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Correlational biases in mean response latency differences.


ABSTRACT: Multifarious psychological constructs are indexed by the mean latency difference (MLD), the within-subject difference between mean response latency on two tasks. Two associations consistently emerge in mean latency data. Firstly, across subjects, mean latencies on distinct tasks are positively correlated. This correlation arises from individual differences in general rates of information processing that are a shared influence on response latency in diverse tasks. Secondly, across tasks, the mean and variance of mean latency are positively correlated. Compared to a simple task, a complex task has both a larger average mean latency and a larger variance of mean latency, across subjects. Taken together, these associations make the interpretation of the MLD problematic by biasing correlations between the MLD and (a) task mean latencies, (b) the average of the mean latencies, (c) external criteria, and (d) other MLDs. A variety of mean latency transformations were evaluated and, while they differed in their effectiveness, they did not satisfactorily rectify MLD biases. An alternative approach, focusing on scale invariant contrasts of within-subject response latency distributions, is introduced in the conclusion.

SUBMITTER: Sriram N 

PROVIDER: S-EPMC2879027 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Correlational biases in mean response latency differences.

Sriram N N   Greenwald Anthony G AG   Nosek Brian A BA  

Statistical methodology 20100501 3


Multifarious psychological constructs are indexed by the mean latency difference (MLD), the within-subject difference between mean response latency on two tasks. Two associations consistently emerge in mean latency data. Firstly, across subjects, mean latencies on distinct tasks are positively correlated. This correlation arises from individual differences in general rates of information processing that are a shared influence on response latency in diverse tasks. Secondly, across tasks, the mean  ...[more]

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