title: Should We Stop Looking for a Better Scoring Algorithm for Handling Implicit Association Test Data? Test of the Role of Errors, Extreme Latencies Treatment, Scoring Formula, and Practice Trials on Reliability and Validity.
abstract: Since the development of D scores for the Implicit Association Test, few studies have examined whether there is a better scoring method. In this contribution, we tested the effect of four relevant parameters for IAT data that are the treatment of extreme latencies, the error treatment, the method for computing the IAT difference, and the distinction between practice and test critical trials. For some options of these different parameters, we included robust statistic methods that can provide viable alternative metrics to existing scoring algorithms, especially given the specificity of reaction time data. We thus elaborated 420 algorithms that result from the combination of all the different options and test the main effect of the four parameters with robust statistical analyses as well as their interaction with the type of IAT (i.e., with or without built-in penalty included in the IAT procedure). From the results, we can elaborate some recommendations. A treatment of extreme latencies is preferable but only if it consists in replacing rather than eliminating them. Errors contain important information and should not be discarded. The D score seems to be still a good way to compute the difference although the G score could be a good alternative, and finally it seems better to not compute the IAT difference separately for practice and test critical trials. From this recommendation, we propose to improve the traditional D scores with small yet effective modifications.
authors.affiliation: Department of Psychology, University of Milan-Bicocca, Milan, Italy.
authors.forename: Juliette
authors.initials: J
authors.lastname: Richetin
authors.affiliation: Department of Psychology, University of Milan-Bicocca, Milan, Italy.
authors.forename: Giulio
authors.initials: G
authors.lastname: Costantini
authors.affiliation: Department of Psychology, University of Milan-Bicocca, Milan, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Perugini
authors.affiliation: Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.
authors.forename: Felix
authors.initials: F
authors.lastname: Schönbrodt
mesh_terms: D000328:Adult; D000465:Algorithms; D001244:Association; D001290:Attitude; D003071:Cognition; D003627:Data Interpretation, Statistical; D005006:Ethnicity; D006801:Humans; D011057:Politics; D011594:Psychometrics / Q000379:methods*; D011930:Reaction Time; D015203:Reproducibility of Results; D012649:Self Concept; D013223:Statistics as Topic