All surveys deviate from the true distributions of the variables, but some more so than others. This is particularly relevant in the context of election studies, where the true distribution of the vote is revealed on election night. Wouldn’t it be nice if one could quantify the bias exhibited by pollster X in their pre-election survey(s), with one single number? Heck, you could even model bias in polls, using RHS variables such as time to election, sample size or sponsor of the survey, coming up with an estimate of the infamous “house effect”,.

Jocelyn Evans and I have developed a method for calculating such a figure by extending Martin, Kennedy and Traugott’s measure $A$ to the multi-party case. Being the very creative chaps we are, we call this new statistic [drumroll] $B$. We also derive a weighted version of this measure $B_w$, and statistics to measure bias in favour/against any single party ($A'$). Of course, our measures can be applied to the sampling of any categorical variable whose distribution is known.

We fully develop all these goodies (and illustrate their usefulness by analysing bias in French pre-election polls) in a paper that
(to our immense satisfaction) has just been accepted for publication in Political Analysis (replication files to follow).

Our module survebias is a Stata ado file that implements these methods. It should become available from SSC over the summer, giving you convenient access to the new methods. I’ll keep you posted.

### One Response to “A Scalar Measure for Bias in (Multi-Party Pre-Election) Surveys”

1. […] a recent paper, we derive various multinomial measures of bias in public opinion surveys (e.g. pre-election polls). Put differently, with our methodology, you may calculate a scalar […]

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