Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment.

Psychonomic Bulletin & Review
Thomas PronkJaap Murre

Abstract

Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was stro...Continue Reading

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