{"title":"相对协作强度(RIC)指标的置信区间","authors":"J. E. Fuchs, Lawrence J. Smolinsky, R. Rousseau","doi":"10.2478/jdis-2022-0021","DOIUrl":null,"url":null,"abstract":"Abstract Purpose We aim to extend our investigations related to the Relative Intensity of Collaboration (RIC) indicator, by constructing a confidence interval for the obtained values. Design/methodology/approach We use Mantel-Haenszel statistics as applied recently by Smolinsky, Klingenberg, and Marx. Findings We obtain confidence intervals for the RIC indicator Research limitations It is not obvious that data obtained from the Web of Science (or any other database) can be considered a random sample. Practical implications We explain how to calculate confidence intervals. Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement. Our approach presents a suggestion to solve this problem. Originality/value Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"5 - 15"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Confidence Intervals for Relative Intensity of Collaboration (RIC) Indicators\",\"authors\":\"J. E. Fuchs, Lawrence J. Smolinsky, R. Rousseau\",\"doi\":\"10.2478/jdis-2022-0021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Purpose We aim to extend our investigations related to the Relative Intensity of Collaboration (RIC) indicator, by constructing a confidence interval for the obtained values. Design/methodology/approach We use Mantel-Haenszel statistics as applied recently by Smolinsky, Klingenberg, and Marx. Findings We obtain confidence intervals for the RIC indicator Research limitations It is not obvious that data obtained from the Web of Science (or any other database) can be considered a random sample. Practical implications We explain how to calculate confidence intervals. Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement. Our approach presents a suggestion to solve this problem. Originality/value Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.\",\"PeriodicalId\":92237,\"journal\":{\"name\":\"Journal of data and information science (Warsaw, Poland)\",\"volume\":\"7 1\",\"pages\":\"5 - 15\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of data and information science (Warsaw, Poland)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/jdis-2022-0021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of data and information science (Warsaw, Poland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/jdis-2022-0021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
摘要目的通过为所得值构建置信区间,扩展我们对相对协作强度(RIC)指标的研究。设计/方法/方法我们使用了最近由Smolinsky、Klingenberg和Marx应用的Mantel-Haenszel统计。我们获得了RIC指标的置信区间研究局限性从Web of Science(或任何其他数据库)获得的数据不明显可以被视为随机样本。我们解释如何计算置信区间。文献计量指标往往以精确值而不是根据数据库和测量时间的近似值表示。我们的方法提出了解决这个问题的建议。我们的方法结合了二元分类数据的统计和文献计量学研究的合作。
Confidence Intervals for Relative Intensity of Collaboration (RIC) Indicators
Abstract Purpose We aim to extend our investigations related to the Relative Intensity of Collaboration (RIC) indicator, by constructing a confidence interval for the obtained values. Design/methodology/approach We use Mantel-Haenszel statistics as applied recently by Smolinsky, Klingenberg, and Marx. Findings We obtain confidence intervals for the RIC indicator Research limitations It is not obvious that data obtained from the Web of Science (or any other database) can be considered a random sample. Practical implications We explain how to calculate confidence intervals. Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement. Our approach presents a suggestion to solve this problem. Originality/value Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.