Department of Psychology, Fordham University, The Bronx, NY, USA.
Behav Res Methods. 2020 Aug;52(4):1480-1490. doi: 10.3758/s13428-019-01328-9.
When a continuous variable is measured twice, paired t test can be used to examine the statistical difference between two time points. However, when several related but dichotomously scored (0, 1) variables are measured twice, it would not be reasonable to use paired t test (or chi-squared test) to examine the related binary variable differences. Therefore, the present study introduces a novel statistical approach, called matched correspondence analysis (matched CA), which tests the related binary value differences between two time points. Matched CA was originally designed to study between-group comparisons (e.g., gender) in two contingency tables of the same size, with the same row and column quantities. However, unlike the original matched CA, the present study applies matched CA to the analysis of within-group matched matrices (e.g., at admission and at discharge) and examines the related binary value differences between two time points. To test the stability of parameter estimates, permutation and bootstrapping methods are used, and the pros and cons of within-group matched CA are discussed.
当连续变量被测量两次时，可以使用配对 t 检验来检查两个时间点之间的统计学差异。然而，当几个相关的但二分（0，1）变量被测量两次时，使用配对 t 检验（或卡方检验）来检查相关的二进制变量差异是不合理的。因此，本研究介绍了一种新的统计方法，称为匹配对应分析（matched CA），它用于检验两个时间点之间的相关二进制值差异。匹配 CA 最初是为了研究两个相同大小的、具有相同行和列数量的列联表之间的组间比较（例如性别）而设计的。然而，与原始的匹配 CA 不同，本研究将匹配 CA 应用于分析组内匹配矩阵（例如入院时和出院时），并检查两个时间点之间的相关二进制值差异。为了测试参数估计的稳定性，使用了置换和自举方法，并讨论了组内匹配 CA 的优缺点。
