♥ Support

Reliability

Multi-trial averaging and reliability analysis for repeated measurements. Compute ICC, CV, SEM, and MDC from multiple trials of the same movement.

Input format: one row per subject, one column per rater or repeated trial (first row = headers). Cells are the measurements — numbers for continuous data (angles, forces, times) or category labels for ratings. The wizard picks the right statistic (ICC form or kappa) from your data and design. Two columns also draw a Bland–Altman plot.
📊 Sample reliability data
Rows = subjects · columns = raters/trials · first row = headers.
What are you assessing?
Design
Status
Load a file to begin.
ICC interpretation

The wizard selects the ICC form (Shrout & Fleiss notation) from your design; all six are shown in the results. Categorical data uses Cohen's / weighted / Fleiss' kappa instead.

Benchmarks (Koo & Li, 2016):

  • Poor: < 0.50
  • Moderate: 0.50 – 0.75
  • Good: 0.75 – 0.90
  • Excellent: > 0.90

SEM = SDpooled × √(1 – ICC). Expressed in original units.

MDC = 1.96 × √2 × SEM. The smallest change that exceeds measurement error at 95% confidence.

References

ICC guidelines: Koo, T.K., & Li, M.Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163.

Bland-Altman: Bland, J.M., & Altman, D.G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307-310.

MDC: Weir, J.P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. Journal of Strength and Conditioning Research, 19(1), 231-240.

biomex.studio2026 Andrew Callaway