library(knitr) options(knitr.kable.NA = "") knitr::opts_chunk$set(comment = ">") options(digits = 3) set.seed(7) .eval_if_requireNamespace <- function(...) { pkgs <- c(...) knitr::opts_chunk$get("eval") && all(vapply(pkgs, requireNamespace, TRUE, quietly = TRUE)) }

This vignette provides a review of effect sizes for 1- and 2-D contingency
tables, which are typically analysed with `chisq.test()`

and `fisher.test()`

.

library(effectsize) options(es.use_symbols = TRUE) # get nice symbols when printing! (On Windows, requires R >= 4.2.0)

For 2-by-2 contingency tables, $\phi$ (Phi) is homologous (though directionless) to the biserial correlation between two dichotomous variables, with 0 representing no association, and 1 representing a perfect association.

(MPG_Gear <- table(mtcars$mpg < 20, mtcars$vs)) phi(MPG_Gear, adjust = FALSE) # Same as: cor(mtcars$mpg < 20, mtcars$vs)

A "cousin" effect size is Pearson's contingency coefficient, however it is not a true measure of correlation, but rather a type of normalized $\chi^2$ (see `chisq_to_pearsons_c()`

):

```
pearsons_c(MPG_Gear)
```

For larger contingency tables, Cramér's *V*, Tschuprow's *T*, Cohen's *w*, and Pearson's *C* can be used.

Both Cramér's *V* and Tschuprow's *T* are true measures of correlation: they range from 0 to 1, with 0 indicating complete independence between the nominal variables, and 1 indicating complete dependence. For square tables, they are equal, however for non-square tables $T < V$; While Cramér's *V* defines complete dependence as "it is possible to know exactly the value of the columns from the rows *or* know exactly the value of the rows from the columns", Tschuprow's *T* required both to be true to achieve complete dependence - something that is not possible for non-square tables. For example:

data("food_class") food_class

In this case, if you know the food product, you know if it is vegan or not, but knowing if the food is vegan or not will not always let you know what food product it is.

cramers_v(food_class, adjust = FALSE) tschuprows_t(food_class, adjust = FALSE)

Cohen's *w* and Pearson's *C* are not true measures of correlation, but are two
types of normalized $\chi^2$ values. While Pearson's *C* is capped at 1, Cohen's
*w* can be larger than 1 (for both, 0 indicates no association between the
variables).

data("Music_preferences2") Music_preferences2 chisq.test(Music_preferences2) cramers_v(Music_preferences2) tschuprows_t(Music_preferences2) pearsons_c(Music_preferences2) cohens_w(Music_preferences2) # > 1

(Cramer's *V*, Tschuprow's *T*, and Cohen's *w* can also be used for 2-by-2 tables, but there they are equivalent to $\phi$.)

A Bayesian estimate of these effect sizes can also be provided based on
`BayesFactor`

's version of a $\chi^2$-test via the `effectsize()`

function:

library(BayesFactor) BFX <- contingencyTableBF(MPG_Gear, sampleType = "jointMulti") effectsize(BFX, type = "phi") # for 2 * 2 BFX <- contingencyTableBF(Music_preferences2, sampleType = "jointMulti") effectsize(BFX, type = "cramers_v") effectsize(BFX, type = "tschuprows_t") effectsize(BFX, type = "cohens_w") effectsize(BFX, type = "pearsons_c")

Cohen's *w* and Pearson's *C* are also applicable to tests of goodness-of-fit,
where they indicate the degree of deviation from the hypothetical probabilities,
with 0 reflecting no deviation.

O <- c(89, 37, 130, 28, 2) # observed group sizes E <- c(.40, .20, .20, .15, .05) # expected group freq chisq.test(O, p = E) pearsons_c(O, p = E) cohens_w(O, p = E)

However, these values are not (anti)correlations - they do not properly adjust for the "shape" of the expected multinational distribution, making them inflated (additionally, Cohen's *w* can be larger than 1, making it harder to interpret).

For these reasons, we recommend the $פ$ (Fei) coefficient, which is a measure of anti-correlation between the observed and the expected distributions, ranging from 0 (observed distribution matches the expected distribution perfectly) and 1 (the observed distribution is maximally different than the expected one).

fei(O, p = E) # Observed perfectly matches Expected (O1 <- E * 286) fei(O1, p = E) # Observed deviates maximally from Expected: # All observed values are in the least expected class! (O2 <- c(rep(0, 4), 286)) fei(O2, p = E)

For 2-by-2 tables, we can also compute the Odds-ratio (OR), where each column
represents a different *group*. Values larger than 1 indicate that the odds are
higher in the first group (and vice versa).

data("RCT_table") RCT_table chisq.test(RCT_table) # or fisher.test(RCT_table) oddsratio(RCT_table)

We can also compute the Risk-ratio (RR), which is the ratio between the
proportions of the two groups, and the Absolute Risk Reduction (ARR), which is
the *difference* between the proportions of the two groups - both are measures
which some claim to be more intuitive.

riskratio(RCT_table) arr(RCT_table)

Additionally, Cohen's *h* can also be computed, which uses the *arcsin*
transformation. Negative values indicate smaller proportion in the first group
(and vice versa).

```
cohens_h(RCT_table)
```

A Bayesian estimate of these effect sizes can also be provided based on
`BayesFactor`

's version of a $\chi^2$-test via the `effectsize()`

function:

BFX <- contingencyTableBF(RCT_table, sampleType = "jointMulti") effectsize(BFX, type = "or") effectsize(BFX, type = "rr") effectsize(BFX, type = "cohens_h")

For dependent (paired) contingency tables, Cohen's *g* represents the symmetry
of the table, ranging between 0 (perfect symmetry) and 0.5 (perfect asymmetry).

For example, these two tests seem to be equally predictive of the disease they are screening:

data("screening_test") phi(screening_test$Diagnosis, screening_test$Test1) phi(screening_test$Diagnosis, screening_test$Test2)

Does this mean they give the same number of positive/negative results?

tests <- table(Test1 = screening_test$Test1, Test2 = screening_test$Test2) tests mcnemar.test(tests) cohens_g(tests)

Test 1 gives more positive results than test 2!

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