Journal article Open Access

A Bayesian Perspective on Theory-Blind Data Collection

Fairfield, Tasha

Copestake, Goertz, and Haggard’s (CGH) “Veil of ignorance Process Tracing” (VPT)—which in essence entails placing a firewall between data collection and hypothesis testing1—is an interesting addition to a growing list of proposals made in recent years that aim to address potential sources of bias in qualitative social science. Many of these proposals (e.g., pre-registration, time-logging whether evidence was discovered before or after a hypothesis was devised) import prescriptions from large-N, frequentist, statistical research that, from a Bayesian perspective, are not applicable to qualitative research. Bayesian reasoning provides its own safeguards against the problems of confirmation bias and ad hoc hypothesizing, without imposing procedural constraints that would interfere with the inherently iterative, dynamic, and interactive nature of case-study research—where we go back and forth between hypothesizing, data collection, and analysis (Fairfield and Charman 2019).

Files (199.3 kB)
Name Size
Fairfield_Bayesian_QMMR_2020_18-2.pdf
md5:d30befda6944d4bd1b57c848dcb46dbb
199.3 kB Download
64
31
views
downloads
All versions This version
Views 6464
Downloads 3131
Data volume 6.2 MB6.2 MB
Unique views 6363
Unique downloads 3030

Share

Cite as