Department of Psychology, University of Tübingen Tübingen, Germany.
Front Psychol. 2012 Sep 25;3:334. doi: 10.3389/fpsyg.2012.00334. eCollection 2012.
People generally prefer their initials to the other letters of the alphabet, a phenomenon known as the name-letter effect. This effect, researchers have argued, makes people move to certain cities, buy particular brands of consumer products, and choose particular professions (e.g., Angela moves to Los Angeles, Phil buys a Philips TV, and Dennis becomes a dentist). In order to establish such associations between people's initials and their behavior, researchers typically carry out statistical analyses of large databases. Current methods of analysis ignore the hierarchical structure of the data, do not naturally handle order-restrictions, and are fundamentally incapable of confirming the null hypothesis. Here we outline a Bayesian hierarchical analysis that avoids these limitations and allows coherent inference both on the level of the individual and on the level of the group. To illustrate our method, we re-analyze two data sets that address the question of whether people are disproportionately likely to live in cities that resemble their name.
人们通常更喜欢他们名字的首字母而不是其他字母，这种现象被称为名字首字母效应。研究人员认为，这种效应使人们搬到某些城市，购买特定品牌的消费品，并选择特定的职业（例如，安吉拉搬到洛杉矶，菲尔购买飞利浦电视，丹尼斯成为牙医）。为了在人们的首字母和他们的行为之间建立这种联系，研究人员通常会对大型数据库进行统计分析。当前的分析方法忽略了数据的层次结构，不能自然处理顺序限制，并且从根本上无法确认零假设。在这里，我们概述了一种贝叶斯层次分析方法，该方法可以避免这些限制，并允许在个体和群体两个层面上进行连贯的推断。为了说明我们的方法，我们重新分析了两个数据集，这些数据集探讨了人们是否更有可能居住在与其名字相似的城市的问题。
