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People generally prefer their initials to the other letters of the alphabet, a phenomenonknown 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 aPhilips TV, and Dennis becomes a dentist). In order to establish such associationsbetween people’s initials and their behavior, researchers typically carry outstatistical analyses of large databases. Current methods of analysis ignore thehierarchical structure of the data, do not naturally handle order-restrictions, andare fundamentally incapable of confirming the null hypothesis. Here we outline aBayesian hierarchical analysis that avoids these limitations and allows coherentinference both on the level of the individual and on the level of the group. Toillustrate our method, we re-analyze two data sets that address the question ofwhether people are disproportionately likely to live in cities that resemble theirname.