Polls and Elections Two Paradigms of Presidential Nominations
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Polls and Elections
Two Paradigms of Presidential Nominations
WAYNE STEGER
DePaul University
This article uses forecasts of the contested vote in presidential primaries to assess competing
hypotheses about political power in presidential nominations. Forecasts are estimated using
information from the invisible primary and from the earliest nominating elections as a means of
assessing which nominations were largely settled during the invisible primary consistent with
Cohen et al. (2008) and which were influenced more by momentum during the primaries as
Aldrich (1980) and Bartels (1988) argue. Both patterns exist, which suggests that future
research on presidential nominations should focus on why party elites and mass partisans unify
more in some years than in other years.
The time frame at which presidential nominations can be forecast accurately
provides us with a clue about when a winning nominating coalition coalesces in a
political party, which in turn provides insights about political power in the nomination
process. This article uses updated forecasts of the contested vote in presidential primaries
to assess competing hypotheses from different theoretical approaches to the study of
presidential nominations. One theoretical perspective holds that presidential nomina-
tions are largely determined during the invisible primary (e.g., Cohen et al., 2003, 2008;
Hadley 1976; Haynes et al. 2004; Mayer 1996; Steger 2000). In this scenario the
caucuses and primaries play a plebiscitary role, confirming the results of the campaign
occurring before the primaries when party elites, activists, donors, and groups evaluate
candidates and coordinate among themselves in order to unify behind a presidential
candidate—before the mass membership of the party weigh in on the selection of the
nominee. The other theoretical approach focuses on candidates’ campaign momentum
during the caucuses and primaries (e.g., Aldrich 1980; Bartels 1988; Norrander 1993,
2006; Popkin 1991). We can get a sense of which argument is correct by assessing the
accuracy of presidential primary vote forecasts that use information from different points
in time.
Wayne Steger is professor of political science at DePaul University. His current research focuses on presidential
nominations and on the implications of partisan ideology and budgetary process for patterns of taxes, spending, and deficits.
Presidential Studies Quarterly 43, no. 2 (June) 377
© 2013 Center for the Study of the Presidency378 | PRESIDENTIAL STUDIES QUARTERLY / June 2013
Forecasts of the presidential primary vote differ from general election forecasts and
other kinds of static explanatory models in a critical respect. Since presidential nominees
are chosen through a sequential process that begins years before and continues through a
series of caucuses and primaries, the process can be thought of as a Bayesian updating
model (e.g., Morton and Williams 1999). In Bayesian terms, the nomination campaign
leading up to the caucuses and primaries establishes an expectation for the outcome of the
campaign, and this baseline expectation is updated through successive caucuses and
primaries. As such, it is possible to use forecast models to assess the relative impact of
conditions and events at different points in time to determine which phases of the
campaign are critical to the determination of the outcome. This article estimates forecast
models using information known at the end of the invisible primary period and compares
these with forecast models using information from the earliest caucuses and primaries—
the events considered to have the greatest impact on subsequent primary vote. If the
nominee can be predicted accurately using information from the invisible primary, then
the caucuses and primaries would appear to confirm the results of the earlier processes.
However, if pre-primary forecasts have substantial error and that error is reduced by
information about the results of the earliest nominating elections, then the caucuses
and primaries can be said to have an independent influence on the outcome and thus
campaign momentum affects the outcome.
Rather than affirming one perspective or the other, this study shows that both
patterns exist in different presidential nomination campaigns. One pattern is evidenced
by substantial coalescence of party elites and mass partisans during the invisible primary
behind a “front-runner” who goes on to win the nomination. Party elites, activists,
and groups, however, do not always unify behind a candidate before the caucuses and
primaries. These campaigns are distinguishable in forecasting models by larger errors in
the prediction of candidate vote shares. In these nomination cycles, models incorporating
information from the results of early caucuses and primaries substantially improve
predictive accuracy. The occurrence of both patterns raises an important question about
why political party elites and mass identifiers unify during the invisible primary period
in some election cycles more than during others.
The next section briefly reviews models of presidential primary vote forecasts.
Then I estimate the models for a common set of candidates and nomination campaigns
from 1980 to 2012 to compare these models on an equal footing. The following section
compares the pre-Iowa forecasts with “momentum” models that incorporate the results
of the Iowa caucus and New Hampshire primary to predict the remaining primary
vote. The discussion focuses on the implications for future research on presidential
nominations.
Presidential Primary Vote Forecasts
Forecasting models implicitly assume that the critical period in the presidential
nomination process occurs before the primaries (e.g., Adkins and Dowdle 2000; Mayer
1996; Steger 2000). Only those candidates who emerge from the pre-primary campaignSteger / TWO PARADIGMS OF PRESIDENTIAL NOMINATIONS | 379
as viable options for primary voters have a realistic chance of winning the nomination.
Viable candidates are those raising money, gaining the backing of party insiders and the
support of party identifiers in national polls prior to the caucuses and primaries (e.g.,
Butler 2004; Cohen et al. 2008; Haynes, et al. 2004; Hull 2008; Mayer 1996; Steger,
Dowdle, and Adkins (2004). If the pre-primary competition for support and resources
affects candidate support in the primaries, then models accounting for these effects
should predict the presidential primary vote.
Mayer’s (1996) presidential primary vote forecast model found that candidates’
support in the last national Gallup poll taken before the Iowa caucus was a powerful
predictor of the aggregate primary vote in contested presidential nominations. Mayer
argues that that candidates’ personal and programmatic appeal are integrated into aggre-
gate level, national polls of party identifiers and leaners—the people most likely to vote
in the presidential primaries. Forecast models consistently show that national opinion
polls are a powerful predictor of the presidential primary vote. Adkins and Dowdle
(2000, 2005) forecast the presidential primary vote in open nominations from 1980 to
2004.1 In addition to mass partisan support before the primaries, their model showed
that candidates’ cash reserves—how much money they have on hand when the primaries
begin also is a significant predictor of the primary vote.
Steger (2007, 2009) found that endorsements by party elected officials were a
significant predictor of the presidential primary vote. Cohen et al. (2003, 2008) find
the same result with a somewhat different data set including endorsements by political
activists and groups active in presidential nomination campaigns.2 Endorsements by
party elites and activists reflect an insider game, as opposed to a mass partisan effect, with
candidate endorsements serving as cues to attentive publics about which candidates are
viable and desirable. Such cues may be important when voters cannot use party labels to
differentiate candidates and when prospective primary voters have little information
about candidates’ policy positions (Jamieson et al. 2000). Steger (2008) also argued that
the predictive effects of Gallup polls and cash reserves vary with the competitiveness
of the pre-primary nomination campaign. Gallup polls predict well in noncompetitive
campaigns in which there is a front-runner before the Iowa caucus, while cash reserves
help predict winners in competitive races without a clear front-runner.
Forecast models based on information from the period before the primaries,
however, have substantial error in some nomination campaigns. Pre-primary forecast
models predict well when party elites and rank-and-file partisans are fairly unified in their
support of a candidate for their party’s presidential nomination. Party elites, activists, and
identifiers do not always unify behind a presidential candidate before the primaries, and
in such cases, pre-primary forecasts miss the mark substantially. Candidates who gain
momentum by beating expectations for their vote share in early caucuses and primaries
receive a surge in media coverage, fund-raising, and support in subsequent primaries
(Bartels 1988), while others drop out as they fall behind in the delegate count or run out
1. Open nominations are those without an incumbent president seeking renomination (see below).
2. The differences between the Steger (2008, 2009) models and those of Cohen et al. (2008) are
relatively small since the number of endorsements by party activist endorsements is small relative to
endorsements by elite elected officials, particularly once endorsements are weighted.380 | PRESIDENTIAL STUDIES QUARTERLY / June 2013
of money (Norrander 2006). Forecasts based on factors from before the primaries do not
account for the effects of positive or negative momentum during the primaries. Thus,
forecasts incorporating the results of the earliest caucuses and primaries—the events
considered to have the greatest impact on the subsequent primary vote—correct for
under- and overpredictions of the precaucus forecasts for candidates who gain or lose
momentum during these events. Adkins and Dowdle (2001), Steger et al. (2004), and
Steger (2007) developed forecast models that incorporate various measures of the Iowa
caucus and New Hampshire primary results to predict the remaining primary vote.
Data and Methods
This article estimates models for a common set of candidates and nomination
campaigns to compare these models on an equal footing.3 Assessing the consistency or
divergence of the results generated by different models provides evidence on the robust-
ness of estimates for individual predictors in the models. The unit of analysis is the
candidates’ vote shares in contested primaries (CPV), which is the vote in primaries prior
to the date on which a candidate gains a majority of convention delegates, in open
nominations without an incumbent president. In the “momentum” models, the depen-
dent variable is calculated as candidates’ shares of the CPV minus their shares of the
New Hampshire primary vote.4 The pre-Iowa models use data from the last the Federal
Election Commission reporting period proximate to the Iowa caucus.5 This ensures that
variables are measured at roughly the same point in political time—at a point closest to the
Iowa caucuses when media and public attention is relatively high. Explanation of the
measurement and coding of variables can be found in Steger (2008, 2009).
Analysis and Discussion
The results presented here differ somewhat from the original published results for
the respective models as a result of using nominations and a primary vote measure that
may differ from those of the original studies. Estimating the models using the same
dependent variable over the same set of candidates and elections, however, is valuable
because it allows us to make reliable comparisons of the relative impacts of variables.
Mayer’s model is the most parsimonious but is somewhat less accurate than the
other forecast models (see Table 1). The model correctly predicts the nominee in eight of
12 open nomination races—nine of 12 if we recognize that Hillary Clinton won the most
3. See Steger (2008, 2009) for extended discussion of the methodological issues involved in forecast-
ing the presidential primary vote.
4. Data on candidate vote shares were obtained from Congressional Quarterly (2005) and from
http://www.thegreenpapers.com (accessed January 22, 2013) for 2008 and 2012.
5. When the Iowa caucus occurs in January, all of the pre-Iowa variables are measured at the end of
December of the preceding calendar year. For nominations in which the Iowa caucus occurs in February,
all of the pre-Iowa variables are measured at the end of January of the election year.Steger / TWO PARADIGMS OF PRESIDENTIAL NOMINATIONS | 381
TABLE 1
Pre-Iowa Forecasts of the Vote in Contested Presidential Primaries, 1980-2012
Mayer Gallup + Adkins & Dowdle Steger Endorse +
Funds1 Gallup + Reserves Competitive Interactions
Constant -.95 (2.46) -1.17 (2.57) 1.74 (1.75)
Gallup relative to leader(last pre-IA) .35 (.06)** .31 (.07)**
Gallup(last pre-IA) .51 (.25)**
Gallup(last pre-IA) x non-competitive race .51 (.31)*
% S Funds Raised relative to leader(closest pre-IA) .10 (.05)*
% S Funds Spent relative to leader(closest pre-IA) .08 (.06)
% Cash Reserves relative to leader(closest pre-IA) .06 (.06)
% Cash Reserves(closest pre-IA) -.26 (.16)
% Cash Reserves(closest pre-IA) x competitive .42 (.22)*
Southern Democrat dummy -.05 (5.32)
Elite Party Endorsements(prior to Iowa) .28 (.12)**
2
Adj. r .53 .52 .64
F 45.48 22.69 28.47
DW 2.19 2.19 2.38
80 80 80
Avg. absolute error, all candidates 8.97 8.99 7.78
** denotes coefficients significant at the p < .05 level, * at the p < .10 level.
primary votes in 2008. The model generates a slightly higher absolute percentage error
in the prediction of candidates’ contested primary vote shares (m = 8.97%). While
getting the winner right in most of the nominations, the model has substantial error
in estimating the nominee’s share of the vote in contested primaries (see Table 2). The
predicted vote share of the eventual nominee differs by an average of 17.5% from the
nominees’ actual vote share. Updated through the 2012 nomination cycle, the model
yields a significant coefficient for fund-raising for the first time. The variable was shown
in all prior years to have been insignificant, including in Mayer’s original model.
The Adkins and Dowdle model performs about the same as the Mayer model.
The model has a similar absolute error of predicted vote shares across all candidates
(m = 8.99%) and predicts correctly the winner in nine of 12 counting Hillary Clinton
as the winner of the primary vote in 2008 (see Table 1). The model has a marginal
improvement in the prediction of the contested primary vote shares of the eventual
nominees (see Table 2). Candidates’ cash reserves are no longer a significant predictor as
was the case for the nomination campaigns of the 1980s and 1990s. The Internet and the
emergence of super political action committees provide sufficient capacity for fund-
raising in very short time periods so that candidates with large cash reserves no longer
have the advantage that they had in prior years.
The Steger endorsement and competition model yields a lower absolute error of
vote share prediction (m = 7.78%) and correctly predicts the winner of the CPV in 10 of
12 open nominations if we count Hillary Clinton as the winner of the primary vote in
2008. The improvement in the model comes mainly from the inclusion of candidate
endorsements by elite elected officials in the political parties. Both elite and mass party382 | PRESIDENTIAL STUDIES QUARTERLY / June 2013
TABLE 2
Predicted Primary Vote Share for the Eventual Nominee in Open Nominations, 1980-20121
Adkins & Dowdle Steger Endorsements &
Mayer Gallup + Funds Gallup + Reserves Competitive Interaction
Pred. rank Vote Error2 Pred. rank Vote Error2 Pred. rank Vote Error2
Ronald Reagan 1980 R 1 -19.24 1 -16.85 1 -16.99
Walter Mondale 1984 D 1 5.72 1 6.09 1 0.18
Michael Dukakis 1988 D 2 -11.92 2 -10.05 1 -21.28
George H. W. Bush 1988 R 1 -15.69 1 -18.81 1 -4.14
Bill Clinton 1992 D 1 -8.25 1 -7.93 1 0.17
Bob Dole 1996 R 1 -10.10 1 -9.73 1 -2.89
Al Gore 2000 D 1 -27.08 1 -28.78 1 -0.46
George W. Bush 2000 R 1 -15.32 1 -14.95 1 4.93
John Kerry 2004 D 4 -42.32 4 -41.80 3 -44.71
Barack Obama 2008 D3 2 -19.08 2 -19.02 2 -21.64
John McCain 2008 R 4 -21.00 4 -21.98 2 -22.78
Mitt Romney 2012 R 1 -14.33 1 -4.51 1 -3.45
Average absolute error 17.50 16.71 11.97
1
Models based on data from the end of January for primaries in 1980, 1984, 1988, and 1992; and data from
the end of December for primaries in 1996, 2000, 2004, 2008, and 2012.
2
Predicted minus actual vote share. Negative signs indicate under prediction.
3
Clinton received the most votes across all primaries, including the Michigan and Florida primaries in 2008.
preferences for candidates are strong predictors of the primary vote, although elite party
endorsements have less predictive power than do candidates’ standings in pre-primary
national polls. As in prior years, the model also indicates that effect of cash reserves is
limited to competitive races lacking a clear front-runner.6 Compared to the other models,
the Steger model substantially reduces the degree of error in predicting the vote shares of
the eventual nominee. The model yields a lower absolute error of prediction of for the
eventual nominees (m = 11.97%), compared to a mean absolute error of prediction of
about 17% in the other models (see Table 2). The nominee usually is the candidate with
the most support from political party insiders.
Though these prediction errors are large compared to those of general election
forecasts, error rates of this magnitude are expected given multiple candidates, low levels
of public knowledge of candidates, and sequential primaries. The models accurately
predict the vote share of the nominee in nomination campaigns when party insiders and
mass partisans coalesce around a front-runner during the invisible primary. Races in
which no clear front-runner emerges are competitive at the end of the pre-primary season,
and these are the nominations that are influenced by the results of the early caucuses
and primaries consistent with arguments about campaign momentum. The errors in the
pre-Iowa forecast models are greatest for the candidates who gained the most momentum
(e.g., Gary Hart in 1984, John McCain in 2000, John Kerry in 2004, and Barack Obama
6. Competitive races are defined as nominations without a pre-Iowa front-runner with more than
50% in Gallup polls in 1988, 1992, 2004, and 2008 for Democrats and 2008 and 2012 for Republicans
(see Mayer 2008; Steger 2008).Steger / TWO PARADIGMS OF PRESIDENTIAL NOMINATIONS | 383
TABLE 3
Momentum Forecasts of the Contested Post–New Hampshire Primary Vote in Open
Nominations, 1980-2012
Adkins & Dowdle + Momentum Steger + Momentum
Constant -1.62 (1.56) -.94 (1.29)
Gallup relative to leader(last pre-IA) .19 (.04)**
Gallup(last pre-IA) .74 (.17)**
Gallup(last pre-IA) x non-competitive race -.11 (.20)
% S Funds Spent relative to leader(closest pre-IA) -.02 (.05)
% Cash Reserves relative to leader(closest pre-IA) .05 (.04)
% Cash Reserves(closest pre-IA) .07 (.11)
Cash reserves(closest pre-IA) x competitive -.07 (.15)
Southern Democrat dummy 4.16 (3.23)
Elite party endorsements(prior to Iowa) .07 (.08)
Iowa winner 17.37 (3.82)** 13.94 (3.48)**
Iowa vote share -.16 (.11)
Iowa vote share—Gallup(last pre-IA) .23 (.12)*
New Hampshire winner 18.55 (4.17)** 18.84 (3.64)**
New Hampshire vote share .42 (.14)**
New Hampshire vote share—Iowa vote share .36 (.12)**
Adj. r2 .83 .85
F 49.43 52.22
DW 2.00 2.17
N 80 80
Average absolute error, all candidates 5.35 5.03
** denotes coefficients significant at the p < .05 level, * denotes p < .10.
and John McCain in 2008) and for those who lost the most momentum (e.g., John Glenn
in 1984, Howard Dean in 2004). Including variables that reflect the effects of “momen-
tum” from the Iowa and New Hampshire elections largely corrects for under- and
overpredictions resulting from gains or losses of momentum from these elections. Models
with information from the Iowa caucuses and the New Hampshire primary reduce by
more than half the remaining variance unaccounted for by the pre-Iowa forecast models
(compare Tables 2 and 4). The momentum models greatly reduce absolute error of
predictions in candidates’ vote shares (bottom row, Table 3), as well as the prediction
errors for the vote shares of the eventual nominees (bottom row, Table 4).
The addition of cases for 2012 improve slightly the predictive accuracy of the
Adkins and Dowdle’s (2005) momentum model, which yields an average absolute error
of 5.35% for all of the candidates compared to 8.90% in their pre-Iowa model. The actual
minus predicted vote share is 7.49% for the eventual nominees compared to 16.71% in
their pre-Iowa model. Their momentum model predicts the winner of the CPV in 11 of
12 open nominations since 1980—and all 12 if Hillary Clinton is recognized as the
winner of the 2008 Democratic primary vote. The model also shows that the candidates’
vote shares in the New Hampshire primary (but not the Iowa caucuses) also significantly
affect candidates’ shares of the vote in the remaining contested primaries.
The Steger momentum model has a slightly lower absolute average error of pre-
diction, and there is very little difference in the errors for the eventual nominees (6.05%)384 | PRESIDENTIAL STUDIES QUARTERLY / June 2013
TABLE 4
Predicted Vote Share in Contested Primaries for the Eventual Nominee in Open Nominations,
1980-20121
Adkins & Dowdle + Momentum Steger + Momentum
Pred. rank Vote Error2 Pred. rank Vote Error2
Ronald Reagan 1980 R 1 -6.56 1 -9.57
Walter Mondale 1984 D 1 2.97 1 4.67
Michael Dukakis 1988 D 1 0.08 1 -6.77
George H. W. Bush 1988 R 1 -7.72 1 -3.74
Bill Clinton 1992 D 1 -18.23 1 -10.92
Bob Dole 1996 R 1 -10.45 1 -6.87
Al Gore 2000 D 1 -4.03 1 1.39
George W. Bush 2000 R 1 -15.67 1 -4.00
John Kerry 2004 D 1 -7.32 1 -10.39
Barack Obama 2008 D3 2 -10.86 2 -10.74
John McCain 2008 R 1 -1.44 1 -2.71
Mitt Romney 2012 R 1 4.59 1 0.76
Average absolute error 7.49 6.05
1
Models based on data from the end of January for primaries in 1980, 1984, 1988, and 1992; and data from
the end of December for primaries in 1996, 2000, 2004, 2008, and 2012.
2
Predicted minus actual vote share. Negative signs indicate under prediction.
3
Clinton received the most votes across all primaries including the Michigan and Florida primaries in 2008.
as for the average candidate (5.03%). This model also correctly predicts the winner in
all 12 open nominations since 1980 if we recognize Clinton as the winner of the 2008
primary vote. In contrast to the estimates after the 2008 elections, the updated model
shows that party elite endorsements are not significant predictors of the primary vote
once the momentum variables are taken into account. There are several possibilities for
this result. One is that elite party endorsements, the party establishment type, have less
effect than in the past. Party activist effects, not measured in the Steger model, could
be gaining relative to party establishment influence. The other possibility is that the
information provided by endorsements of party elites is eclipsed by the informational
effects of the first nominating elections consistent with a Bayesian updating process.
Party elite endorsements also may be having less impact because fewer political
party elites are making an endorsement prior to the caucuses and primaries. In 2012, for
example, more than three-quarters of the Republican governors, senators, and represen-
tatives stood on the sidelines waiting for a front-runner to emerge before committing to
a candidate. In the 2004 and 2008 Democratic and the 2008 Republican campaigns, the
eventual nominee ran behind other candidates in endorsements. Romney had more elite
endorsements than had John McCain prior to the 2008 Iowa caucuses. That a greater
proportion of party elites are refraining from making an endorsement during the invisible
primary is not consistent with the theoretical argument that political party insiders
coordinate among themselves to pick their party’s nominee. If these results hold up in
other kinds of analyses, then it might suggest that the influence of party elites may be
waning relative to party activists and relative to the impact of primary voters.Steger / TWO PARADIGMS OF PRESIDENTIAL NOMINATIONS | 385
These models indicate that the winners of the Iowa and New Hampshire nominat-
ing elections gain a substantial increase in their shares of the remaining contested
primary vote. In these and other variants of the models presented here, adding informa-
tion on the results of the early caucuses and primaries improves the predictive accuracy
of the models by reducing underprediction of candidates who gain momentum and by
reducing overprediction of primary vote shares for candidates who lose momentum. Both
models have greater errors of prediction for the nominees in those campaigns in which the
nominees gained momentum in caucuses or primaries following New Hampshire, as
happened in 1992 for Bill Clinton, 2004 for John Kerry, and 2008 for Barack Obama.
Conclusions
Across nominations, the pre-Iowa forecast models predict the winner correctly
when one candidate gains a substantial lead in polls and endorsements during the
invisible primary. That is, information from the invisible primary phase of the campaign
is predictively accurate when party insiders and mass partisans coalesce behind a front-
runner in advance of the Iowa caucuses. The Pre-Iowa forecasts have greater error in open
nominations in which several candidates are roughly evenly matched in party elite
support and pre-primary national polls. The important take away is that party insiders
and party identifiers do not always coalesce strongly around a front-runner before the
caucuses and primaries, and in in these races, the earliest caucuses and primaries continue
to have substantial effects on primary votes in subsequent primaries (indicated by the
substantial reduction in forecast errors of the predicted vote shares for the eventual
nominees). Overall, the impact of momentum during the caucuses and primaries is
greater in nomination campaigns when party insiders and identifiers fail to coalesce
behind a front-runner during the invisible primary.
Future research needs to recognize that we have two patterns in presidential
nomination campaigns. Steger (2000) and Cohen et al (2003, 2008) emphasize the
importance of the invisible primary as a period of intraparty coordination and coales-
cence. Others emphasize campaign momentum (e.g., Aldrich 1980; Bartels 1988) or
winnowing during the primaries (e.g., Norrander 2006). These two views are not
incompatible. They both exist to some extent in every election, though one model or the
other tends to be dominant in a given nomination cycle. These patterns of presidential
nomination campaigns, however, reflect real differences in how nominees are selected,
even though the rules for electing delegates to the national nomination campaigns have
remained largely the same since the mid-1970s. These patterns also reflect differences
in who exercises influence over the nomination. Political party elites can influence
presidential nominations when (and perhaps only when) they weigh in on the nomination
in large numbers and are unified in their support of a particular candidate. The presi-
dential nominations from 2004 through 2012 differ from those of the late 1980s through
2000 in that fewer party insiders are making endorsements prior to the Iowa caucuses.
Party elites also vary in their unity across elections—coalescing to a greater degree in
some nomination cycles than in others. Republican Party insiders were relatively divided386 | PRESIDENTIAL STUDIES QUARTERLY / June 2013
during the invisible primaries of 1980 (splitting into Reagan and anybody-but-
Reagan camps), 2008, and 2012. The lack of public declarations of support and unity by
Republican Party insiders in 2008 and 2012 could reflect uncertainty about the candi-
dates, or it could signal the onset of Republican Party coalition fragmentation. Demo-
cratic insiders were divided or undecided in 1988, 1992, 2004, and 2008—unifying only
in the nominating elections of 1984 and 2000 when a vice president entered the race.
Overall, this variation suggests caution is warranted before embracing the argument that
political parties are able to resolve intraparty differences and coordinate support during
the invisible primary.
Given that both kinds of causal dynamics seem to exist, future research on presi-
dential nominations needs to focus on the process of coalition formation in the context of
evolving political parties. Both political parties and the candidates who seek nominations
play important roles here. Presidency scholars need to develop a theoretical framework
that melds these pieces into a cogent whole.
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