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Estimating sensitivity and bias in a yes/no task
Author(s) -
Hautus Michael J.,
Lee Alan
Publication year - 2006
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711005x65753
Subject(s) - contingency table , transformation (genetics) , sensitivity (control systems) , variance (accounting) , mathematics , task (project management) , inverse , statistics , algorithm , table (database) , estimation , computer science , econometrics , data mining , biochemistry , chemistry , geometry , accounting , management , electronic engineering , economics , engineering , business , gene
The estimation of sensitivity and bias from data collected in a yes/no detection‐theoretic experiment is complicated by the possibility of proportions of 0 or 1 appearing in the resulting contingency table. Inverse normal transforms of these probabilities result in mathematically intractable infinities. Typically, some transformation of the data must be applied prior to parameter estimation. Several transformations have been reviewed in the literature, in terms of both the bias and the variance of the estimates they produce. We propose three generalized transformations, which contain the two most reported transformations as special cases, and consider their performance in terms of the mean square error of the estimates they produce. Results indicate that the ‘1/ N ’ and the adaptive log‐linear transformations outperform the others. Guidelines for the application of these transformations are presented.