library(knitr) options(knitr.kable.NA = "") options(digits = 2) knitr::opts_chunk$set(comment = ">", warning = FALSE) set.seed(1) pkgs <- c("effectsize", "parameters", "car", "afex") if (!all(sapply(pkgs, require, quietly = TRUE, character.only = TRUE))) { knitr::opts_chunk$set(eval = FALSE) }

In the context of ANOVA-like tests, it is common to report ANOVA-like effect sizes. Unlike standardized parameters, these effect sizes represent the amount of variance explained by each of the model's terms, where each term can be represented by 1 *or more* parameters.

For example, in the following case, the parameters for the `treatment`

term represent specific contrasts between the factor's levels (treatment groups) - the difference between each level and the reference level (`obk.long == 'control'`

).

data(obk.long, package = "afex") # modify the data slightly for the demonstration: obk.long <- obk.long[1:240 %% 3 == 0, ] obk.long$id <- seq_len(nrow(obk.long)) m <- lm(value ~ treatment, data = obk.long) parameters::model_parameters(m)

But we can also ask about the overall effect of `treatment`

- how much of the
variation in our dependent variable `value`

can be predicted by (or explained
by) the variation between the `treatment`

groups. Such a question can be
answered with an ANOVA test:

parameters::model_parameters(anova(m))

As we can see, the variance in `value`

(the *sums-of-squares*, or *SS*) has been
split into pieces:

- The part associated with
`treatment`

. - The unexplained part (The Residual-
*SS*).

We can now ask what is the percent of the total variance in `value`

that is
associated with `treatment`

. This measure is called Eta-squared (written as
$\eta^2$):

$$ \eta^2 = \frac{SS_{effect}}{SS_{total}} = \frac{72.23}{72.23 + 250.96} = 0.22 $$

and can be accessed via the `eta_squared()`

function:

library(effectsize) eta_squared(m, partial = FALSE)

When we add more terms to our model, we can ask two different questions about
the percent of variance explained by a predictor - how much variance is
accounted by the predictor in *total*, and how much is accounted when
*controlling* for any other predictors. The latter questions is answered by the
*partial*-Eta squared ($\eta^2_p$), which is the percent of the **partial**
variance (after accounting for other predictors in the model) associated with a
term:

$$
\eta^2_p = \frac{SS_{effect}}{SS_{effect} + SS_{error}}
$$
which can also be accessed via the `eta_squared()`

function:

m <- lm(value ~ gender + phase + treatment, data = obk.long) eta_squared(m, partial = FALSE) eta_squared(m) # partial = TRUE by default

*( phase is a repeated-measures variable, but for simplicity it is not modeled as such.)*

In the calculation above, the *SS*s were computed sequentially - that is the
*SS* for `phase`

is computed after controlling for `gender`

, and the *SS* for
`treatment`

is computed after controlling for both `gender`

and `phase`

. This
method of sequential *SS* is called also *type-I* test. If this is what you
want, that's great - however in many fields (and other statistical programs) it
is common to use "simultaneous" sums of squares (*type-II* or *type-III* tests),
where each *SS* is computed controlling for all other predictors, regardless of
order. This can be done with `car::Anova(type = ...)`

:

eta_squared(car::Anova(m, type = 2), partial = FALSE) eta_squared(car::Anova(m, type = 3)) # partial = TRUE by default

$\eta^2_p$ will always be larger than $\eta^2$. The idea is to simulate the
effect size in a design where only the term of interest was manipulated. This
terminology assumes some causal relationship between the predictor and the
outcome, which reflects the experimental world from which these analyses and
measures hail; However, $\eta^2_p$ can also simply be seen as a
**signal-to-noise- ratio**, as it only uses the term's *SS* and the error-term's
*SS*.[^in repeated-measure designs the term-specific residual-*SS* is used for
the computation of the effect size].

(Note that in a one-way fixed-effect designs $\eta^2 = \eta^2_p$.)

Type II and type III treat interaction differently.
Without going into the weeds here, keep in mind that **when using type III SS, it is important to center all of the predictors**;
for numeric variables this can be done by mean-centering the predictors;
for factors this can be done by using orthogonal coding (such as `contr.sum`

for *effects-coding*) for the dummy variables (and *NOT* treatment coding, which is the default in R).
This unfortunately makes parameter interpretation harder, but *only* when this is does do the *SS*s associated with each lower-order term (or lower-order interaction) represent the ** SS** of the

# compare m_interaction1 <- lm(value ~ treatment * gender, data = obk.long) # to: m_interaction2 <- lm( value ~ treatment * gender, data = obk.long, contrasts = list( treatment = "contr.sum", gender = "contr.sum" ) ) eta_squared(car::Anova(m_interaction1, type = 3)) eta_squared(car::Anova(m_interaction2, type = 3))

If all of this type-III-effects-coding seems like a hassle, you can use the `afex`

package, which takes care of all of this behind the scenes:

library(afex) m_afex <- aov_car(value ~ treatment * gender + Error(id), data = obk.long) eta_squared(m_afex)

These effect sizes are unbiased estimators of the population's $\eta^2$:

**Omega Squared**($\omega^2$)**Epsilon Squared**($\epsilon^2$), also referred to as*Adjusted Eta Squared*.

omega_squared(m_afex) epsilon_squared(m_afex)

Both $\omega^2$ and $\epsilon^2$ (and their partial counterparts, $\omega^2_p$ & $\epsilon^2_p$) are unbiased estimators of the population's $\eta^2$ (or $\eta^2_p$, respectively), which is especially important is small samples. Though $\omega^2$ is the more popular choice [@albers2018power], $\epsilon^2$ is analogous to adjusted-$R^2$ [@allen2017statistics, p. 382], and has been found to be less biased [@carroll1975sampling].

*Partial* Eta squared aims at estimating the effect size in a design where only
the term of interest was manipulated, assuming all other terms are have also
manipulated. However, not all predictors are always manipulated - some can only
be observed. For such cases, we can use *generalized* Eta squared ($\eta^2_G$),
which like $\eta^2_p$ estimating the effect size in a design where only the term
of interest was manipulated, accounting for the fact that some terms cannot be
manipulated (and so their variance would be present in such a design).

eta_squared(m_afex, generalized = "gender")

$\eta^2_G$ is useful in repeated-measures designs, as it can estimate what a
*within-subject* effect size would have been had that predictor been manipulated
*between-subjects* [@olejnik2003generalized].

Finally, we have the forgotten child - Cohen's $f$. Cohen's $f$ is a
transformation of $\eta^2_p$, and is the ratio between the term-*SS* and the
error-*SS*.

$$\text{Cohen's} f_p = \sqrt{\frac{\eta^2_p}{1-\eta^2_p}} = \sqrt{\frac{SS_{effect}}{SS_{error}}}$$

It can take on values between zero, when the population means are all equal, and an indefinitely large number as the means are further and further apart. It is analogous to Cohen's $d$ when there are only two groups.

```
cohens_f(m_afex)
```

Until now we've discusses effect sizes in fixed-effect linear model and
repeated-measures ANOVA's - cases where the *SS*s are readily available, and so
the various effect sized presented can easily be estimated. How ever this is not
always the case.

For example, in linear mixed models (LMM/HLM/MLM), the estimation of all required *SS*s is not straightforward. However, we can still *approximate* these effect sizes (only their partial versions) based on the **test-statistic approximation method** (learn more in the *Effect Size from Test Statistics* vignette).

library(lmerTest) fit_lmm <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) anova(fit_lmm) # note the type-3 errors F_to_eta2(45.8, df = 1, df_error = 17)

Or directly with `eta_squared() and co.:

eta_squared(fit_lmm) epsilon_squared(fit_lmm) omega_squared(fit_lmm)

Another case where *SS*s are not available is when use Bayesian models. `effectsize`

has Bayesian solutions for Bayesian models, about which you can read in the *Effect Sizes for Bayesian Models* vignette.

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