluster randomised controlled trial. PLOS ONE . 2017;12. doi: 10.1371/journal.pone.0171932

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

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Triangulation in qualitative research

Qualitative methods are sometimes criticised as being subjective, based on single, unreliable sources of data. But with the exception of some case study research, most qualitative research will be designed to integrate insights from a variety of data sources

Daniel Turner

Triangles are my favourite shape,  Three points where two lines meet alt-J

Qualitative methods are sometimes criticised as being subjective, based on single, unreliable sources of data. But with the exception of some case study research, most qualitative research will be designed to integrate insights from a variety of data sources, methods and interpretations to build a deep picture. Triangulation is the term used to describe this comparison and meshing of different data, be it combining quantitative with qualitative, or ‘qual on qual’.

I don’t think of a data in qualitative research as being a static and definite thing. It’s not like a point of data on a plot of graph: qualitative data has more depth and context than that. In triangulation, we think of two points of data that move towards an intersection. In fact, if you are trying to visualise triangulation, consider instead two vectors – directions suggested by two sources of data, that may converge at some point, creating a triangle. This point of intersection is where the researcher has seen a connection between the inference of the world implied by two different sources of data. However, there may be angles that run parallel, or divergent directions that will never cross: not all data will agree and connect, and it’s important to note this too.

You can triangulate almost all the constituent parts of the research process: method, theory, data and investigator.

Data triangulation, (also called participant or source triangulation) is probably the most common, where you try to examine data from different respondents but collected using the same method. If we consider that each participant has a unique and valid world view, the researcher’s job is often to try and look for a pattern or contradictions beyond the individual experience. You might also consider the need to triangulate between data collected at different times, to show changes in lived experience.

Since every method has weaknesses or bias, it is common for qualitative research projects to collect data in a variety of different ways to build up a better picture. Thus a project can collect data from the same or different participants using different methods, and use method or between-method triangulation to integrate them. Some qualitative techniques can be very complementary, for example semi-structured interviews can be combined with participant diaries or focus groups, to provide different levels of detail and voice. For example, what people share in a group discussion maybe less private than what they would reveal in a one-to-one interview, but in a group dynamic people can be reminded of issues they might forget to talk about otherwise.

Researchers can also design a mixed-method qualitative and quantitative study where very different methods are triangulated. This may take the form of a quantitative survey, where people rank an experience or service, combined with a qualitative focus group, interview or even open-ended comments. It’s also common to see a validated measure from psychology used to give a metric to something like pain, anxiety or depression , and then combine this with detailed data from a qualitative interview with that person.

In ‘theoretical triangulation’, a variety of different theories are used to interpret the data, such as discourse, narrative and context analysis, and these different ways of dissecting and illuminating the data are compared.

Finally there is ‘investigator triangulation’, where different researchers each conduct separate analysis of the data, and their different interpretations are reconciled or compared. In participatory analysis it’s also possible to have a kind of respondent triangulation, where a researcher is trying to compare their own interpretations of data with that of their respondents.

While there is a lot written about the theory of triangulation, there is not as much about actually doing it ( Jick 1979 ). In practice, researchers often find it very difficult to DO the triangulation: different data sources tend to be difficult to mesh together, and will have very different discourses and interpretations. If you are seeing ‘anger’ and ‘dissatisfaction’ in interviews with a mental health service, it will be difficult to triangulate such emotions with the formal language of a policy document on service delivery.

In general the qualitative literature cautions against seeing triangulation as a way to improve the validity and reliability of research, since this tends to imply a rather positivist agenda in which there is an absolute truth which triangulation gets us closer to. However, there are plenty that suggest that the quality of qualitative research can be improved in this way, such as Golafshani (2003) . So you need to be clear of your own theoretical underpinning: can you get to an ‘absolute’ or ‘relative’ truth through your own interpretations of two types of data? Perhaps rather than positivist this is a pluralist approach, creating multiplicities of understandings while still allowing for comparison.

It’s worth bearing in mind that triangulation and multiple methods isn’t an easy way to make better research. You still need to do all different sources justice: make sure data from each method is being fully analysed, and iteratively coded (if appropriate). You should also keep going back and forth, analysing data from alternate methods in a loop to make sure they are well integrated and considered.

Qualitative data analysis software can help with all this, since you will have a lot of data to process in different and complementary ways. In software like Quirkos you can create levels, groups and clusters to keep different analysis stages together, and have quick ways to do sub-set analysis on data from just one method. Check out the features overview or mixed-method analysis with Quirkos for more information about how qualitative research software can help manage triangulation.

References and further reading

Carter et al. 2014, The use of triangulation in qualitative research, Oncology Nursing Forum, 41(5), https://www.ncbi.nlm.nih.gov/pubmed/25158659

Denzin, 1978 The Research Act: A Theoretical Introduction to Sociological Methods, McGraw-Hill, New York.

Golafshani, N., 2003, Understanding reliability and validity in qualitative research, 8(4), https://nsuworks.nova.edu/cgi/viewcontent.cgi?article=1870&context=tqr

Bekhet A, Zauszniewski J, 2012, Methodological triangulation: an approach to understanding data, Nurse Researcher, 20 (2), https://journals.rcni.com/doi/pdfplus/10.7748/nr2012.11.20.2.40.c9442

Jick, 1979, Mixing Qualitative and Quantitative Methods: Triangulation in Action,  Administrative Science Quarterly, 24(4),   https://www.jstor.org/stable/2392366

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Using data to predict and support the future Royal Navy 

Helping the Royal Navy maintain a skilled and capable workforce amid rapid technological advancements and demographic shifts.

The Royal Navy needs to maintain a skilled and capable workforce in the face of rapid technological advancements and demographic shifts so that it can draw on the right people with the right skills to meet the demands of modern warfare. 

The Accelerated Capability Environment ( ACE ) was asked to explore how available data – including open-source datasets - could be used to understand what the Navy’s workforce will look like over the next five to ten years as well as to aid reduction of staff turnover. This would allow procurement strategies and recruitment campaigns to be proactively adapted to address any emerging talent gaps in critical areas such as engineering and digital skills as well as ensure skilled and experienced personnel stay as long as possible. 

Over a seven-week commission, a joint team from Butterfly Data and Cranfield University were selected to conduct research into changing demographics. The team carried out interviews with Royal Navy human resources ( HR ) staff and analysed Navy and open-source data including the Office for National Statistics ( ONS ) population and community data, ONS labour statistics, Ministry of Defence statistics and jobs and skills data from the Department for Education.  

A changing world

Analysis indicated several demographic and societal shifts impacting the potential Navy workforce. The population is aging, leading to a decline in individuals meeting eligibility requirements. Conversely, educational attainment is rising, with more individuals achieving GCSEs and A-levels. However, societal values are evolving, with job seekers prioritising meaningful work and environmentally responsible employers. The transformative nature of industries, such as the rise of autonomous vehicles, necessitates a higher skill set among future employees. 

Recommendations for the Royal Navy to adapt to this include focusing recruitment on the eligible population, and widening recruitment focus beyond science, technology, engineering and maths ( STEM ) to the most popular subjects. 

Reducing staff turnover

A second element of the commission focusing on whether machine learning ( ML ) could be used on Royal Navy personnel records was undertaken solely by Butterfly Data.  The aim was to use data sources including exit interviews to explore whether these could explain common causes of people leaving.

Developing representative synthetic data and a proof of concept they were able to show it was possible to identify drivers for staff attrition using artificial intelligence/ ML , which would enable Royal Navy HR to see how staff could be better supported, and whether there are trends or trigger points where more effective intervention or greater support could make a difference. 

This has enabled the Navy to take tangible steps towards ML use within the personnel and training area, as well as the opportunity to work with a small, non-traditional supplier who they were not familiar with.

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How the US government is addressing suicide prevention

The U.S. Department of Health and Human Services has previously described suicide as "an urgent and growing public health crisis."

During Suicide Prevention Month, the department is emphasizing its 2024 National Strategy for Suicide Prevention which outlines strategies for community-based suicide prevention, treatment and crisis services, surveillance and research and health equity in suicide prevention.

Deputy Secretary of Health and Human Services Andrea Palm appeared on Scripps News Morning Rush to talk about her department's national strategy on World Suicide Prevention Day.

RELATED STORY | Male construction workers face 75% higher suicide rate than general population

"There are lots of ways that we as the government are looking to lean in and we want to make sure we understand what works," Palm explained, adding that they want to support best practices that are evidence-based.

Palm said treating people like human beings and meeting individuals where they are at is a great place to start if you have a loved one who appears to be struggling.

"It's just as simple as 'I care about you. It seems like there's something going on. Can I help you?'" Palm explained. "It really doesn't have to be complicated."

This week is National Construction Suicide Prevention Week, and resources are available for free here . If you or someone you know needs help, call, text, or chat 988 for the Suicide and Crisis Prevention Lifeline .

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