Research

Validity in Qualitative Research

Qualitative research is frequently criticized for lacking scientific rigor with poor justification of the methods adopted, lack of transparency in the analytical procedures and the findings being merely a collection of personal opinions subject to researcher bias (Anfara & Mertz, 2015; Mills & Birks, 2014). For the novice to expert researcher, demonstrating rigor when undertaking qualitative research is challenging because there is no of accepted consensus about the standards by which such research should be judged (Berg & Lune, 2012). Depending on their philosophical perspectives, some qualitative researchers reject the framework of validity that is commonly accepted in more quantitative research in the social sciences (Biemer & Lyberg, 2003). They reject the basic realist assumption that there is a reality external to our perception of it (Bucholtz, 2000). Consequently, it doesn’t make sense to be concerned with the “truth” or “falsity” of an observation with respect to an external reality (which is a primary concern of validity) (Corti, Van den , Bishop, & Woollard, 2014). These qualitative researchers argue for different standards for judging the quality of research (Creswell, 2012).

According to Gibbs (2007); Kuckartz (2014); Marshall & Rossman (2010); Maxwell (2012), unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings from creating a research design; (b) analyzing data; and (c) writing up findings. These strategies include:

  1. Accounting for personal biases which may have influenced findings.
  2. Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis.
  3. Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent.
  4. Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented.
  5. Including rich and thick verbatim descriptions of participants’ accounts to support findings.
  6. Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations.
  7. Engaging with other researchers to reduce research bias.
  8. Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated.
  9. Data triangulation whereby different methods and perspectives help produce a more comprehensive set of findings.

References

  • Anfara, V. A., & Mertz, N. T. (2015). Theoretical frameworks in qualitative research (2 ed.). Thousand Oaks: Sage.
  • Berg, B. L., & Lune, H. (2012). Qualitative research methods for the social sciences. Boston: Pearson.
  • Biemer, P. P., & Lyberg, L. E. (2003). Introduction to Survey Quality. Hoboken: John Wiley & Sons.
  • Bucholtz, M. (2000). The Politics of Transcription. Journal of Pragmatics , 32(1), 1439-1465.
  • Corti, L., Van den , E., Bishop, L., & Woollard, M. (2014). Managing and Sharing Research Data. A Guide to Good Practice. London: Sage.
  • Creswell, J. (2012). Qualitative inquiry and research design: Choosing among five approaches (3 ed.). Thousand Oaks: Sage Publications.
  • Gibbs, G. (2007). Analyzing Qualitative Data. London: Sage.
  • Kuckartz, U. (2014). Qualitative text analysis: A guide to methods, practice and using software. London: Sage.
  • Marshall, C., & Rossman, G. B. (2010). Designing Qualitative Research (5 ed.). Thousand Oaks: Sage.
  • Maxwell, J. A. (2012). Qualitative research design: An interactive approach. Thousand Oaks: Sage.
  • Mills, J., & Birks, M. (2014). Qualitative Methodology: A Practical Guide. London: Sage.
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