ESTIMATING INSPECTION TIME - RESPONSE PROBABILITIES, THE BRAT IT ALGORITHM, AND IQ CORRELATIONS

Bates and Eysenck(1993), used a 3rd-order cubic polynomial curve fitti ng procedure on correct-response probabilities computed from the trial record of individual research participants (N = 70) in an inspection time (IT) task. They demonstrated that this methodology produced estim ates of IT that, when correlated with full-scale IQ scores (assessed b y Jackson's Multidimensional Aptitude Battery), provided a measure of agreement that exceeded that given by the Barrett BRAT IT algorithm. T he correlation between IT computed via the BRAT algorithm and full-sca le IQ in this sample was -0.35, that between IQ and the cubic polynomi al estimate was -0.35. When removing one outlier observation from the polynomial estimate data, this correlation increased to -0.47. Further , Bates and Eysenck also removed a further 5 cases from the dataset on the basis of ''bad fit'' of the data by the polynomial function, this had the effect of increasing the correlation to -0.62. However, it is demonstrated in this paper that when systematic, explicit, and quanti fied, criteria are applied to the outlier analysis, and replication of the results is sought across a further four IT datasets, the correlat ions between the BRAT algorithm parameters and those produced from 3 c urve equation functions are actually equivalent. The average systemati c outlier-corrected correlation between IT and IQ for both the BRAT an d cubic polynomial estimates is -0.34. Further, the unadjusted correla tions between BRAT IT estimates and cubic polynomial estimates all exc eed 0.95, across all 5 datasets. It is concluded that given the relati ve difficulty of producing exact polynomial estimates at 0.76 response probability, the inappropriate use of a cubic polynomial for a functi on bounded by (0, 1), and the perhaps inappropriate data produced by t he BRAT algorithm for this type of approach to IT estimation, the use of the curve-fit procedure is sub-optimal with regard to this particul ar form of IT estimation algorithm. (C) 1998 Elsevier Science Ltd. All rights reserved.