# interpret_r: Interpret Correlation Coefficient In effectsize: Indices of Effect Size

 interpret_r R Documentation

## Interpret Correlation Coefficient

### Description

Interpret Correlation Coefficient

### Usage

interpret_r(r, rules = "funder2019", ...)

interpret_phi(r, rules = "funder2019", ...)

interpret_cramers_v(r, rules = "funder2019", ...)

interpret_rank_biserial(r, rules = "funder2019", ...)


### Arguments

 r Value or vector of correlation coefficient. rules Can be "funder2019" (default), "gignac2016", "cohen1988", "evans1996", "lovakov2021" or a custom set of rules(). ... Not directly used.

### 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

• Lovakov & Agadullina (2021) ("lovakov2021")

• r < 0.12 - Very small

• 0.12 <= r < 0.24 - Small

• 0.24 <= r < 0.41 - Moderate

• r >= 0.41 - 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

### Note

As \phi can be larger than 1 - it is recommended to compute and interpret Cramer's V instead.

### References

• Lovakov, A., & Agadullina, E. R. (2021). Empirically Derived Guidelines for Effect Size Interpretation in Social Psychology. European Journal of Social Psychology.

• 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 behavioral sciences (2nd Ed.). New York: Routledge.

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

interpret_r(.015)