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September 10, 2025

Peaks and pitfalls: Unwrapping four overlooked problems in multilevel modeling of cyclic trends and their circular solutions

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This analysis highlights critical flaws in multilevel modeling of cyclic trends, suggesting approaches from circular statistics.

Many individuals may not exhibit a cycle at all, undermining standard modeling methods used in psychology.
Current estimation methods can lead to peak time estimates that are inaccurate by several hours, inflating uncertainty.
Integration of circular statistics within a Bayesian framework is proposed to correct flaws in peak timing assessment.
Conventional correlation measures for peak timing become unreliable due to their dependence on arbitrary cycle start times.

Haqiqatkhah et al. (2025) studied this question.

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September 10, 2025

View Full Paper

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Authors

This analysis highlights critical flaws in multilevel modeling of cyclic trends, suggesting approaches from circular statistics.

Many individuals may not exhibit a cycle at all, undermining standard modeling methods used in psychology.
Current estimation methods can lead to peak time estimates that are inaccurate by several hours, inflating uncertainty.
Integration of circular statistics within a Bayesian framework is proposed to correct flaws in peak timing assessment.
Conventional correlation measures for peak timing become unreliable due to their dependence on arbitrary cycle start times.

Haqiqatkhah et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark