MRCV: A Package for Analyzing Categorical Variables with Multiple Response Options
MRCV: A Package for Analyzing Categorical Variables with Multiple Response Options
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MRCV：用于分析具有多个响应选项的分类变量的软件包
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
C. Bilder
中科院分区：
文献类型：
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作者：
N. Koziol;C. Bilder
多重响应类别变量 (MRCV)，也称为“选择任意”或“选择所有适用”变量，总结了允许受访者选择多个类别响应选项的调查问题。由于响应之间存在受试者内依赖性，用于分析分类变量之间关联的传统方法不适用于 MRCV。我们开发了 MRCV 包作为第一个可用于正确分析 MRCV 数据的 R 包。我们的软件包提供的统计方法包括独立性和对数线性模型的传统皮尔逊卡方检验的对应方法，其中依靠引导方法和 Rao-Scott 调整来获得有效的推论。我们通过分析一项评估堪萨斯州农民生猪废物管理实践的调查数据来展示该软件包的主要功能。
Multiple response categorical variables (MRCVs), also known as "pick any" or "choose all that apply" variables, summarize survey questions for which respondents are allowed to select more than one category response option. Traditional methods for analyzing the association between categorical variables are not appropriate with MRCVs due to the within-subject dependence among responses. We have developed the MRCV package as the first R package available to correctly analyze MRCV data. Statistical methods offered by our package include counterparts to traditional Pearson chi-square tests for independence and loglinear models, where bootstrap methods and Rao-Scott adjustments are relied on to obtain valid inferences. We demonstrate the primary functions within the package by analyzing data from a survey assessing the swine waste management practices of Kansas farmers.