• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Professional Communication >Correspondence Analysis: A Statistical Technique Ripe for Technical and Professional Communication Researchers
【24h】

Correspondence Analysis: A Statistical Technique Ripe for Technical and Professional Communication Researchers

机译：通讯分析：一种统计技术，对于技术和专业传播研究人员来说已经成熟

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Correspondence analysis is a statistical method that allows researchers to explore relationships among complex categorical variables. This paper will provide researchers with the theoretical and practical foundations for understanding and applying correspondence analysis to their own research agendas. Problem: Technical communicators use a variety of research methods and collect a variety of types of data. Of particular interest to technical communicators is categorical data, or data that are not traditionally quantitative. For instance, technical communicators often collect and analyze language data from a variety of texts. Analyzing this type of data can be difficult using traditional statistical methods. Key concepts: Variable types, a priori versus exploratory research designs, contingency tables, and data visualization are central to understanding the foundations of correspondence analysis. Key lessons: To conduct correspondence analysis, a researcher must walk through a series of steps including: (1) determining whether correspondence analysis is appropriate, (2) choosing a statistical software package, (3) running the correspondence analysis, and (4) interpreting and applying the results. Implications for practice: While correspondence analysis provides many useful insights into categorical data, a researcher must consider several things when deciding to use correspondence analysis. These include the potential to misinterpret and misapply the results of a correspondence analysis.
机译：对应分析是一种统计方法，可让研究人员探索复杂类别变量之间的关系。本文将为研究人员提供理论和实践基础，帮助他们将对应分析理解和应用到他们自己的研究议程中。问题：技术交流者使用各种研究方法并收集各种类型的数据。技术交流者特别感兴趣的是分类数据，或者传统上不是定量的数据。例如，技术交流者经常从各种文本中收集和分析语言数据。使用传统的统计方法很难分析此类数据。关键概念：变量类型，先验与探索性研究设计，列联表和数据可视化对于理解对应分析的基础至关重要。关键课程：要进行对应分析，研究人员必须完成一系列步骤，包括：（1）确定对应分析是否合适；（2）选择统计软件包；（3）运行对应分析；以及（4）解释和应用结果。对实践的启示：尽管对应分析为分类数据提供了许多有用的见解，但研究人员在决定使用对应分析时必须考虑几件事。其中包括可能会误解和错误应用对应分析的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号