Human Sciences Division, Northern Ontario School of Medicine, Thunder Bay, ON, Canada, P7B 5E1.
Behav Res Methods. 2013 Sep;45(3):880-95. doi: 10.3758/s13428-012-0289-7.
Several procedures that use summary data to test hypotheses about Pearson correlations and ordinary least squares regression coefficients have been described in various books and articles. To our knowledge, however, no single resource describes all of the most common tests. Furthermore, many of these tests have not yet been implemented in popular statistical software packages such as SPSS and SAS. In this article, we describe all of the most common tests and provide SPSS and SAS programs to perform them. When they are applicable, our code also computes 100 × (1 - α)% confidence intervals corresponding to the tests. For testing hypotheses about independent regression coefficients, we demonstrate one method that uses summary data and another that uses raw data (i.e., Potthoff analysis). When the raw data are available, the latter method is preferred, because use of summary data entails some loss of precision due to rounding.
已经在各种书籍和文章中描述了使用汇总数据来检验 Pearson 相关系数和普通最小二乘法回归系数假设的几个程序。然而，据我们所知，没有一个单一的资源描述了所有最常见的测试。此外，这些测试中的许多尚未在流行的统计软件包（如 SPSS 和 SAS）中实现。在本文中，我们描述了所有最常见的测试，并提供了 SPSS 和 SAS 程序来执行这些测试。当它们适用时，我们的代码还计算了与测试相对应的 100×（1-α）%置信区间。对于检验关于独立回归系数的假设，我们演示了一种使用汇总数据的方法和另一种使用原始数据（即 Potthoff 分析）的方法。当原始数据可用时，首选后者方法，因为使用汇总数据会由于舍入而导致精度损失。
