Department of Psychology, Lake Forest College, Lake Forest, Illinois, USA.
Behav Res Methods. 2010 Feb;42(1):36-41. doi: 10.3758/BRM.42.1.36.
Many researchers studying the effectiveness of working in groups have compared group performance with the scores of individuals combined into nominal groups. Traditionally, methods for forming nominal groups have been shown to be poor, and more recent procedures (Wright, 2007) are difficult to use for complex designs and are inflexible. A new procedure is introduced and tested in which thousands of possible combinations of nominal groups are sampled. Sample characteristics, such as the mean, variance, and distribution, of all these sets are calculated, and the set that is most representative of all of these sets is returned. The user can choose among different ways of conceptualizing the meaning of most representative, but on the basis of simulations and the fact that most subsequent statistical procedures are based on the mean and variance, we argue that finding the set with the mean and variance most similar to the means of the representative statistics for all of the sets is the preferred approach. The algorithm is implemented in a stand-alone C++ executable program and as an R function. Both of these allow anyone to use the procedures freely.
许多研究小组工作效率的研究人员将小组的表现与名义小组组合的分数进行了比较。传统上，已经证明形成名义小组的方法很差，而最近的程序（Wright，2007）对于复杂的设计很难使用，并且不灵活。引入并测试了一种新程序，其中对数千个可能的名义小组组合进行了抽样。计算了所有这些组合的样本特征，例如平均值、方差和分布，并且返回了最能代表所有这些组合的组合。用户可以在不同的概念化方式之间进行选择，但是基于模拟以及大多数后续统计程序都基于平均值和方差的事实，我们认为找到与所有组合的代表统计数据的平均值和方差最相似的组合的方法是首选方法。该算法已在独立的 C++可执行程序和 R 函数中实现。这两种方法都允许任何人免费使用这些程序。
