July 14, 2025

Simulation-based sample size planning for Rasch family models

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Larger sample sizes lead to improved statistical power for Rasch models in varying conditions.
Polytomous models and complex item-design matrices can achieve high power due to lower variances.
Power analyses were conducted using simulation methods, applying R packages like lme4 and mixedpower.
Findings advance the field of experimental psychology by aiding in robust study design and sample size determination.

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Implication

Simulation-based power analyses reveal that larger sample sizes improve statistical power in Rasch models, indicating a practical approach for study design.

Cite This Study

Hanadate et al. (2025) studied this question.

synapsesocial.com/papers/689a02b6e6551bb0af8cc426 https://doi.org/10.31219/osf.io/pu53j_v2

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Implication

Simulation-based power analyses reveal that larger sample sizes improve statistical power in Rasch models, indicating a practical approach for study design.

Simulation-based sample size planning for Rasch family models

Key Points

Discussion

Implication

Cite This Study

Simulation-based sample size planning for Rasch family models

Key Points

Discussion

Implication

Cite This Study

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