Cogn Neuropsychol. 2002 Jun 1;19(4):291-9. doi: 10.1080/02643290143000150.
The single case methodology that is widely used in cognitive neuropsychology often requires a comparison of data from a single individual (the patient) with that from a group of controls, in order to ascertain whether the patient's mean score can be viewed as significantly different from that of controls. This article reviews methods that have been used to deal with such data. Although Analysis of Variance (ANOVA) provides one possible solution of comparing group means, unequal group sizes and differences in variability between patient and controls can violate the assumptions of the test. Using Monte Carlo simulations, it was found that differences in group size and a group of N = 1 did not significantly affect the reliability of the analysis. In contrast, unacceptably high Type I errors were obtained when, in addition to unequal group sizes, there were relatively modest differences between the variance of the patient and that of the controls. We suggest that ANOVA can be used for the comparison of the mean score of an individual with that of a group of controls, but that when there is a difference in variability between the two groups, revised F criteria should be used in order to make the analysis reliable. A table of modified F values is given, which can be used for various departures from homogeneity of variance.
在认知神经心理学中广泛使用的单一案例方法通常需要将单个个体（患者）的数据与一组对照进行比较，以确定患者的平均分数是否可以被视为与对照组明显不同。本文综述了用于处理此类数据的方法。尽管方差分析（ANOVA）为比较组平均值提供了一种可能的解决方案，但组大小不等和患者与对照组之间的变异性差异可能违反测试的假设。通过蒙特卡罗模拟发现，组大小的差异和 N = 1 的组不会显着影响分析的可靠性。相比之下，当除了组大小不等之外，患者和对照组的方差之间存在相对较小的差异时，会得到不可接受的高Ⅰ型错误。我们建议可以使用 ANOVA 来比较个体的平均分数与一组对照的平均分数，但当两组之间存在变异性差异时，应该使用修正的 F 标准以使分析可靠。给出了一个修正 F 值的表格，可用于各种方差非齐性的情况。
