Palko points us to this post, Did Republican-Leaning Polls “Flood the Zone” in 2022?, by Joe Angert et al., who write:
As polling averages shifted towards Republicans in the closing weeks of the 2022 midterms, one interpretation was that Americans were reverting to the usual pattern of favoring out-party candidates. Other observers argued that voter intentions were not changing and that the shift was driven by the release of a disproportionate number of pro-Republican polls – an argument supported by the unexpectedly favorable results for Democratic candidates on Election Day.
They continue:
We are not alleging a conspiracy among Republican pollsters to influence campaign narratives. . . . Even so, our results raise new concerns about the use of polling averages to assess campaign dynamics. A shift from one week to another may reflect changes in underlying voter preferences but can also reflect differences in the types of polls used to construct polling averages. This concern is particularly true for sites that aggregate polls without controlling for house effects (pollster-specific corrections for systematic partisan lean). . . .
Our results are also salient for aggregators who use pollster house effects to adjust raw polling data. In theory, these corrections remove poll-specific partisan biases, allowing polling averages to be compared week-to-week, even given changes in the types of polls being released. However, in most cases, aggregators use black-box models to estimate and incorporate house effects, making it impossible to assess the viability of this strategy. . . .
There’s a statistical point here, too, which is that additive “house effects” can appropriately shift individual polls so that even biased polls can supply information, but “weighting” can never do this. You need to move the numbers, not just reweight them.
Additive house-specific effects would be constant though, right? Or would they be time-varying? If constant, then how would they be able to account for a coordinated, biased move to their preferred candidate close to the election to try to shift the narrative?
The plotted lines here are the “best-fit logarithmic trend lines” — Is this standard practice?
Is there a Bayesian standard approach for fitting a curve through polling data for estimating the current percentage (i.e., for “nowcasting”)?
Harlan
Harlan:
It depends on your model. In our Economist forecast we used a random walk model, actually separate random walks for national and state trends.