Rochon J
Department of Epidemiology and Biostatistics, University of Western Ontario, London, Canada.
Biometrics. 1989 Mar;45(1):193-205.
Grizzle, Starmer, and Koch (1969, Biometrics 25, 489-503) presented a unified approach for data analysis when the outcome variable is measured on a nominal or ordinal scale. The technique uses a weighted least squares methodology, and hypotheses are tested using asymptotic chi-square statistics. In this paper, we adapt these procedures to the problem of determining the minimum sample size required for an applied research effort, and use the noncentral versions of these chi-square statistics. The results are compared against several procedures widely used in the literature, and are found to concur well with these techniques. As well, some new situations are considered.
格里兹尔、斯塔默和科赫（1969年，《生物统计学》第25卷，第489 - 503页）提出了一种在结果变量按名义或有序尺度测量时进行数据分析的统一方法。该技术采用加权最小二乘法，并用渐近卡方统计量检验假设。在本文中，我们将这些程序应用于确定应用研究所需的最小样本量问题，并使用这些卡方统计量的非中心形式。将结果与文献中广泛使用的几种程序进行比较，发现与这些技术非常吻合。此外，还考虑了一些新情况。
