Department of Nursing, School of Health and Human Services, National University, Aero Court, San Diego, California, USA.
Biochem Med (Zagreb). 2013;23(2):143-9. doi: 10.11613/bm.2013.018.
The Chi-square statistic is a non-parametric (distribution free) tool designed to analyze group differences when the dependent variable is measured at a nominal level. Like all non-parametric statistics, the Chi-square is robust with respect to the distribution of the data. Specifically, it does not require equality of variances among the study groups or homoscedasticity in the data. It permits evaluation of both dichotomous independent variables, and of multiple group studies. Unlike many other non-parametric and some parametric statistics, the calculations needed to compute the Chi-square provide considerable information about how each of the groups performed in the study. This richness of detail allows the researcher to understand the results and thus to derive more detailed information from this statistic than from many others. The Chi-square is a significance statistic, and should be followed with a strength statistic. The Cramer's V is the most common strength test used to test the data when a significant Chi-square result has been obtained. Advantages of the Chi-square include its robustness with respect to distribution of the data, its ease of computation, the detailed information that can be derived from the test, its use in studies for which parametric assumptions cannot be met, and its flexibility in handling data from both two group and multiple group studies. Limitations include its sample size requirements, difficulty of interpretation when there are large numbers of categories (20 or more) in the independent or dependent variables, and tendency of the Cramer's V to produce relative low correlation measures, even for highly significant results.
卡方检验是一种非参数（无分布）工具，用于分析名义水平测量的因变量的组间差异。与所有非参数统计量一样，卡方检验对数据的分布具有稳健性。具体来说，它不需要研究组之间的方差相等或数据具有同方差性。它允许评估二分类自变量和多组研究。与许多其他非参数和一些参数统计量不同，计算卡方所需的计算为研究中每个组的表现提供了相当多的信息。这种详细信息的丰富性允许研究人员理解结果，从而从该统计量中得出比许多其他统计量更详细的信息。卡方是一个显著性统计量，应该紧随其后进行强度统计量。当获得显著的卡方结果时，Cramer's V 是最常用的用于检验数据的强度检验。卡方的优点包括其对数据分布的稳健性、计算的简便性、可以从检验中得出的详细信息、在不能满足参数假设的研究中使用、以及在处理来自两组和多组研究的数据时的灵活性。其局限性包括样本量要求、当独立或因变量中有大量类别（20 个或更多）时解释困难、以及 Cramer's V 倾向于产生相对较低的相关度量，即使对于非常显著的结果也是如此。
