I am sorry to say that you may need to return to the drawing board. Both your
IF will explain
Changes perfectly because:
$Change = 0 + 1 * ExternalFactors$
$Change = 0 + 4 * InternalFactors$
If you put both of them into the model, most software packages will just pick one for you, either by the order you have fed them in or by the alphabetical order of your variable names.
In order to make this question testable, you'd probably need some validated tools to assess the external and internal factors separately. They are latent variables, and we cannot simply assign a constant to them without measuring them. Using that two values, you can then use regression to find out which one is more predictive, and if the internal factor matters in the presence of the external factor.
What do you have in mind when you say that I'd "need some validated tools to assess the external and internal factors separately"?
Well, I am not a linguistic specialist (actually not a specialist in anything) so I can't tell you what to use. But if I were tasked to examine this phenomenon, I'd first document the linguistic change like you did. And then I'll either look for or design an instrument to capture the "external factors" such as change in social status, change in education, change in weather, etc. Whatever you feel is a significant contributor to the "externality." And then I'll do the same for the internality, but with another specific instrument. (Notice that some instruments can capture both constructs at the same time, they don't have to be two separate tools.) With these three variable, I can then run some tests.
The major challenges I can see is what to classify as internal and external. For that you may need to consult some experts in this matter.
Without any empirical data, your argument would still be valid. If something can 100% explain an outcome, adding anything else is going to be useless. If you'd like get away from this coding dilemma, think from $R^2$ (search "coefficient of determination"). If what you said is right, the $R^2$ of the model using EF as a predictor would be $1$, and the $R^2$ of the model using IF as a predictor would only be $.25$. As EF has already perfected the model, IF is then unnecessary.