The release of new
surveys yesterday by the American
Research Group (ARG) in Iowa, New Hampshire and South Carolina has
generated quite a bit of buzz as well as questions about ARG of the sort I
alluded to yesterday in my post on the screens pollsters use to select likely primary
voters and caucus attendees. That post is the first in a series that will look
at the methodological differences among the various pollsters active in the
early primary states. For now, however, let me take up two issues specific to Iowa and ARG's surveys
there.
The first question comes from reader AL:
I
think your averaging is wrong on the Iowa
race for the democrats. I think it should be Edwards at 25.7 and Hillary at
25.4. You have the averaging mixed up. You should correct it.
I am not sure how AL arrived
at those numbers, but the current Pollster.com estimates in Iowa (Clinton 25.7%,
Edwards 25.4%) are not "mixed up." The confusion may arise from the fact that
our estimates are regression based estimators rather than true averages. Professor
Franklin explains the difference in detail here.
The second issue concerns the Iowa
surveys by ARG.
As commenter jsamuel put
it, "ARG seems to almost always under poll Edwards." While I lack Professor
Franklin's flair for graphics and regression trend lines, some simple averages show
that jsamuel is right. Sen. Clinton does consistently better ARG's surveys than
those from other pollsters, while former Sen. Edwards does consistently worse:
We logged in six new Iowa
surveys during June and July. The two from ARG show Clinton ahead of Edwards by an average six
points (31% to 25%), while the four from other pollsters give Edwards an
average lead of five points (27% to 22%).
Surveys from February, March, April and May show essentially the same
pattern. Clinton
leads by two points (30% to 28%) in four surveys conducted by ARG, while
Edwards leads by an average six points (27% to 21%) in 13 surveys by other
pollsters.
Unfortunately, this is the sort of scenario in which
averaging (or simple regression based estimation) can be potentially
misleading. One pollster (ARG) is getting consistently different results and contributing a large number of polls
to our overall estimate. So if ARG is both different and wrong, their polls are throwing off our estimates.
We know ARG's results in Iowa are different. Why? And what should we
make of that difference? For some clues, stay tuned to my series on primary
screens.