Research Synthesis Methods

Reconciling disparate data to determine the right answer: A grounded theory of meta analysts' reasoning in meta‐analysis

Journal Article

  • Author(s): Lisa Chan, Mary Ellen Macdonald, Franco A. Carnevale, Russell J. Steele, Ian Shrier
  • Article first published online: 03 Sep 2017
  • DOI: 10.1002/jrsm.1258
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While the systematic review process is intended to maximize objectivity and limit researchers' biases, examples remain of discordant recommendations from meta‐analyses. Current guidelines to explore discrepancies assume the variation is produced by methodological differences and thus focus only on the study process. Because heterogeneity of interpretation also occurs when experts examine the same data, our purpose was to examine if there are reasoning differences, ie, in how information is processed and valued. We created simulated meta‐analyses based on idealized randomized studies (ie, perfect studies with no bias) to ensure differences in interpretations could only be due to reasoning. We recruited published meta‐analysts using purposeful variables. We conducted 3 audio‐recorded interviews per participant using structured and semi‐structured interviews, with paraphrasing and reflective listening to enhance and verify responses. Recruitment and analysis of transcripts and field notes followed the principles of grounded theory (eg, theoretical saturation, constant comparative analysis). Results show the complexity of meta‐analytic reasoning. At each step of the process, participants attempted to reconcile disparate forms of knowledge to determine a right answer (moral concern) and accurately draw a treatment effect (epistemological concern). The reasoning processes often shifted between considering the meta‐analysis as if the data were whole, and as if the data were discrete components (individual studies). These findings highlight paradigmatic tensions regarding the epistemological premises of meta‐analysis, resembling previous historical investigations of the functioning of scientific communities. In understanding why different meta‐analysts interpret data differently, it may be unrealistic to expect objective homogenous recommendations based on meta‐analyses.

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