How to find what's in a name: Scrutinizing the optimality of five scoring algorithms for the name-letter task



Although the name-letter task (NLT) has become an increasingly popular technique to measure implicit self-esteem (ISE), researchers have relied on different algorithms to compute NLT scores and the psychometric properties of these differently computed scores have never been thoroughly investigated. Based on 18 independent samples, including 2690 participants, the current research examined the optimality of five scoring algorithms based on the following criteria: reliability; variability in reliability estimates across samples; types of systematic error variance controlled for; systematic production of outliers and shape of the distribution of scores. Overall, an ipsatized version of the original algorithm exhibited the most optimal psychometric properties, which is recommended for future research using the NLT. Copyright © 2009 John Wiley & Sons, Ltd.