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Tasha Fairfield (LSE): “Reliability of Inference: Analogs of Replication in Qualitative Research”

27 November, 4:30 pm6:00 pm

Abstract: How do issues related to replication translate into the context of qualitative research? As Freese and Peterson (forthcoming) forewarn, discussions of replication in quantitative social science cannot be directly transposed into this realm. However, we can identify analogs for the various combinations of same data vs. new data, same procedures vs. different procedures scrutiny that have been discussed in the quantitative context. While some of our analogs share the same overarching definitions and import as their quantitative relatives, others diverge significantly. The differences in these instances arise from distinctions between frequentism, which underpins orthodox statistics, and Bayesianism, which a growing body of research identifies as the most promising methodological foundation for inference in qualitative research (Bennett 2015, Humphreys and Jacobs 2015, Fairfield and Charman 2017).

In this chapter, we advance two positions that we believe could help build common ground among quantitative and qualitative scholars. First, we advocate restricting the use of the term replication to a narrowly-defined set of new-data, same-procedures scrutiny that applies to orthodox statistical analysis and experimental research. Second, we argue that the overarching concern in all scientific inquiry is reliability of inference: how much confidence we can justifiably hold in our conclusions. Reliability encompasses but extends beyond the notion of replication. Our discussion therefore focuses on practices that could help improve how we assess evidence, build consensus among scholars, and promote knowledge accumulation within a Bayesian framework, which provides a natural language for evaluating uncertainty.

Details

Date:
27 November
Time:
4:30 pm – 6:00 pm
Event Category:

Organiser

CPNSS

Venue

LAK 2.06
Lakatos Building
London, WC2A 2AE United Kingdom
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Website:
http://www.lse.ac.uk/