2019/02/11 by Tamarinde Haven, Leonie van Grootel · 1 voice · 9 citations
Computer Science · Decision Sciences · #Explainable Artificial Intelligence (XAI) #Meta-analysis and systematic reviews #scientometrics and bibliometrics research
paper · pdf · doi:10.1080/08989621.2019.1580147
openalex publication_date 2019/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/04
The threat to reproducibility and awareness of current rates of research misbehavior sparked initiatives to better academic science. One initiative is preregistration of quantitative research. We investigate whether the preregistration format could also be used to boost the credibility of qualitative research. A crucial distinction underlying preregistration is that between prediction and postdiction. In qualitative research, data are used to decide which way interpretation should move forward, using data to generate hypotheses and new research questions. Qualitative research is thus a real-life example of postdiction research. Some may object to the idea of preregistering qualitative studies because qualitative research generally does not test hypotheses, and because qualitative research design is typically flexible and subjective. We rebut these objections, arguing that making hypotheses explicit is just one feature of preregistration, that flexibility can be tracked using preregistration, and that preregistration would provide a check on subjectivity. We then contextualize preregistrations alongside another initiative to enhance credibility in qualitative research: the confirmability audit. Besides, preregistering qualitative studies is practically useful to combating dissemination bias and could incentivize qualitative researchers to report constantly on their study's development. We conclude with suggested modifications to the Open Science Framework preregistration form to tailor it for qualitative studies.