ABSTRACT

Statistical paradigms limit the perspective and tools social work researchers use to study the world and answer questions impacting people and policy. Currently, quantitative social work researchers overwhelmingly rely on the frequentist paradigm of statistics. This paper discusses foundational differences between the frequentist and Bayesian statistical paradigms, describes basic concepts of Bayesian analysis, compares Bayesian and frequentist statistical analysis for a sample social work problem, and introduces two types of causal analyses built on Bayesian statistical thinking: counterfactual causality, and causality based on work by computer scientist Judea Pearl. Implications for social work research are discussed.

摘要

统计范式限制了社会工作研究人员用来研究世界和回答影响人们和政策的问题的视角和工具。目前，定量社会工作研究人员绝大多数依赖于统计的频率论范式。本文讨论了频率论和贝叶斯统计范式之间的基本差异，描述了贝叶斯分析的基本概念，比较了贝叶斯和频率论统计分析样本社会工作问题，并介绍了两种基于贝叶斯统计思维的因果分析：反事实因果关系和因果关系基于计算机科学家 Judea Pearl 的工作。讨论了对社会工作研究的影响。