Explicit Bayesian Analysis for Process Tracing: Guidelines, Opportunities, and Caveats
Explicit Bayesian Analysis for Process Tracing: Guidelines, Opportunities, and Caveats
复制标题
DOI：
10.1017/pan.2017.14
复制
发表时间：
2017-07-01
影响因子：
5.4
通讯作者：
Charman, Andrew E.
中科院分区：
文献类型：
--
作者：
Fairfield, Tasha;Charman, Andrew E.
贝叶斯概率有可能作为定性和定量方法之间的重要桥梁。然而，虽然贝叶斯统计技术已经成功地阐述了定量研究，应用贝叶斯概率定性研究仍然是一个开放的前沿。本文提出了新兴的文献贝叶斯过程跟踪的贝叶斯“概率扩展逻辑”从物理科学的论述，其中概率表示理性程度的信念，在命题不可避免地有限的信息，我们拥有。我们提供了一步一步的指导方针明确贝叶斯过程跟踪，提请注意已被忽视或未充分解决的技术点，我们说明了如何将这种方法与第一个系统的应用程序的案例研究，借鉴了多件详细的证据。虽然我们警告说，在定性社会科学中明确应用贝叶斯学习的努力将不可避免地遇到概率不能明确指定的困难，但我们仍然设想明确的贝叶斯分析在确定争论的轨迹时，学者们不同意推理的重要作用，并在训练直觉更系统地遵循贝叶斯概率。
Bayesian probability holds the potential to serve as an important bridge between qualitative and quantitative methodology. Yet whereas Bayesian statistical techniques have been successfully elaborated for quantitative research, applying Bayesian probability to qualitative research remains an open frontier. This paper advances the burgeoning literature on Bayesian process tracing by drawing on expositions of Bayesian "probability as extended logic" from the physical sciences, where probabilities represent rational degrees of belief in propositions given the inevitably limited information we possess. We provide step-by-step guidelines for explicit Bayesian process tracing, calling attention to technical points that have been overlooked or inadequately addressed, and we illustrate how to apply this approach with the first systematic application to a case study that draws on multiple pieces of detailed evidence. While we caution that efforts to explicitly apply Bayesian learning in qualitative social science will inevitably run up against the difficulty that probabilities cannot be unambiguously specified, we nevertheless envision important roles for explicit Bayesian analysis in pinpointing the locus of contention when scholars disagree on inferences, and in training intuition to follow Bayesian probability more systematically.