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多对应分析作为一种工具,用于考察 1901 年至 2018 年诺贝尔奖项数据。

Multiple correspondence analysis as a tool for examining Nobel Prize data from 1901 to 2018.

机构信息

School of Information & Physical Sciences, College of Engineering, Science and Environment, University of Newcastle, Callaghan NSW, Australia.

出版信息

PLoS One. 2022 Apr 1;17(4):e0265929. doi: 10.1371/journal.pone.0265929. eCollection 2022.

Abstract

The main goal of this paper is to examine Nobel Prize data by studying the association among the laureate's country of birth or residence, discipline, time period in which the Nobel Prize was awarded, and gender of the recipient. Multiple correspondence analysis is used as a tool to examine the association between these four categorical variables by cross classifying them in the form of a four-way contingency table. The data that we examine comprise Nobel Prize recipients from 1901 to 2018 (inclusive) from eight-developed countries, with a total sample of 785 Nobel Prize recipients. The countries include Canada, France, Germany, Italy, Japan, Russia, the British Isles, and the USA and the disciplines in which the individuals were awarded the prizes include chemistry, physics, physiology or medicine, literature, economics, and peace.

摘要

本文的主要目的是通过研究诺贝尔奖得主的出生地或居住地、学科、获奖时间以及获奖者的性别之间的关系,来研究诺贝尔奖数据。多元对应分析被用作一种工具,通过以四向交叉表的形式对这四个分类变量进行交叉分类,来检验它们之间的关联。我们所研究的数据包括 1901 年至 2018 年(包括)期间来自八个发达国家的诺贝尔奖获得者,总共有 785 名诺贝尔奖获得者。这些国家包括加拿大、法国、德国、意大利、日本、俄罗斯、不列颠群岛和美国,获奖者所获奖项的学科包括化学、物理、生理学或医学、文学、经济学和和平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c863/8974957/7ff5b223055c/pone.0265929.g001.jpg

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