interpret_r: Interpret Correlation Coefficient

In effectsize: Indices of Effect Size

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", ...)

interpret_fei(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.

Details

Since Cohen's w does not have a fixed upper bound, for all by the most simple of cases (2-by-2 or 1-by-2 tables), interpreting Cohen's w as a correlation coefficient is inappropriate (Ben-Shachar, et al., 2024; Cohen, 1988, p. 222). Please us cramers_v() of the like instead.

Rules

Rules apply to 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.

• Ben-Shachar, M.S., Patil, I., Thériault, R., Wiernik, B.M., Lüdecke, D. (2023). Phi, Fei, Fo, Fum: Effect Sizes for Categorical Data That Use the Chi‑Squared Statistic. Mathematics, 11, 1982. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3390/math11091982")}

See Also

Page 88 of APA's 6th Edition.

Examples

interpret_r(.015)
interpret_r(c(.5, -.02))
interpret_r(.3, rules = "lovakov2021")

effectsize documentation built on July 3, 2024, 9:07 a.m.