Powering Nutrition Research: Practical Strategies for Sample Size in Multiple Regression.

Area of Science:

• Nutrition Science
• Biostatistics
• Epidemiology

Background:

• Robust statistical analysis, especially multiple regression, is vital for evidence-based nutrition research.
• Inadequate sample size in nutrition studies risks Type II errors and reduces result interpretability.
• Formal sample size justifications are often missing in published nutrition research.

Purpose of the Study:

• To review and compare three common sample size determination approaches for multiple regression in nutrition research.
• To provide a practical educational resource for researchers to optimize sample size decisions.
• To enhance the reproducibility and interpretability of quantitative nutrition research.

Main Methods:

• Methodological review of three sample size approaches: rule of thumb, variance explained (R²), and beta weights.
• Illustration using a consistent hypothetical example to compare sample size recommendations.
• Analysis of advantages, assumptions, and limitations of each method.

Main Results:

• Sample size recommendations vary significantly across the reviewed methods.
• The rule of thumb offers simplicity, the R² method links to model performance, and the beta weights approach provides precision.
• Each method has distinct assumptions and limitations impacting its applicability.

Conclusions:

• Rigorous and transparent sample size planning is essential for advancing nutrition research.
• Choosing the appropriate sample size method depends on study design, objectives, and desired statistical power.
• Improved sample size practices will enhance the reliability and generalizability of nutrition findings.