Abstract
Research on group differences in response latency often has as its goal the detection of Group x Treatment interactions. However, accumulating evidence suggests that response latencies for different groups are often linearly related, leading to an increased likelihood of finding spurious overadditive interactions in which the slower group produces a larger treatment effect. The authors propose a rate-amount model that predicts linear relationships between individuals and that includes global processing parameters based on large-scale group differences in information processing. These global processing parameters may be used to linearly transform response latencies from different individuals to a common information-processing scale so that small-scale group differences in information processing may be isolated. The authors recommend linear regression and z-score transformations that may be used to augment traditional analyses of raw response latencies.
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