大数据与社会科学研究与实践的数据科学方法与工具

IF 1.5 3区数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS

Journal of the Royal Statistical Society Series A-Statistics in Society Pub Date : 2023-08-14 DOI:10.1093/jrsssa/qnad109

V. Kalyani

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Big Data and Social Science Data Science Methods and Tools for Research and Practice

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来源期刊

Journal of the Royal Statistical Society Series A-Statistics in Society 数学-统计学与概率论

CiteScore

2.90

自引率

5.00%

发文量

136

审稿时长

>12 weeks

期刊介绍： Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.