Green S B
Multivariate Behav Res. 1991 Jul 1;26(3):499-510. doi: 10.1207/s15327906mbr2603_7.
Numerous rules-of-thumb have been suggested for determining the minimum number of subjects required to conduct multiple regression analyses. These rules-of-thumb are evaluated by comparing their results against those based on power analyses for tests of hypotheses of multiple and partial correlations. The results did not support the use of rules-of-thumb that simply specify some constant (e.g., 100 subjects) as the minimum number of subjects or a minimum ratio of number of subjects (N) to number of predictors (m). Some support was obtained for a rule-of-thumb that N ≥ 50 + 8 m for the multiple correlation and N ≥104 + m for the partial correlation. However, the rule-of-thumb for the multiple correlation yields values too large for N when m ≥ 7, and both rules-of-thumb assume all studies have a medium-size relationship between criterion and predictors. Accordingly, a slightly more complex rule-of thumb is introduced that estimates minimum sample size as function of effect size as well as the number of predictors. It is argued that researchers should use methods to determine sample size that incorporate effect size.
人们已经提出了许多经验法则来确定进行多元回归分析所需的最少样本量。通过将这些经验法则的结果与基于多元和偏相关假设检验的功效分析结果进行比较,对这些经验法则进行了评估。结果不支持使用简单指定某个常数(例如100个样本)作为最少样本量或样本量(N)与预测变量数量(m)的最小比率的经验法则。对于多元相关,当N≥50 + 8m;对于偏相关,当N≥104 + m时,该经验法则得到了一些支持。然而,对于多元相关的经验法则,当m≥7时,N的值过大,并且这两个经验法则都假设所有研究在标准变量和预测变量之间具有中等大小的关系。因此,引入了一个稍微复杂一些的经验法则,该法则根据效应大小以及预测变量的数量来估计最小样本量。有人认为,研究人员应该使用纳入效应大小的方法来确定样本量。