2021
DOI: 10.1108/jocm-04-2021-0117
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Fooling ourselves and others: confirmation bias and the trustworthiness of qualitative research – Part 1 (the threats)

Abstract: PurposeTo describe the implicit epistemic flaw of “confirmation bias” and to illustrate and evaluate the threats to qualitative research trustworthiness from that bias.Design/methodology/approachThe article overviews evidence and analysis from a wide range of disciplines. The adverse effect of three varieties of confirmation bias is described in some detail in illustrative examples.FindingsIt is argued that the threats from the bias go to the heart of the research. A subsequent article summarizes and critiques… Show more

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Cited by 15 publications
(12 citation statements)
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References 73 publications
(74 reference statements)
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“…Karl Popper (2014) made this abundantly clear in his famous parable about black swans—one does not demonstrate the truth claim, “all swans are white” by looking for white swans. This only leads to the logical fallacy of confirmation bias, whereby it is a certainty that the researcher will find exactly what he or she was looking for in the first place (McSweeney, 2021). Rather, the best way to demonstrate that all swans are white is to look for black swans.…”
Section: Qualitative Research Definedmentioning
confidence: 99%
“…Karl Popper (2014) made this abundantly clear in his famous parable about black swans—one does not demonstrate the truth claim, “all swans are white” by looking for white swans. This only leads to the logical fallacy of confirmation bias, whereby it is a certainty that the researcher will find exactly what he or she was looking for in the first place (McSweeney, 2021). Rather, the best way to demonstrate that all swans are white is to look for black swans.…”
Section: Qualitative Research Definedmentioning
confidence: 99%
“…In particular, there was a feeling that at times engagement with theory was excessive, or driven by a desire to please senior academics and editors, and not necessarily allowing the best forms of innovation and theorization. This may even be seen dangerously encouraging forms of confirmation bias (McSweeney, 2021) and thus deserve future attention. Conversely, engagement with literature was seen at times as being too limited, as some authors tend to avoid acknowledging relevant papers, perhaps because they belong to different disciplines, or adopt different methods.…”
Section: Final Reflections and Possible Future Developmentsmentioning
confidence: 99%
“…Interestingly, the authors also found that papers abandoning common criteria generally provided accurate description of methods, and that, while having adopted a mainstream approach will not mean a specific preference for criteria, conversely, having adopted critical or interpretive will make it more likely to report alternative criteria or abandon them. A few other studies have looked at the concrete application of quality criteria in some sample papers, generally pointing to the complex nature of validation in qualitative research (Ahrens and Dent, 1998; Ahrens and Chapman, 2006) or highlighting the risks of bias in the interpretation of data (McSweeney, 2021). It would thus be interesting to complement these desk-based analyses with explorations of the personal views and experiences of accounting scholars on what counts as good research, and thus how they feel they need to demonstrate the quality of their research, and how, in turn, they assess others' research.…”
Section: Assessing and Proving The Quality Of Qualitative Research: O...mentioning
confidence: 99%
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“…There, is however, care to be taken and a critical self-reflection required to ensure ethics, criticality of thought, and avoidance of taking for granted that one person's experience is the same as another. There is also a danger of the researcher being susceptible to confirmation bias and unconsciously shaping questions and coproduction of data with participants inequitably (McSweeney, 2021). To ensure the first author did not overly influence research findings, she continuously reflected upon and challenged her preexisting assumptions and experiences to appreciate how these may influence the data collection process, and drew upon her co-authors challenges and critiques of data regarding interpretations of data.…”
Section: Research Design and Originsmentioning
confidence: 99%