{"title":"Media diversity and the analysis of qualitative variation","authors":"David Deacon, J. Stanyer","doi":"10.1177/20570473211006481","DOIUrl":null,"url":null,"abstract":"Diversity is recognised as a significant criterion for appraising the democratic performance of media systems. This article begins by considering key conceptual debates that help differentiate types and levels of diversity. It then addresses a core methodological challenge in measuring diversity: how do we model statistical variation and difference when many measures of source and content diversity only attain the nominal level of measurement? We identify a range of obscure statistical indices developed in other fields that measure the strength of ‘qualitative variation’. Using original data, we compare the performance of five diversity indices and, on this basis, propose the creation of a more effective diversity average measure. The article concludes by outlining innovative strategies for drawing statistical inferences from these measures, using bootstrapping and permutation testing resampling. All statistical procedures are supported by a unique online resource developed for this article.","PeriodicalId":44233,"journal":{"name":"Communication and the Public","volume":"6 1","pages":"19 - 32"},"PeriodicalIF":1.2000,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/20570473211006481","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communication and the Public","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/20570473211006481","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 2
Abstract
Diversity is recognised as a significant criterion for appraising the democratic performance of media systems. This article begins by considering key conceptual debates that help differentiate types and levels of diversity. It then addresses a core methodological challenge in measuring diversity: how do we model statistical variation and difference when many measures of source and content diversity only attain the nominal level of measurement? We identify a range of obscure statistical indices developed in other fields that measure the strength of ‘qualitative variation’. Using original data, we compare the performance of five diversity indices and, on this basis, propose the creation of a more effective diversity average measure. The article concludes by outlining innovative strategies for drawing statistical inferences from these measures, using bootstrapping and permutation testing resampling. All statistical procedures are supported by a unique online resource developed for this article.