Reliability Statistics in Strength and Conditioning

Reliability Statistics in Strength and Conditioning

Introduction

  • Reliability statistics are crucial for strength and conditioning coaches to assess the consistency and accuracy of measurements.
  • These statistics help determine if a real change in performance has occurred.

Types of Reliability Statistics

  • Intraclass Correlation Coefficient (ICC)
  • Coefficient of Variation (CV)
  • Standard Error of the Measurement (SEM)
  • Typical Error (TE)

Intraclass Correlation Coefficient (ICC)

  • Assesses the relative consistency of a measure.
  • Indicates how consistent participants are relative to the sample.
  • Commonly required by journals for research publication.
  • Multiple types exist (6-10), but type 3,1 is generally used in strength and conditioning for test-retest within the same group.
  • Interpretation:
    • It's a correlation coefficient, so it estimates relative consistency.
    • Interpret based on the 95% confidence interval to assess where the true value may lie.
    • Use a scale proposed by Koo and Lee (2016) based on the lower bound of the 95% confidence interval.
    • Qualitative scale:
      • < 0.5: Poor relative consistency/reliability.
      • > 0.9: Excellent relative consistency/reliability.
  • Limitation: Doesn't provide an intuitive understanding of what relative reliability means.

Standard Error of the Measurement (SEM)

  • Calculates the absolute consistency of a test.
  • Used in conjunction with ICC to determine test reliability.
  • Related to ICC, calculated as: SEM = \text{Standard Deviation} \times \sqrt{1 - ICC}
  • Commonly used to calculate other statistics like the coefficient of variation.

Coefficient of Variation (CV)

  • Most common reliability statistic in strength and conditioning.
  • Represents the absolute consistency of the measurement.
  • Calculated as a percentage: CV = (\frac{\text{Standard Deviation}}{\text{Mean}}) \times 100
  • Interpreted alongside 95% confidence intervals.
  • Thresholds exist to determine acceptable levels of measurement error (e.g., < 10% is considered reliable).
  • Example: Force-time curve characteristics in isometric mid-thigh pull and isometric squat.
  • Importance of considering confidence intervals: Don't rely solely on point estimates.

Within-Individual Reliability

  • CV can assess the reliability of an athlete within a test.
  • Indicates the error an athlete displays relative to their actual performance.
  • Helps determine if a change in performance is meaningful, regardless of whether monitoring fatigue or performance enhancement.
  • Meaningful change: A change in performance exceeding the level of within-individual reliability.
  • Calculation:
    • Repeated trials for the athlete.
    • Calculate the average (true value) and standard deviation.
    • CV = (\frac{\text{Standard Deviation}}{\text{Mean}}) \times 100
  • Example: Athlete A performs countermovement jumps (47 cm, 48 cm, 47.8 cm, 47.6 cm, 45.6 cm).
    • Average = 47 cm.
    • Standard Deviation = 1.2 cm.
    • CV = (\frac{1.2}{47}) \times 100 = 2.57 \%.
    • Any change > 2.57% is likely meaningful.

Typical Error (TE)

  • Similar to the standard error of the measure.
  • Represents the typical variation expected for an athlete between trials or sessions.
  • Expressed in the real unit of measurement (e.g., meters per second for barbell velocity).
  • Used to determine within-athlete or between-session variability.
  • Calculation:
    1. Conduct a series of repeated trials.
    2. Calculate the standard deviation of the test outcome.
    3. Divide the standard deviation by the square root of two: TE = \frac{\text{Standard Deviation}}{\sqrt{2}}.
  • Can be used to calculate a form of the coefficient of variation.

Conclusion

  • Understanding and careful interpretation of reliability statistics and their associated confidence intervals are essential when reading sport science literature.
  • Close reading of method sections of studies is essential to properly interpret the results.