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EXT 501 - Triangulation Presentation


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  • All measurement is of the same kind and based on common ontology and epistemology.
  • Transcript

    • 1. 501: Applied Research Mixed Methods and Triangulation Pam Brown & Joanna Wiebe (Originally presented as Prezi slideshow, which doesn’t work on
    • 2. Mixed methods
      • Combines different methods - whether qualitative or quantitative - to enhance validity and reliability while allowing deeper interpretations of what you’re studying.
      • Studies that compare quantitative and qualitative observations at the conceptual or propositional level.
      • Central to the development and testing of theory.
    • 3. Triangulation is NOT
      • 3 points of view on a phenomenon
      • Intended to look like a triangle :)
      • Mixed methods (that is, it’s not about the mixing of the methods but of analyzing the data)
    • 4. Triangulation
      • Describes how, along with the use of multiple approaches to a research question (mixed methods), the researcher can zero-in on the answers or information sought in the data.
    • 5. Origins of triangulation in social research
      • 1959: Campbell & Fiske
      • 1966: Webb
      • 1970: Denzin
    • 6. Denzin (1978)
      • Methodological triangulation
      Refers to using more than one research method (e.g., in-person survey, phone survey) in measuring the same object of interest
    • 7. Denzin (1978) Refers to using the same approach for different sets of data in order to verify or falsify generalizable trends detected in one data set Data triangulation
    • 8. Denzin (1978) Refers to making use of different investigators with different backgrounds Investigator triangulation
    • 9. Denzin (1978) Refers to using at least 2 of the other methods of triangulation in combination Multiple triangulation
    • 10.
      • Denzin marries methodological approaches - and their ontologies & epistemologies - in his understanding of triangulation.
      • Denzin’s goal: To create a complete picture after using mixed methods (after combining qual and quant)
    • 11. Why triangulation?
      • The primary reason of triangulation is the recognition that data-set or investigator survey bias can be introduced by using only one research method.
      • Data & investigator triangulation are argued to overcome problems of bias and validity.
    • 12. Assumption of triangulation
      • In social research, if one relies on a single piece of data, there’s the danger that undetected error in the data-production process may render the analysis incorrect.
      • If, on the other hand, diverse kinds of data lead to the same conclusion, you can be a little more confident in that conclusion. (If 2 points cross, you can be more confident that your boat’s going in the right direction.)
    • 13. Different things to different people
      • Triangulation is regarded differently by adherents to various perspectives, which have their own ontological and epistemological approaches.
      • Positivists seek to remove bias
      • Interpretivists seek to increase validity
      • Positivists and interpretivists conceive and act on subjective data differently.
    • 14. Overcoming deficiencies
      • The deficiencies of any one method can be overcome by combining methods and thus capitalizing on their individual strengths.
      • Overcome bias issues (e.g., data bias, investigator bias)
      • Overcome validity issues (for quantitative methods)
      • ASSUMPTION: The more different the instruments/methods, data sets and investigators used in analyzing a specific problem, the greater will be the confidence in the final result
    • 15. Keep in mind…
      • Theoretical triangulation does not necessarily reduce bias
      • Methodological triangulation does not necessarily increase validity
      • Triangulation isn’t meant to combine qualitative and quantitative data
      • Denzin’s approach had strong elements of a positivist frame of reference - assumes a single reality that multiple accounts can be mapped on to pinpoint “absolute truths”
    • 16.
      • The effectiveness of triangulation rests on the assumption that the methods or strategies used will not share the same biases.
      • INSIGHT: Think about the significance of the role of methodological perspectives in social research generally and in the use of triangulation in particular.
    • 17.
    • 18. How triangulation SHOULD be
      • All measurement should be of the same kind and based on the same ontology and epistemology
      • Triangulation is possible only within paradigms; any effort to compare or integrate findings from different methods requires the prior adoption of one paradigm or another
      • It should describe diverse, severe quantitative tests (not qualitative)
    • 19. Should you avoid triangulation, then?
      • The sociologist should not necessarily avoid generating data in multiple ways and analysing it; rather, the sociologist should just avoid the mistake of using data to adjudicate between accounts
      • You shouldn’t adopt a naively optimistic view that the aggregation of data from different sources will unproblematically add up to produce a more complete picture.
    • 20. Remember this when using triangulation
      • What’s involved in triangulation is not just a matter of checking whether inferences are valid but of discovering which inferences are valid.
      • Don’t take any data at face value. And don’t regard some data as true and some as false.
      • Use multiple methods if your research requires it. And use data-analysis approaches like triangulation if it fits. But just use triangulation consistently within one perspective - without drawing from different methodological perspectives.
    • 21. Towards a less problematic approach
      • Triangulation constitutes the first phase of analysis; it’s used to estimate the error inherent in quantitative measurements. It also serves as a building block for the incorporation of qualitative data. Sequential mixed method:
      • QUANT > TRIANGULATION > QUAL (to deepen the analysis of quantitative findings)