By Mariya Razzaghian*
1. What is triangulation in research?
Triangulation is the combination of two or more theoretical perspective, methodological approaches, analytical methods, data sources, or investigators within the same study. Denzin (1970) has called mixing methods as a 'triangulation perspective'.
2. Why we need triangulation?
Researchers use triangulation for two reasons mainly:
- The use of a single method may not allow the researcher to address all aspects of the research questions.
- Combining research methods increases the validity of the research findings because using a variety of methods means that one serves as a check on the other.
Since many researchers now recognize that there are differences in social science and natural science and that the interpretations that human beings have of their social behavior and the external world are important factors that need to be kept in mind, they now appreciate that both quantitative and qualitative methods have a role to play in social science.
3. Types of Triangulation
There can be many types of triangulations. However, for the sake of parsimony we shall focus on the four widely recognized types of triangulations here, i.e.
- methodological triangulation
- investigator triangulation,
- theoretical triangulation,
- data analysis triangulation.
3.1. Methodological Triangulation
This type of triangulation has also been called as mixed methods, multi-methods, and methods triangulation. By using multiple methods, the research strives to decrease the deficiencies and biases that stem from any single method creating the potential for counterbalancing the flaws or weaknesses of one method with the the strength of the other. Methodological triangulation can be further divided into two types, i.e. withing method and between methods triangulation.
3.1.1 With-in-method triangulation
This is done at the data level and uses two different tools for collecting data. While, this type of triangulation has narrow scope, it provides alternative ways for measuring a variable.
3.1.2 Between methods triangulation
we, may also call this across methods triangulation. It is done at the design level and uses both quantitative as well as qualitative methods. The approaches are merged at the level of interpretation.
3.2 Investigator Triangulation
It involves using more than one observer, interviewer, coder or data analyst in the study. Confirmation of data among investigators without prior decision or collaboration with one another lends greater credibility to the observation. The purpose of multiple investigator is to reduce the potential of bias in selecting, analyzing, coding, or gathering of data, as well as to increase the internal validity of the findings. Having more than one investigator also has the potential for keeping the team honest which increase the credibility of findings. Analysis of data (particularly quantitative data) by multiple analysts serves not only to amplify the results and increase validity but it also adds to the reliability.
Verification of the the interpretation of data (quantitative and qualitative by more than one research can increase the value of the findings. Also, if the research is conducted by investigators who are equally skilled in their respective approaches can enhance the value of the study by contributing to the study from different view points.
Investigators equally apt in their respective research approaches can conduct the different phases of research allowing for both the investigators and methodological triangulation.
3.3 Theoretical Triangulation
This type involves using multiple theories and hypotheses to study a phenomenon. The use of multiple theories and hypotheses is done with the intention that the study can be conducted with multiple lenses and questions in mind in order to lend support to or refute the findings.
The theoretical perspectives or hypotheses used in the study may be related to or they may have opposing views points, depending on what the researcher hopes to accomplish. The use of multiple theories provides the advantage that alternative explanation for a phenomenon can be decreased. Multiple theories also help to rule out competing hypotheses, prevent premature acceptance of plausible explanations, and increase confidence in constructing concepts in theory development.
3.4 Data Analysis Triangulation
Data analysis triangulation is the combination of two or more methods of analytical tools. These techniques can include different families of statistical tools such t-test, regression, anova, structural equation modelling in order to validate the data or determine similarities.
*Mariya Razzaghian is a PhD student at the Institute of Management Sciences, Peshawar. Her area of specialization is HRM, with special focus on work place bullying and workaholism.
