Without examining interaction effects in your model, sometimes we are incorrect about the real relationship between variables.

This is particularly evident in political science when we consider the impact of regime type on the relationship between our dependent and independent variables.

For example, if I were to look at the relationship between pro-democracy protests and executive bribery, I would expect to see that the higher the bribery score for a given authority, the more likely it is that people will protest against this corrupt authority.

So, I run a quick regression,

`summary(protest_model <- lm(democracy_protest ~ executive_bribery, data = data_2010))`

With the dependent variable` democracy_protest`

being an interval score, based upon the question:

In this year, how frequent and large have events of mass mobilization for pro-democratic

aims been?

When deciding this score, events are pro-democratic if they are organized with the explicit aim to advance and/or protect democratic institutions such as free and fair elections with multiple parties,

and courts and parliaments; or if they are in support of civil liberties such as freedom of association and speech. This question concerns the mobilization of citizens for mass events

such as demonstrations, strikes and sit-ins.

The main independent variable is another interval score on executive_bribery scale and is based upon the question:

How clean is the executive (the head of state, the head of government, and cabinet ministers), and their agents from bribery (granting favors in exchange for bribes, kickbacks, or other material inducements?). Higher scores indicate cleaner executive.

Examining the results of the regression model:

We see that there is indeed a negative relationship. The cleaner the government, the less likely people in the country will protest in the year under examination.

However, examining the R2, we see that less than 1% of the variance in protest prevalence is explained by executive bribery scores.

Is there an interaction effect with regime type? The color clusters indicate that it is indeed a factor. Liberal democracies (purple) cluster at the top left hand corner. Higher scores in clean executive index and lower prevalence in pro-democracy protesting. Electoral autocracies (teal) cluster in the middle and electoral democracies (khaki green) cluster at the bottom of the graph. The closed autocracy countries (red) seem to have a upward trend, opposite to the overall best fitted line.

So let’s examine the interaction effect between regime types and executive corruption with mass pro-democracy protests.

Plot the model and add the * interaction effect:

`summary(protest_model_2 <-lm(democracy_protest ~ executive_bribery*regime_type, data = data_2010))`

Adding the regime type variable, the R2 shoots up to 27%.

The interaction effect appears to only be significant between clean executive scores and liberal democracies. The cleaner the country’s executive, the prevalence of mass mobilization and protests decreases by -0.779 and this is a statistically significant relationship.

The initial relationship we saw in the first model, the simple relationship between clean executive scores and protests, has disappeared. There appears to be no relationship between bribery and protests in the semi-autocratic countries; (those countries that are not quite democratic but not quite fully despotic).

In the `plot_model()`

function, first type the name of the model we fitted above, `protest_model`

.

Next, choose the `type`

. For different type arguments, scroll to the bottom of this blog post. We use the `type = "pred"`

argument, which plots the marginal effects.

Marginal effects tells us how a dependent variable changes when a specific independent variable changes, if other covariates are held constant. The two terms typed here are the two variables we added to the model with the * interaction term.

```
install.packages("sjPlot")
library(sjPlot)
plot_model(protest_model, type = "pred", terms = c("executive_bribery", "regime_type"), title = 'Predicted values of Mass Mobilization Index',
legend.title = "Regime type")
```

Looking at the graph, we can see that the relationship changes across regime type. For liberal democracies (purple), there is a negative relationship. Low scores on the clean executive index are related to high prevalence of protests. So, we could say that when people in democracies see corrupt actions, they are more likely to protest against them.

However with closed autocracies (red) there is the opposite trend. Very corrupt countries in closed autocracies appear to not have high levels of protests.

It seems that when there are no democratic features available to the people (free media, freedom of assembly, active civil societies, or strong civil rights protections, freedom of expression et cetera) the barriers to protesting are too high. However, as the corruption index improves and executives are seen as “cleaner”, these democratic features may be more accessible to them.

If we only looked at the relationship between the two variables and ignore this important interaction effects, we would incorrectly say that as

Of course, panel data would be better to help separate any potential causation from the correlations we can see in the above graphs.

The blue line is almost vertical. This matches with the regression model which found the coefficient in electoral autocracy is 0.001. Virtually non-existent.

### Different Plot Types

`type = "std"`

– Plots standardized estimates.

`type = "std2"`

– Plots standardized estimates, however, standardization follows Gelman’s (2008) suggestion, rescaling the estimates by dividing them by two standard deviations instead of just one. Resulting coefficients are then directly comparable for untransformed binary predictors.

`type = "pred"`

– Plots estimated marginal means (or marginal effects). Simply wraps `ggpredict`

.

`type = "eff"`

– Plots estimated marginal means (or marginal effects). Simply wraps `ggeffect`

.

`type = "slope"`

and `type = "resid"`

– Simple diagnostic-plots, where a linear model for each single predictor is plotted against the response variable, or the model’s residuals. Additionally, a loess-smoothed line is added to the plot. The main purpose of these plots is to check whether the relationship between outcome (or residuals) and a predictor is roughly linear or not. Since the plots are based on a simple linear regression with only one model predictor at the moment, the slopes (i.e. coefficients) may differ from the coefficients of the complete model.

`type = "diag"`

– For **Stan-models**, plots the prior versus posterior samples. For **linear (mixed) models**, plots for multicollinearity-check (Variance Inflation Factors), QQ-plots, checks for normal distribution of residuals and homoscedasticity (constant variance of residuals) are shown. For **generalized linear mixed models**, returns the QQ-plot for random effects.