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. 2023 Jan 5;56(1):301–317. doi: 10.3758/s13428-022-02038-5

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© The Author(s) 2023

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — Permutation analyses of the effect of task length and sample size on the reliability of RT-derived learning scores. a We varied the number of blocks (max 45) to be included in the reliability calculation. The top figure shows the Cronbach alpha, and its Feldt 95% CI for each task length. The dashed horizontal line indicates the .65 level. The bottom figure shows the width of the 95% CI only. Increasing task length increases the point estimate of reliability, but has only a minor effect on its precision. b We varied the number of subjects (max 180) to be included in the reliability calculation. The top figure shows the mean Cronbach alpha across 100 random samples of subjects, and its Feldt 95% CI, for each sample size tested. The dashed horizontal line indicates the .65 level. The bottom figure shows the width of the 95% CI only. Increasing sample size has no effect on the point estimate of reliability, but increases its precision