effectsize (version 0.4.1)

interpret_r: Interpret correlation

Description

Interpret correlation

Usage

interpret_r(r, rules = "funder2019")

Arguments

r

Value or vector of correlation coefficient.

rules

Can be "funder2019" (default), "gignac2016", "cohen1988", "evans1996" or custom set of rules().

Rules

Rules apply positive and negative r alike.

• Funder & Ozer (2019) ("funder2019"; default)

  • r < 0.05 - Tiny

  • 0.05 <= r < 0.1 - Very small

  • 0.1 <= r < 0.2 - Small

  • 0.2 <= r < 0.3 - Medium

  • 0.3 <= r < 0.4 - Large

  • r >= 0.4 - Very large

• Gignac & Szodorai (2016) ("gignac2016")

  • r < 0.1 - Very small

  • 0.1 <= r < 0.2 - Small

  • 0.2 <= r < 0.3 - Moderate

  • r >= 0.3 - Large

• Cohen (1988) ("cohen1988")

  • r < 0.1 - Very small

  • 0.1 <= r < 0.3 - Small

  • 0.3 <= r < 0.5 - Moderate

  • r >= 0.5 - Large

• Evans (1996) ("evans1996")

  • r < 0.2 - Very weak

  • 0.2 <= r < 0.4 - Weak

  • 0.4 <= r < 0.6 - Moderate

  • 0.6 <= r < 0.8 - Strong

  • r >= 0.8 - Very strong

References

• Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: sense and nonsense. Advances in Methods and Practices in Psychological Science.

• Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and individual differences, 102, 74-78.

• Cohen, J. (1988). Statistical power analysis for the behavioural sciences.

• Evans, J. D. (1996). Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co.

See Also

Page 88 of APA's 6th Edition.

Examples

# NOT RUN {
interpret_r(.015)
interpret_r(c(.5, -.02))
# }