{"title":"Sage Statisticians in Social Sciences: Impact of Rubin’s Work","authors":"Kazuo Shigemasu","doi":"10.1007/s41745-022-00329-6","DOIUrl":null,"url":null,"abstract":"<div><p>Contemporary social scientists have cast serious doubts over traditional statistical testing procedures and questioned the reproducibility of the findings. The Bayesian approach provides sound statistical tools to draw inferences about unknown parameters and potential outcomes in a methodical way. This paper reviews D. B. Rubin’s work from the orthodox Bayesian viewpoint and discusses how his brilliant ideas and suggestions should be applied when social scientists deal with real data. The discussion focuses on making inferences about causal relationships and handling missing data. It is argued that social scientists who are confident about both the Bayesian coherent system and the necessitated effective software for numerical solutions can build relevant statistical models and derive relevant information from the Bayesian analysis of real data. This paper specifically explains how to deal with the data, using examples from situations that social scientists should often encounter.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Indian Institute of Science","FirstCategoryId":"103","ListUrlMain":"https://link.springer.com/article/10.1007/s41745-022-00329-6","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 1
Abstract
Contemporary social scientists have cast serious doubts over traditional statistical testing procedures and questioned the reproducibility of the findings. The Bayesian approach provides sound statistical tools to draw inferences about unknown parameters and potential outcomes in a methodical way. This paper reviews D. B. Rubin’s work from the orthodox Bayesian viewpoint and discusses how his brilliant ideas and suggestions should be applied when social scientists deal with real data. The discussion focuses on making inferences about causal relationships and handling missing data. It is argued that social scientists who are confident about both the Bayesian coherent system and the necessitated effective software for numerical solutions can build relevant statistical models and derive relevant information from the Bayesian analysis of real data. This paper specifically explains how to deal with the data, using examples from situations that social scientists should often encounter.
期刊介绍:
Started in 1914 as the second scientific journal to be published from India, the Journal of the Indian Institute of Science became a multidisciplinary reviews journal covering all disciplines of science, engineering and technology in 2007. Since then each issue is devoted to a specific topic of contemporary research interest and guest-edited by eminent researchers. Authors selected by the Guest Editor(s) and/or the Editorial Board are invited to submit their review articles; each issue is expected to serve as a state-of-the-art review of a topic from multiple viewpoints.