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Design-parameter matching in a priori power analysis

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 quant-synthesis), q3 · 0 of 1 report an effect size · 1 claim rests on one study

Description

This principle holds that a priori power analyses should strive for an ideal match between the selected design parameters and the planned study's characteristics: the clustering structure (single-, two-, or three-level), the target outcome domain, the assessment method, and the target age group. The article urges selecting "design parameters that offer the best match to the assumptions of how the data of the planned study will be clustered" and, given heterogeneity, using estimates from the same or a very similar target measure and age group.

Accounts

  • Match design parameters to the key characteristics of the planned intervention study when conducting a priori power analyses: This principle holds that a priori power analyses should strive for an ideal match between the selected design parameters and the planned study's characteristics: the clustering structure (single-, two-, or three-level), the target outcome domain, the assessment method, and the target age group. The article urges selecting "design parameters that offer the best match to the assumptions of how the data of the planned study will be clustered" and, given heterogeneity, using estimates from the same or a very similar target measure and age group. (Martin Brunner et al. (2025))
  • Treat optimal designs as locally optimal and apply a rule of thumb when the optimal number of students per class exceeds typical class size: The article cautions that "the optimal design is only 'locally optimal,' meaning that it is optimal only for the specific values that the researcher specified for the quantities and can be suboptimal for other values," suggesting maximin designs as a workaround. It also notes that power computations assume unbiased estimation, which holds in large samples but not small ones, and offers a practical rule: "set this number to 25 and then increase the number of classes to compensate for the loss of power" when the optimal students per class exceeds typical class size. (Steffen Zitzmann et al. (2021))

Claims

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Key Sources

  • Martin Brunner, Sophie E. Stallasch, Cordula Artelt, Oliver Lüdtke. (2025). An Individual Participant Data Meta-Analysis to Support Power Analyses for Randomized Intervention Studies in Preschool: Cognitive and Socio-Emotional Learning Outcomes. Educational Psychology Review. https://doi.org/10.1007/s10648-024-09981-z
  • Steffen Zitzmann, Wolfgang Wagner, Martin Hecht, Christoph Helm, Christian Fischer, Lisa Bardach, Richard Göllner. (2021). How Many Classes and Students Should Ideally be Sampled When Assessing the Role of Classroom Climate via Student Ratings on a Limited Budget? An Optimal Design Perspective. Educational Psychology Review (2022) 34:511–536. https://doi.org/10.1007/s10648-021-09635-4
Page metadata
Field Value
type research-method
id design-parameter-matching-in-a-priori-power-analysis
title Design-parameter matching in a priori power analysis
description This principle holds that a priori power analyses should strive for an ideal match between the selected design parameters and the planned study's characteristics: the clustering structure (single-, two-, or three-level), the target outcome domain, the assessment method, and the target age group.
status review
generated by process:settle-candidates on 2026-10-19
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