Plotting an interaction between a continuous IV and DV and an ordinal covariate, can I use a bar chart? I think my question is similar to this one:
How do you plot an interaction between a factor and a continous covariate?
My IV and DV are both scales, and my interaction term has 3 levels. I am trying to look for associations between my IV and DV so I need to be using correlation coefficients rather than means... I think!
Basically, someone suggested to me doing a bar chart (because it would show the difference more clearly), with the three interaction levels on the x axis and the IV/DV r on the Y axis. I think that's what they were suggesting but it has completely baffled me! I have no idea how to use r in this way...
Any help would be great :)
EDIT:
Just to clarify, I have done a regression to look for significance, and then also an ANCOVA because it was significant.
But what i'm really asking is for a way to illustrate my findings in a figure.
 A: No, you will probably not look at correlations - the appropriate method is ANCOVA with an interaction term.
You will want to plot the DV against the IV separately per level of the factor. I don't really see how barplots can help here. Let's create some toy data in R:
set.seed(1)
ff <- factor(rep(LETTERS[1:3],10))
iv <- rnorm(length(ff))
dv <- rnorm(length(ff),as.numeric(ff)-as.numeric(ff)*iv)
foo <- data.frame(ff,iv,dv)

Now, there are at least two different ways of plotting this. You can create an interaction plot of the raw data like this:
plot(range(iv),range(dv),type="n",xlab="IV",ylab="DV")
for ( ii in seq_along(unique(ff)) ) {
    with(foo[foo$ff==unique(ff)[ii],],
      lines(sort(iv),dv[order(iv)],lty=ii,type="o",pch=19))
}
legend("topright",inset=.01,lty=seq_along(unique(ff)),legend=unique(ff))


Or you can fit a model and plot the fitted values:
model <- lm(dv~iv*ff,foo)
plot(range(iv),range(dv),type="n",xlab="IV",ylab="DV")
for ( ii in seq_along(unique(ff)) ) {
    newdata <- data.frame(ff=unique(ff)[ii],iv=range(iv[ff==unique(ff)[ii]]))
    lines(newdata$iv,predict(model,newdata),lty=ii)
}
legend("topright",inset=.01,lty=seq_along(unique(ff)),legend=unique(ff))


This second alternative of course depends crucially on the model you assume. You can (really: should) refine it with confidence regions.
(You can also do something similar with the R interaction.plot() function, but I honestly don't like its output.)
A: Typically two continuous variables, one dependent, one independent are plotted with some kind of regression line (dependent variable on the y axis). With a nominal (or categorical) interaction term, you would simply plot two (or more) regression lines on the same graph, and label each line according to the values of the interaction term.
