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What is the best way to visualize the relationship between an ordinal predictor and a continuous outcome? So far I have this, but I feel like this is lacking...

The way I modeled it is I treated the ordinal predictor as interval instead of categorical. If this is not the best way to treat this type of data, I'd appreciate the feedback.

The problem with this is that there's no way of knowing how many dots are bunched up together. Two solutions I've seen:

Box plot

This would give you tighter box if data points are bunched up together.

Bubble chart

Not sure if this is the official name, but basically you put the vertical axis into bins. The size of the bubble is determined by how many observations fall into that bin.

To your scatterplot, I would add a large point indicating the mean Y-value at every unique X-value, and also do one or more of the following:

1. Square-root (or cube-root) transform your Y-axis. Both these
   transformations can deal with zeroes, unlike log transformations.
   Cube roots can also deal with negative numbers.


2. Make the points a bit transparent.


3. Add a little jitter to the X-axis values if the previous steps are insufficient.

As Glen_b notes, there is insufficient information right now to note whether adding a linear regression line is meaningful.

The plot you shown is pretty good. But I think you can improve the data-ink ratio (invented by Edward Tufte) even more by showing all the datapoints. You can do this by adding jitter to the x-axis.

Another improvement is to emphasise that the ordinal variable is categorical and not continuous. You can do this by using a different colour for the different levels.

As an example I have plotted the titanic dataset in R, using the passenger class as an ordinal variable and the passenger age as the continuous variable.

library(tidyverse)
library(ggplot2)
library(titanic)

df <- titanic_train %>% mutate(Class=factor(Pclass))

ggplot(df, aes(Class, Age, color=Class)) +
 geom_jitter(height = 0) + 
 ggtitle("Titanic passenger age vs. class")

You state that one variable is ordinal, then you decide to treat it as interval. Is that reasonable? There is no way for us to know, as you have not said what the ordinal variable actually is. If you do decide to keep it as ordinal, then what to do depends on your sample size. If N is very large then I like the box plot solution. If N is not so large, then I like jitter. There are other additions you can make to the scatterplot as well - I wrote a presentation about this using SAS, but I am sure it could be duplicated in R. (If that link does not work, Googling flom, scatterplots, enhancements should find it).

But what if treating the variable as interval is not reasonable? You could come to this conclusion either substantively or by trying different codings and seeing how results change. In that case, I suggest trying optimal scaling. There is an R package optiscale that may help (I have not used this package).