Cohen's g is an effect size of asymmetry (or marginal heterogeneity) for
dependent (paired) contingency tables ranging between 0 (perfect symmetry)
and 0.5 (perfect asymmetry) (see
stats::mcnemar.test()). (Note this is not
not a measure of (dis)agreement between the pairs, but of (a)symmetry.)
cohens_g(x, y = NULL, ci = 0.95, alternative = "two.sided", ...)
a numeric vector or matrix.
a numeric vector; ignored if
Confidence Interval (CI) level
a character string specifying the alternative hypothesis;
Controls the type of CI returned:
A data frame with the effect size (
(possibly with the prefix
Cohens_h) and its CIs (
Confidence intervals are based on the proportion (P = g + 0.5)
confidence intervals returned by
stats::prop.test() (minus 0.5), which give
a good close approximation.
"Confidence intervals on measures of effect size convey all the information
in a hypothesis test, and more." (Steiger, 2004). Confidence (compatibility)
intervals and p values are complementary summaries of parameter uncertainty
given the observed data. A dichotomous hypothesis test could be performed
with either a CI or a p value. The 100 (1 - α)% confidence
interval contains all of the parameter values for which p > α
for the current data and model. For example, a 95% confidence interval
contains all of the values for which p > .05.
Note that a confidence interval including 0 does not indicate that the null (no effect) is true. Rather, it suggests that the observed data together with the model and its assumptions combined do not provided clear evidence against a parameter value of 0 (same as with any other value in the interval), with the level of this evidence defined by the chosen α level (Rafi & Greenland, 2020; Schweder & Hjort, 2016; Xie & Singh, 2013). To infer no effect, additional judgments about what parameter values are "close enough" to 0 to be negligible are needed ("equivalence testing"; Bauer & Kiesser, 1996).
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.
Other effect sizes for contingency table:
data("screening_test") phi(screening_test$Diagnosis, screening_test$Test1) phi(screening_test$Diagnosis, screening_test$Test2) # Both tests seem comparable - but are the tests actually different? (tests <- table(Test1 = screening_test$Test1, Test2 = screening_test$Test2)) mcnemar.test(tests) cohens_g(tests) # Test 2 gives a negative result more than test 1!
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