计算机科学
可靠性(半导体)
度量(数据仓库)
编码(社会科学)
缺少数据
样品(材料)
宏
数据挖掘
统计
机器学习
数学
物理
量子力学
化学
色谱法
程序设计语言
功率(物理)
作者
Andrew F. Hayes,Klaus Krippendorff
标识
DOI:10.1080/19312450709336664
摘要
In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.
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