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      What’s in a Name: A Bayesian Hierarchical Analysis of the Name-Letter Effect

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          Abstract

          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.

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          Most cited references39

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          Bayes Factors

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            Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method.

            In the field of cognitive psychology, the p-value hypothesis test has established a stranglehold on statistical reporting. This is unfortunate, as the p-value provides at best a rough estimate of the evidence that the data provide for the presence of an experimental effect. An alternative and arguably more appropriate measure of evidence is conveyed by a Bayesian hypothesis test, which prefers the model with the highest average likelihood. One of the main problems with this Bayesian hypothesis test, however, is that it often requires relatively sophisticated numerical methods for its computation. Here we draw attention to the Savage-Dickey density ratio method, a method that can be used to compute the result of a Bayesian hypothesis test for nested models and under certain plausible restrictions on the parameter priors. Practical examples demonstrate the method's validity, generality, and flexibility. Copyright 2009 Elsevier Inc. All rights reserved.
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              Implicit social cognition: attitudes, self-esteem, and stereotypes.

              Social behavior is ordinarily treated as being under conscious (if not always thoughtful) control. However, considerable evidence now supports the view that social behavior often operates in an implicit or unconscious fashion. The identifying feature of implicit cognition is that past experience influences judgment in a fashion not introspectively known by the actor. The present conclusion--that attitudes, self-esteem, and stereotypes have important implicit modes of operation--extends both the construct validity and predictive usefulness of these major theoretical constructs of social psychology. Methodologically, this review calls for increased use of indirect measures--which are imperative in studies of implicit cognition. The theorized ordinariness of implicit stereotyping is consistent with recent findings of discrimination by people who explicitly disavow prejudice. The finding that implicit cognitive effects are often reduced by focusing judges' attention on their judgment task provides a basis for evaluating applications (such as affirmative action) aimed at reducing such unintended discrimination.
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                Author and article information

                Journal
                Front Psychol
                Front Psychol
                Front. Psychology
                Frontiers in Psychology
                Frontiers Research Foundation
                1664-1078
                25 September 2012
                2012
                : 3
                : 334
                Affiliations
                [1] 1simpleDepartment of Psychology, University of Tübingen Tübingen, Germany
                [2] 2simpleDepartment of Psychology, University of Amsterdam Amsterdam, Netherlands
                Author notes

                Edited by: Heather M. Buzick, Educational Testing Service, USA

                Reviewed by: Xin-Yuan Song, Chinese University of Hong Kong, Hong Kong; Yanyan Sheng, Southern Illinois University, USA; Daisy Rutstein, SRI International, USA

                *Correspondence: Eric-Jan Wagenmakers, Department of Psychology, University of Amsterdam, Weesperplein 4, 1018 XA Amsterdam, Netherlands. e-mail: ej.wagenmakers@ 123456gmail.com

                This article was submitted to Frontiers in Quantitative Psychology and Measurement, a specialty of Frontiers in Psychology.

                Article
                10.3389/fpsyg.2012.00334
                3457077
                23055989
                c09ab3af-56b0-40ac-b30f-e20e2b065484
                Copyright © 2012 Dyjas, Grasman, Wetzels, van der Maas and Wagenmakers.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 09 May 2012
                : 20 August 2012
                Page count
                Figures: 5, Tables: 3, Equations: 5, References: 72, Pages: 14, Words: 11822
                Categories
                Psychology
                Methods Article

                Clinical Psychology & Psychiatry
                analysis of large databases,bayesian hierarchical hypothesis test,name-letter effect,order-restrictions,random effects

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