PowerUpR: Statistical Power Analysis Tools for Multilevel Randomized Experiments

PowerUpR provides tools for calculating statistical power, minimum detectable effect size, and required sample sizes for multilevel randomized experiments. The package accommodates 14 types of designs for main treatment effects, seven for moderated effects, five for mediated effects, and several partially nested designs. It was created by Metin Buluş and is associated with the 'PowerUp!' Excel series.

Use Cases

• Calculate statistical power for a planned multilevel experiment based on the described design types.
• Determine the minimum detectable effect size for a main treatment effect based on the 14 supported MRE designs.
• Plan sample size requirements for detecting moderated treatment effects based on the seven supported design types.
• Analyze power for mediated treatment effects using the five supported design structures.

Strengths

• Supports a specific set of 14 multilevel randomized experiment designs for main treatment effects.
• Includes tools for seven distinct design types to detect moderated treatment effects.
• Provides calculations for five design types to detect mediated treatment effects.

Limitations

• Row count and dataset scale are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Metin Buluş, associated with the 'PowerUp!' Excel series at causalevaluation.org.

Collection Method

Likely a software package or collection of tools for statistical calculation.

Time Range

Temporal coverage is unknown.

Freshness

Last updated date is unknown.

Geography

Spatial coverage is unknown.