Treffer: Accuracy in parameter estimation and simulation approaches for sample-size planning accounting for item effects.

The planning of sample size for research studies often focuses on obtaining a significant result given a specified level of power, significance, and an anticipated effect size. This planning requires prior knowledge of the study design and a statistical analysis to calculate the proposed sample size. However, there may not be one specific testable analysis from which to derive power (Silberzahn et al., Advances in Methods and Practices in Psychological Science, 1(3), 337356, 2018) or a hypothesis to test for the project (e.g., creation of a stimuli database). Modern power and sample size planning suggestions include accuracy in parameter estimation (AIPE, Kelley, Behavior Research Methods, 39(4), 755-766, 2007; Maxell et al., Annual Review of Psychology, 59, 537-563, 2008) and simulation of proposed analyses (Chalmers & Adkins, The Quantitative Methods for Psychology, 16(4), 248-280, 2020). These toolkits offer flexibility in traditional power analyses that focus on the if-this, then-that approach. However, both AIPE and simulation require either a specific parameter (e.g., mean, effect size, etc.) or a statistical test for planning sample size. In this tutorial, we explore how AIPE and simulation approaches can be combined to accommodate studies that may not have a specific hypothesis test or wish to account for the potential of a multiverse of analyses. Specifically, we focus on studies that use multiple items and suggest that sample sizes can be planned to measure those items adequately and precisely, regardless of the statistical test. This tutorial also provides multiple code vignettes and package functionality that researchers can adapt and apply to their own measures.
(© 2026. The Author(s).)

Declarations. Conflicts of Interest: The researchers declare no conflicts of interest. Erin Buchanan is a co-editor of Behavior Research Methods but had no involvement in the review process or the editorial decision for this manuscript. Ethical Approval: Not applicable, simulation study. Consent to participate: Not applicable, simulation study. Consent for publication: All authors approved the manuscript, no participant approval necessary. Materials: No materials were used. Analysis Code: Manuscript repository with code and data: https://osf.io/swmva/ or https://github.com/SemanticPriming/stimuli-power Package repository with vignettes and data: https://github.com/SemanticPriming/semanticprimeR Pre-registration: We did not pre-register this study, as it was a simulation study.