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Denzin Triangulation

Denzin identified four types of triangulation that can be used in qualitative research: 1) Data triangulation, which involves collecting data from different people, times, and locations to find common themes. 2) Investigator triangulation, which uses multiple researchers to study the same phenomenon to reduce bias. 3) Theory triangulation, which applies different theories and perspectives to analyze the data. 4) Methodological triangulation, which involves using multiple data collection methods, such as interviews and observations, either within the same method or across methods. The goal is a deeper understanding of the phenomenon being studied.

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2K views2 pages

Denzin Triangulation

Denzin identified four types of triangulation that can be used in qualitative research: 1) Data triangulation, which involves collecting data from different people, times, and locations to find common themes. 2) Investigator triangulation, which uses multiple researchers to study the same phenomenon to reduce bias. 3) Theory triangulation, which applies different theories and perspectives to analyze the data. 4) Methodological triangulation, which involves using multiple data collection methods, such as interviews and observations, either within the same method or across methods. The goal is a deeper understanding of the phenomenon being studied.

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Denzin (1989) noted that triangulation involves the employment of multiple external data

collection methods concerning the same events may be enhanced by multiple external analysis
methods. Triangulation is one method by which the researcher analyzes data and then presents
the results to others to understand the experience of a common phenomenon.

Four Types of Triangulation


Denzin (1970, 1978) built on the notion of triangulating multiple sources of data and developed
four types of triangulation that qualitative researchers can use to enhance the enhance objectivity,
truth, and validity (dependability and credibility) of social research.

 Data triangulation
Correlating people, time, and space. These three data points (not methods to generate
data) are inter-related and ongoing. Each data point represents different data of the same
event; discovering commonalities within dissimilar settings. Furthermore, the data points
take place over time to observe ongoing interactions—days, weeks, months, years.

 Investigator triangulation
Correlating the findings from multiple researchers in a study. There is more than one
investigator/researcher exploring the phenomenon. This does not include coders, graduate
students/assistants, or data analysts. Rather, the persons with the best skills should be
closest to the data. Bias is mitigated by different investigators observing the same data
who may not agree on its interpretation

 Theory triangulation
Correlating the findings from multiple researchers in a study. In theory triangulation, one
applies different theories and alternative theories to the data set. Particularly, one views
the data through a theoretical lens and through contradictory theories. Another strategic
approach is to let the raw data speak to the researcher to ascertain a new theory. The
point, as noted by Denzin (2009), is to widen one’s theoretical lens through a six-step
process that expands one’s knowledge of the known.

 Methodological triangulation
Correlating data from multiple data collection methods. Denzin noted that
methodological triangulation can be within method or between method (also known as
across method), although the generally understood type is within method, such as
multiple sources of data found within one design. For example, triangulating the data
from multiple data collection methods (interviews, focus groups, observations, etc.) in a
qualitative case study or ethnography would be within-method triangulation, whereas
triangulating the data from a combination of quantitative and qualitative techniques in a
mixed-methods study would be betweenmethod (or across method) triangulation. The
challenge is that the inherent flaws within one method are still present and can impact the
data. The ideal application, Denzin (2009) stated, is betweenmethod triangulation to
account for flaws and deficiencies. Between-method triangulation takes the best of both
to overcome the weakness of each. In-depth understanding of the phenomenon is the
goal; validity is not always enhanced.

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