adapted and updated from a feature on ABC News’ Nightline.
Predicting Elections
In 2012 Barack Obama and
Mitt Romney ran for President.
All the TV networks and both
campaigns knew by 3pm Central
Time that Obama had won the
election.
In 2008 Barack Obama and John
McCain ran for President.
All the TV networks and both
campaigns knew by 11:00am
Central Time that Obama had
won the election.
In 2004 George W. Bush ran
against John Kerry for President.
All the TV networks and both
campaigns knew by 4:00pm
Central Time that Bush had won
the election.
In 1996 Bill Clinton and Bob Dole
ran for President.
All the TV networks and both
campaigns knew by noon Central
Time that Clinton had won the
election.
In every election, the networks wait to
broadcast this information until 8pm
Central Time, when polls in the
majority of states have closed.
In every election since 1964—
except for 2000—the networks knew
the outcome before the time the
evening news was broadcast.
Why is it that TV
networks are almost
always right when they
predict elections?
1.
They are careful to take a large,
good sample
 In most election years,
the networks pool their
resources to take a
stratified random
sample of as many as
10,000 voters.
 After the problems in 2000, they have
surveyed 40 – 50,000 people each
election.
2. They repeatedly poll each
state before the election,
looking for consistency in
the results. They use the
results to pre-sort states by
how they expect them to
vote (red vs. blue).
Pre-sorting lets them put most
resources into “toss-up” states (like
Iowa) that could go either way.
3.
They compute an estimate of each
candidate’s percentage of the vote,
including a margin of error, and
only call states where the victory
margin victory exceeds the margin
of error.
EXAMPLE
 Poll says the Republican
will win South Dakota with
58% + 3%
 The Republican will
probably get somewhere
between 55% and 61%
 Call South Dakota for
the Republican.
EXAMPLE
 Poll says Democrat will win
Nevada with 51% + 3%
 The Democrat could get
anywhere between
48% and 54%
 Nevada is too close to call.
4. The mathematics of statistics
gives them confidence that
their results will be correct
between 95 and 99% of the
time.
How it’s Done
Forecasting Election Outcomes
• Let us consider how — and how accurately and how far in
advance — the outcome of Presidential elections can be
predicted.
• We need to distinguish between
• short-term forecasts (made shortly before the Presidential election), and
• long-term forecasts (made at the outset of the general election
campaign or even earlier).
• We will consider three types of forecasts:
• pre-election polls and surveys;
• a more or less qualitative scoring method; and
• predictive models based on long-term patterns in aggregate data.
Pre-Election Polls
• In the relatively distant past, pre-election polls taken even shortly before
election day have sometimes been disastrously and famously wrong —
most notably
• a Literary Digest poll predicted the defeat of President Roosevelt in 1936, and
• the Gallup Poll (and almost all other polls) predicted the defeat of President
Truman in 1948.
The Literary Digest Polls
• The Literary Digest collected from telephone companies and state motor
vehicle departments lists with the names and addresses of telephone
subscribers and registered automobile owners.
• The magazine then mailed out many millions of “sample ballots” to these
names and addresses.
• Many people did not respond, of course, but the Digest still received
millions of ballots, on the basis of which it forecast the outcome of the
election.
• This procedure was quite successful in predicting the Republican
presidential victories in the 1920s and also Franklin Roosevelt’s victory in
1932.
• But in 1936 it predicted that President Roosevelt would be badly defeated
for re-election by Alf Landon, when in fact he won in one of the greatest
Presidential landslides of all time.
• Can you account for why the Literary Digest forecasts that had previously
been successful failed so badly in 1936?
• Gallup (and other) polls still used quota sampling (rather than
random sampling).
• There were relatively few polling organizations, each of
which took relatively few polls.
• Gallup averaged together the results of their several most
recent polls.
• Gallup stopped polling altogether about two weeks before
the election.
• There were two third-party candidacies, support for which
was hard to predict
• In the election itself, Truman ran relatively weakly in the
Northeast but much more strongly in the farm belt and the
West.
• Hence the famous Chicago Tribune photo.
Gallup and Other Polls
in 1948
Polling and Survey Research Today
• Today most commercial and media polls) use well tested
methodologies,
• based on some variant of random sampling, and with
• considerable care going into the wording of the questions and their
assembly into a questionnaire.
• Most such polls are very accurate, giving very similar
estimates.
• However, all such polls are subject to sampling error, stated in
terms of a margin of error of ± X% that depends largely on
sample size.
• They are also subject to “house effects” pertaining to mode
of interview, call-back procedures, various adjustments, and
(especially) their “likely voter” screen.
• Pre-election polls ask something like “If the election
were held today, who would you vote for?”
• Such polls taken many months in advance of an
election bear no reliable relation to the actual outcome.
• Hence the question: why are American Presidential
election campaign polls so variable [over time] when voters
[in the aggregate] are so predictable?
2000
2004
2008
• Lichtman developed this system in 1981, in collaboration
with Volodia Keilis-Borok, a [Soviet] world-renowned
authority on the mathematics of prediction models.
• It presumes that presidential elections are primarily referenda
on how well the party holding the White House has
governed during its term, but taking account of more than
economic performance.
• The Keys further presumes that “by default” the incumbent
party (that controls the White House) wins unless six or
more keys are false [turned against the incumbent].
• The keys gives specificity to this idea of how presidential elections
work, assessing the performance, strength, and unity of the party
holding the White House to determine whether or not it has
crossed the threshold that separates victory from defeat.
Lichtman’s Keys to the White
House
2008
2012
2016?
1. T F
T F
T F
2. T F
T F
T F
3. T F
T F
T F
4. T F
T F
T F
5. T F
T F
T F
6. T F
T F
T F
7. T F
T F
T F
8. T F
T F
T F
9. T F
T F
T F
10. T F
T F
T F
11. T F
T F
T F
12. T F
T F
T F
13. T F
T F
T F
Polling and Survey Research Today
• Today most commercial and media polls) use well tested
methodologies,
• based on some variant of random sampling, and with
• considerable care going into the wording of the questions and their
assembly into a questionnaire.
• Most such polls are very accurate, giving very similar
estimates.
• However, all such polls are subject to sampling error, stated
in terms of a margin of error of ± X% that depends largely
on sample size.
• In general, the margin of error is inversely proportional to the
square root of sample size.
• Margin of error = X%: if we took a great many random sample of
this size from the same population, 95% of them would come
within ±X% points of the “true value” (population parameter).
Polling and Survey Research Today (cont.)
• Different polls are also subject to “house effects” pertaining
to mode of interview, call-back procedures, various
adjustments, and (especially) their “likely voter” screen.
• Poll averages/aggregates are more accurate than individual
polls, e.g., Real Clear Politics Poll Average
• But all polls ask (literally or in effect): “If the election were
held today, how would you vote?”
• And voters may change their voting intentions.
• The claim of predictive models (e.g., Lichtman) is that they
can predict how voters will vote (in the aggregate) better
than voters can.
Predictive Models
• Forecasting models constructed by social scientists seek
• to predict the percent of the popular votes received by
the Presidential candidate of the party the controls the
White House, and
• to do this using information that is available well
before the election (e.g., in mid-summer).
• All such models use information pertaining to
• the recent performance of the economy, and
• the recent popularity of the incumbent President
• The models differ with respect to the exact measures of
economic performance and Presidential popularity that
are used and with respect to what other variables [if any]
are also used.
Predictive Models: 2004
Predictive Models Presented at APSA 2008
Political Scientist
Brad Lockerbie
Thomas Holbrook
Alan Abramowitz
Christopher Wlezien
Alfred Cuzan
Helmut Norpoth
Michael Lewis-Beck
James Campbell
Predicted Obama Vote
58%
55.5%
54.3%
52.2%
51.9%
50.1%
50.07%
Wait till Labor Day
Department of Commerce has revised second-quarter growth from 1.9%
to 3.3%
Predictive Models: 2012
Alan Abramowitz 2012
PV = 47.3 + (.107*NETAPP) + (.541*Q2GDP) +
(4.4*TERM1INC)
•
•
PV stands for the predicted share of the major party vote for the
party of the incumbent president; NETAPP stands for the
incumbent president’s net approval rating (approval – disapproval)
in the final Gallup Poll in June; Q2GDP stands for the annualized
growth rate of real GDP in the second quarter of the election year;
and TERM1INC stands for the presence or absence of a first-term
incumbent in the race.
In order to incorporate this polarization effect in the Time for
Change Model, I added a new predictor (POLARIZATION) for
elections since 1996.** The estimates for the revised model are as
follows:
PV = 46.9 + (.105*NETAPP) + (.635*Q2GDP) +
(5.22*TERM1INC) – (2.76*POLARIZATION)
•
**For elections since 1996, the polarization variable takes on the value 1 when there is
a first-term incumbent running or when the incumbent president has a net approval
rating of greater than zero; it takes on the value -1 when there is not a first-term
incumbent running and the incumbent president has a net approval rating of less
than zero.
Model: Electoral Vote outcome as a factor of popular vote outcomes
EV by PV: Incumbents vs. Open Seat
Democratic Party vote Share: