一致性g指数的统计推断

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2022-04-29 DOI:10.3102/10769986221088561

D. Bonett

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引用次数: 0

摘要

本文回顾了Cohen 's κ的局限性，并推荐了一种替代的g指数来评估名义尺度的一致性。最大似然估计，标准误差和置信区间的两个评级的g指数为一组和两组设计推导。提出了一种新的多参数设计一致性g指数。本文还推导了几种重要的特殊情况下的统计推断方法。提出了g指数荟萃分析方法，可用于组合和比较两个或多个人群的一致性。提出了用于双因子和多因子设计的封闭式样本大小公式，以达到所需的置信区间精度。所有结果都给出了R函数。

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Statistical Inference for G-indices of Agreement

The limitations of Cohen’s κ are reviewed and an alternative G-index is recommended for assessing nominal-scale agreement. Maximum likelihood estimates, standard errors, and confidence intervals for a two-rater G-index are derived for one-group and two-group designs. A new G-index of agreement for multirater designs is proposed. Statistical inference methods for some important special cases of the multirater design also are derived. G-index meta-analysis methods are proposed and can be used to combine and compare agreement across two or more populations. Closed-form sample-size formulas to achieve desired confidence interval precision are proposed for two-rater and multirater designs. R functions are given for all results.

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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.