Prior exposure: Training in Bayesian data analysis to complement and enhance traditional statistical methods for psychology and the social sciences

先前接触：贝叶斯数据分析培训，以补充和增强心理学和社会科学的传统统计方法

• 批准号: ES/M004910/1
• 负责人: Thom Baguley
• 金额: $ 3.6万
• 依托单位: Nottingham Trent University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FM004910%2F1
• 关键词: Prior; exposure; Training; Bayesian; data

Bayesian methods are now part of the standard repertoire of statistical methods in science and are becoming increasingly important in psychology and the social sciences in field as diverse as election forecasting, brain imaging and clinical psychology. However, while top international journals in these fields regularly publish quantitative work using sophisticated Bayesian methods, opportunities for psychologists and social psychologists in the UK to acquire skills in Bayesian data analysis are few and far between. Postgraduate social science training in the UK does not typically introduce these methods, while undergraduate and postgraduate training in traditional statistical methods will not cover some fundamental philosophical and statistical concepts necessary for advanced training in Bayesian methods.Offering training in Bayesian data analysis for psychologists and social scientists therefore involves overcoming a number of barriers. These include the differences in prior training (e.g., those with a basic quantitative social science grounding and those with experience of more advanced methods in a non-Bayesian context) and differences in technical competence (ranging from students familiar with spreadsheets and basic statistical packages to those with experience writing their own computer code for data analysis). However, perhaps the largest barrier is the difference in philosophy between Bayesian and traditional statistical methods. Although the different philosophies have historically been a source of conflict, recent developments in statistical science emphasise the practical advantages of drawing on both sets of methods. We propose a training package that aims to present Bayesian tools as a way to complement and enhance traditional statistical methods.Our training package comprises a series of four one-day workshops that begin with an introduction to the philosophy and practice of Bayesian data analysis and end with sophisticated Bayesian tools that allow researchers to handle messy real world data that are difficult to model using traditional approaches. Participants may attend one or more individual workshops (e.g., to understand basic Bayesian methods enough to review a paper or improve their collaboration with a more expert colleague) or attend the entire sequence. In addition to the workshops a final symposium will offer an opportunity for research students and early career researchers to present work involving Bayesian data analysis alongside papers by experts in the field. The overarching theme for the symposium is the teaching of Bayesian data analysis. On completion of the project the training resources will be made available to researchers under an open source license in order to facilitate the development of new training and to support the integration of Bayesian data analysis into the curriculum.

贝叶斯方法现在是科学统计方法的标准剧目的一部分，在心理学和社会科学领域变得越来越重要，如选举预测，脑成像和临床心理学。然而，虽然这些领域的顶级国际期刊定期发表使用复杂贝叶斯方法的定量研究，但英国心理学家和社会心理学家获得贝叶斯数据分析技能的机会却很少。英国社会科学研究生的培训通常不会介绍这些方法，而传统统计方法的本科生和研究生培训不会涵盖贝叶斯方法高级培训所需的一些基本哲学和统计概念。因此，为心理学家和社会科学家提供贝叶斯数据分析培训涉及克服许多障碍。这些包括先前培训的差异（例如，具有基本定量社会科学基础的学生和具有非贝叶斯背景下更先进方法经验的学生）以及技术能力差异（从熟悉电子表格和基本统计软件包的学生到具有编写自己的计算机代码进行数据分析的经验的学生）。然而，也许最大的障碍是贝叶斯和传统统计方法之间的哲学差异。虽然不同的哲学在历史上一直是冲突的根源，但统计科学的最新发展强调了利用这两套方法的实际优势。我们提出了一个培训包，旨在介绍贝叶斯工具作为一种补充和加强传统的统计方法。我们的培训包包括一系列的四个为期一天的研讨会，开始介绍贝叶斯数据分析的哲学和实践，并与复杂的贝叶斯工具，使研究人员能够处理混乱的真实的世界数据，难以使用传统的方法建模结束。参加者可以参加一个或多个单独的讲习班（例如，了解基本的贝叶斯方法足以审查论文或改善他们与更专业的同事的合作）或参加整个序列。除了研讨会，最后的研讨会将为研究生和早期职业研究人员提供一个机会，介绍涉及贝叶斯数据分析的工作以及该领域专家的论文。研讨会的首要主题是贝叶斯数据分析的教学。项目完成后，培训资源将根据开源许可证向研究人员提供，以促进新培训的开发并支持将贝叶斯数据分析整合到课程中。

项目成果

The Cambridge Encyclopedia of Child Development

剑桥儿童发展百科全书

• 发表时间: 2017
• 作者: Andrews, M.