Imagine we have great covariates for correcting for unit nonresponse...

I am continuing on the recent article and commentaries on weighting to correct for unit nonresponse by Michael Brick, as published in the recent issue of the Journal of Official Statistics ( here ). The article by no means is all about whether one should impute or weight. I am just picking out one issue that got me thinking. Michael Brick rightly says that in order to correct succesfully for unit nonresponse using covariates, we want the covariates to do two things:

To weight or to impute for unit nonresponse?

This week, I have been reading the most recent issue of the Journal of Official Statistics , a journal that has been open access since the 1980s. In this issue is a critical review article of weighting procedures authored by Michael Brick with commentaries by Olena Kaminska ( here ), Philipp Kott ( here ), Roderick Little ( here ), Geert Loosveldt ( here ), and a rejoinder ( here ).

why access panels cannot weight elections polls accurately

There are a lot of reasons why would not want to use acces panels for predicting electoral outcomes . These are well discussed in many places on- and offline. I’ll shortly summarize them, before adding some thoughts to why access panels do so badly predicting election outcomes. 1. Access panels don’t draw random samples, but rely on self-selected samples. A slightly better way to get panel respondents is a quota sample, but even these have problems, well discussed here, here and here for example.

Dutch elections 2012 - poll results

The night after the election, one can conclude that all pollsters in the Netherlands did a bad job of predicting the election results. All polls were at least off by 20 seats (out of 150), and I expect the newspapers to make headlines of this in the next days. See the table below for the final predictions (before election day), the exit poll and final election results. The last row shows how much each poll was off (in the number of seats