The JSmediation package was designed to help intuitively typing the code to test mediations. In this vignette, we will use it to assess a simple mediation.
Simple mediation analysis refers to the analysis testing whether the
effect of an independent variable on a dependent variable goes through a
third variable (the mediator). The
ho_et_al data set,
shipped with the JSmediation package, contains data
illustrating a case of simple mediation. This data set contains the data
collected by Ho et al. in a paper focusing on hypodescent (2017), a rule sometimes use when people have to
perform multiracial categorization and where a perceivers associate a
biracial person more easily to their lowest status group.
In this experiment, Ho et al. (2017) made the hypothesis that a Black American participants exposed to the discrimination of Black-White biracials would be more likely to associate Black-White biracials with Black Americans rather than with White Americans. In other words, participants of their experiment would be more likely to use the hypodescent rule when exposed to high discrimination content compared to low discrimination content. In the experiment that we will investigate, the authors went further and tested whether the effect of the discrimination condition on the use of hypodescent was mediated by a feeling of linked fate between the participants (Black Americans) and Black-White biracials (Ho et al., 2017).
In this vignette, we will use the
ho_et_al data set to
test whether feeling of linked fate mediates the relationship
between the exposition to a high discrimination content and the use of
hypodescent among Black Africans.
Simple mediation is often times summarized with one equation (Baron & Kenny, 1986; Cohen & Cohen, 1983):
\[ c = c' + a \times b \]
with \(c\) the total effect of the
independent variable (\(X\)) on the
dependent variable (\(Y\)), \(c'\) the direct of \(X\) on \(Y\), and \(a
\times b\) the indirect effect of \(X\) on \(Y\) through the mediator variable (\(M\); see Models section of the
mdt_simple help page).
To assess whether the indirect effect is different from the null, one has to assess the significance against of both \(a\) (the effect of \(X\) on \(M\)) and \(b\) (effect of \(M\) on \(Y\) controlling for the effect of \(X\)). Both \(a\) and \(b\) need to be simultaneously significant for an indirect effect to be claimed (Yzerbyt et al., 2018).
Because we want to test whether the feeling of linked fate is mediating the effect of the discrimination condition on the use of hypodescent, we must test whether the discrimination condition predicts the feeling of linked fate and whether feeling of link fate predicts the use of hypodescent (when controlling for the effect of the discrimination condition). The JSmediation package will help us in that regard.
Our first step will be to attach the JSmediation package to our environment. This will allow us to use the functions and data sets shipped with the package.
To begin with the analysis, we will take a look at the
ho_et_al data set.
data(ho_et_al) head(ho_et_al) #> id condition sdo linkedfate hypodescent #> 1 2 Low discrimination 1.8125 6.000 2.333333 #> 2 3 High discrimination 1.5625 5.875 6.000000 #> 3 4 High discrimination 1.7500 6.625 6.000000 #> 4 5 Low discrimination 4.2500 5.125 5.666667 #> 5 6 Low discrimination 1.9375 4.375 4.000000 #> 6 9 High discrimination 2.8750 3.750 4.000000
This data set contains 5 columns: *
ìd: a unique
identifier for each participant, *
discrimination condition of the participants (either “Low
discrimination” or “High discrimination”), *
sdo: a measure
of Social Dominance Orientation (SDO) of the participant which is
extensively used in our example of [moderated
linkedfate: the feeling of linked fate between the
participants and Black-White biracials, *
tendency to use the hypodescent rules in multiracial categorization
(see, Ho et al. 2017).
This data set is almost ready for our analysis. The only thing that
we need is a data frame (or a
tibble) with the value of our
different variables for each participant (i.e., the independent
variable, the dependent variable, and the mediator). Our data, however,
must be properly formatted for the analysis. In particular, every
variable must be coded as a numeric variable.
condition variable is coded as a character
(and not as a numeric)—a format which is not supported by
JSmediation, we will need to pre-process our data set. Thanks
build_contrast function, we will create a new
representing the discrimination condition as a numeric variable.
ho_et_al$condition_c <- build_contrast(ho_et_al$condition, "Low discrimination", "High discrimination") head(ho_et_al) #> id condition sdo linkedfate hypodescent condition_c #> 1 2 Low discrimination 1.8125 6.000 2.333333 -0.5 #> 2 3 High discrimination 1.5625 5.875 6.000000 0.5 #> 3 4 High discrimination 1.7500 6.625 6.000000 0.5 #> 4 5 Low discrimination 4.2500 5.125 5.666667 -0.5 #> 5 6 Low discrimination 1.9375 4.375 4.000000 -0.5 #> 6 9 High discrimination 2.8750 3.750 4.000000 0.5
Now that we have a data frame ready for analysis, we will use the
mdt_simple function to fit a simple mediation model. Any
mediation model supported by JSmediation comes with a
mdt_* function. These functions need the users to indicate
the data set used for the analysis as well as the variable relevant for
the analysis thanks to the function argument. Once done, it will run the
relevant linear regression in order to test the conditions necessary for
mediation_fit <- mdt_simple(ho_et_al, IV = condition_c, DV = hypodescent, M = linkedfate)
mediation_fit model that we just created contains
every bit of information necessary to use a joint-significance approach
to assess simple mediation (Yzerbyt et al.,
Before diving into the results, because the joint-significance
approach runs linear regression under the hood, we will test the
assumptions of ordinary least square for each of the regression used by
mdt_simple (Judd et al.,
2017). To do so, we will use the
the performance package function which prints several
diagnostic plots (Lüdecke et al., 2021)1.
We will first extract the models used by
then run the
check_model function. The
extract_model function will be helpful to that regard. This
function uses a mediation model as a first argument, and the model name
(or model index) as a second argument. It then returns a linear model
object (i.e., an
We will do the same thing for the two other models mdt_simple has fitted.
third_model <- extract_model(mediation_fit, step = 3) performance::check_model(third_model) #> Variable `Component` is not in your data frame :/
Thanks to these plots, we can now interpret the results of the mediation knowing whether their data suffer from any violation (Judd et al., 2017).
Now that we check for our assumptions, we can interpret our model. To
do so, we simply have to call
mediation_fit #> Test of mediation (simple mediation) #> ============================================== #> #> Variables: #> #> - IV: condition_c #> - DV: hypodescent #> - M: linkedfate #> #> Paths: #> #> ==== ============== ===== ======================= #> Path Point estimate SE APA #> ==== ============== ===== ======================= #> a 0.772 0.085 t(822) = 9.10, p < .001 #> b 0.187 0.033 t(821) = 5.75, p < .001 #> c 0.171 0.081 t(822) = 2.13, p = .034 #> c' 0.027 0.083 t(821) = 0.33, p = .742 #> ==== ============== ===== ======================= #> #> Indirect effect index: #> #> Indirect effect index is not computed by default. #> Please use add_index() to compute it. #> #> Fitted models: #> #> - X -> Y #> - X -> M #> - X + M -> Y
In this summary, we can see that both \(a\) and \(b\) paths are significant, and we can therefore conclude that the indirect effect of the discrimination condition on hypodescent used passing through the feeling of linked fate is significant (Yzerbyt et al., 2018).
Thanks to the
mdt_simple function, we almost have every
information to report our joint-significance test (Yzerbyt et al., 2018). Besides reporting the
significance of \(a\) and \(b\), it is sometimes recommended to report
the index of indirect effect, a single value accounting for \(a \times b\). Wa can compute this index
thanks to Monte Carlo methods thanks to the
function. This functions adds the indirect effect to the model summary
model_fit_with_index <- add_index(mediation_fit) model_fit_with_index #> Test of mediation (simple mediation) #> ============================================== #> #> Variables: #> #> - IV: condition_c #> - DV: hypodescent #> - M: linkedfate #> #> Paths: #> #> ==== ============== ===== ======================= #> Path Point estimate SE APA #> ==== ============== ===== ======================= #> a 0.772 0.085 t(822) = 9.10, p < .001 #> b 0.187 0.033 t(821) = 5.75, p < .001 #> c 0.171 0.081 t(822) = 2.13, p = .034 #> c' 0.027 0.083 t(821) = 0.33, p = .742 #> ==== ============== ===== ======================= #> #> Indirect effect index: #> #> - type: Indirect effect #> - point estimate: 0.144 #> - confidence interval: #> - method: Monte Carlo (5000 iterations) #> - level: 0.05 #> - CI: [0.0888; 0.209] #> #> Fitted models: #> #> - X -> Y #> - X -> M #> - X + M -> Y
The only thing left to do is to report the mediation analysis:
First, we examined the effect of the discrimination condition (low vs. high) on hypodescent use. This analysis revealed a significant effect, t(822) = 2.13, p = .034. > > We then tested our hypothesis of interest, namely, we tested whether the sentiment of linked fate between Black Americans and Black-White biracials mediated the effect of the discrimination condition on hypodescent. To do so, we conducted a joint significant test (Yzerbyt et al., 2018). This analysis revealed a significant effect of discrimination condition on linked fate, t(822) = 9.10, p < .001, and a significant effect of linked fate on hypodescent, controlling for the discrimination condition, t(821) = 5.75, p < .001. The effect of discrimination condition on hypodescent after controlling for the feeling of linked fate was no longer significant, t(821) = 0.33, p = .742. Consistently with this analysis, the Monte Carlo confidence interval for the indirect effect did not contain 0, CI95% [0.0889; 0.208]. This analysis reveals that the feeling of linked fate mediates the effect of the discrimination condition on hypodescent.
JSmediation makes conducting a mediation analysis easy
mdt_* functions, but they are not the only
function in the package. Some functions will help with the linear
regression models fitted during the analysis.
check_assumptions tests every model’s OLS
assumptions using the performance package.
plot_assumptions plots plots diagnostic of the
models’ OLS assumptions using the performance package.
extract_model returns one of the model used (as an
extract_models returns a named list of the models
extract_tidy_models returns a data frame containing
models summary information à la broom (Robinson et al., 2021).
display_models print a summary of each
Recent versions of JSmediation offers the
plot_assumptions to help
you check the OLS assumptions of the fitted model.↩︎