Sean also has a network model of the apparent Diebold effect.
At bottom is a disagreement over how to infer causality in observational data, and how to diagnose the functional form of a data set.
The good news is two-fold: there may not be a large “Diebold effect” when nonlinear methods are used, and reason suggests that the apparent Diebold effect will be explained through demographics.
The “bad news” is also two-fold: not everyone agrees those nonlinear methods are appropriate, and there’s an alarmingly persistent, consistent, and large Diebold effect when simple – but traditional – inferential statistics are used.
It’s still not clear exactly which demographic feature results in such discrepant results between nonlinear and linear models. (Edit 1/21: An important but previously unconsidered variable is how each precinct voted in the 2004 democratic primaries).