Diagnosed Anxiety and Political Judgment,
WORD COUNT: 2,750
Diagnosed Anxiety Disorder and Political Judgment:
An Empirical Test of Affective Intelligence Theory
Christopher D. Johnston
Department of Political Science
Duke University
Durham, NC 7708
christopher.johnston@duke.edu
Howard Lavine
Department of Political Science
University of Minnesota
Minneapolis, MN 55455
lavine@umn.edu
1
Diagnosed Anxiety and Political Judgment,
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Abstract
Political reasoning is contingent; sometimes voters rely primarily on their partisan habits, while
at other times they take in more (or better) information and engage in more effortful reflection.
Affective intelligence theory (AIT) – a theory of the impact of emotion on political judgment
strategies – has risen to prominence as an account of the underlying mechanisms by which this
heterogeneity occurs. However, past tests of the theory suffer from: (1) endogeneity (candidate
evaluations causing emotions); and/or (2) confounds in the measurement of emotion. In this
research, we overcome these limitations by employing a unique aspect of the 2008 ANES panel
study: an item in the March wave in which respondents were asked whether they had been
diagnosed with an anxiety disorder in the previous twelve months. Using this measure of anxiety
(which is appropriately separated from candidate evaluation) and a statistical model capable of
isolating causal order, we find strong support for the central dynamics of AIT.
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Contemporary models of political behavior posit that citizens are frugal with their
cognitive resources, preferring low effort strategies when these offer a reasonable degree of
judgment confidence. It is only when such strategies fail to reduce uncertainty that citizens will
be motivated to employ more effortful modes of deliberation (Basinger and Lavine 2005;
Hillygus and Shields 2008; Lavine, Johnston and Steenbergen 2012; Marcus, Neumann and
MacKuen 2000). Within this general dual-process framework, affective intelligence theory
(hereafter, AIT; Marcus et al. 2000) has risen to prominence as an account of the underlying
mechanisms by which this process-related heterogeneity occurs. Relying on insights from
neuroscience (e.g., Damasio 1994; Grey 1981; LeDoux 1996), Marcus and colleagues argue that
the nature of political reasoning lies in the experience of emotion. Specifically, two key
emotions, enthusiasm and anxiety – viewed as the products of functionally distinct biobehavioral regulatory systems – influence how citizens construct their political judgments. The
“disposition system,” which mediates the emotion of enthusiasm, responds to positive incentives
by directly initiating habit-based behavior. A second system, referred to by Marcus et al. (2000)
as the “surveillance system,” detects threat and danger in the environment. Activation of the
surveillance system results in the emotion of anxiety, which interrupts ongoing habitual action
and promotes increased thoughtfulness and greater motivation for learning.
Applied to the realm of mass political judgment, enthusiasm is expected to motivate
political participation and to increase voters’ reliance on their political habits. Anxiety, by
contrast, is expected to lead voters to turn away from habitual responses and devote more
attention to diagnostic information in the environment. In line with AIT, Marcus et al. (2000)
have shown that anxious voters (a) pay greater attention to the political environment and acquire
more information about candidates’ policy stands; (b) rely less on partisanship and more on
Diagnosed Anxiety and Political Judgment,
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policy preferences and assessments of candidate character in forming candidate evaluations; and
(c) defect at higher rates in presidential elections (see also Marcus and MacKuen 1993; Brader
2006). Finally, anxiety – though not anger or enthusiasm – has been found to increase the
quantity and quality of information processing, including the desire for balance and compromise
(MacKuen et al. 2010; Valentino, Hutchings, Banks and Davis 2008). Taken together, this work
suggests that anxiety leads to political decisions that are more informed by contemporary
information and less by partisan loyalty. In the language of dual-process theories of cognition,
anxiety reduces heuristic processing and stimulates systematic processing.
Despite its appeal, most empirical tests of AIT suffer from two important shortcomings:
endogeneity and confounds in the measurement of emotion. First, nearly all AIT-inspired
research relies on cross-sectional data, so that the variables assessing emotion and judgment are
measured at the same time. It is therefore possible that the former is endogenous to the latter.
Ladd and Lenz (2008) proposed such an endogenous affect mechanism to account for the indirect
effect of anxiety in a pooled cross-sectional analysis using ANES data. They argued that the
stronger influence of policy agreement on candidate evaluation among anxious (vs. non-anxious)
voters could occur in the absence of anxiety playing a causal role. If endogenous affect is
responsible for the interactions (between anxiety and partisanship and anxiety and issue
orientation), they should disappear when anxiety is measured at a lagged interval. This is exactly
what Ladd and Lenz (2008) found.
A second, related problem is that emotion is almost always measured with respect to a
specific candidate. For example, the traditional ANES emotion battery reads: “Has [candidate] –
because of the kind of person he is, or because of something he has done – made you feel
[emotion term]?” This object-centered measurement approach generates responses that are better
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characterized as object-evaluations – i.e., attitudes – than as discrete emotions. That is, when
answering a question about whether Barack Obama has ever “made you feel anxious,” voters are
likely to rely on their overall attitudes toward Obama, rather than attempt to recall whether he
has engendered a specific emotion such as anxiety, anger, or sadness. It isn’t clear, then, whether
the motives underlying variation in judgment effort are due to the experience of anxiety per se
(i.e., a discrete emotion) or because of attitudinal ambivalence, which has been shown to exert
similar effects on political judgment (e.g., Basinger & Lavine, 2005; Lavine et al. 2012).
Given the prominence of AIT in contemporary studies of political judgment, it is
important to conduct tests of the theory in which the key variable of anxiety is operationalized
independent of candidate evaluation (i.e., the DV), and with statistical models that can better
isolate causal mechanisms. We further these goals by utilizing a unique aspect of the 2008 ANES
panel study. The (panel) structure of the data allows us to rule out endogeneity as an alternative
explanation for the observed empirical patterns. Moreover, we leverage a happy coincidence of
the inclusion (for reasons unrelated to AIT) of information regarding respondents’ status as
diagnosed (or not) with an anxiety disorder. This allows us to operationalize AIT’s key variable
in a way that is uncontaminated by attitudes toward the political outcome variables of interest.
The combination of these two features allows for a particularly appealing and unique test of this
prominent theory.
Hypotheses
According to AIT, anxiety initiates a shift from reliance on standing partisan habits to a
more systematic mode of information processing in which citizens think more carefully about
contemporary information in the political environment. Thus, with respect to candidate
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evaluation, we expect anxious voters to rely less on partisan cues and more on policy preferences
than non-anxious voters. In the realm of preference formation over issues, we expect updating
among anxious citizens to occur more on the basis of policy-relevant considerations (e.g.
material interests), and less on the basis of simple partisan cues, and that non-anxious citizens
will show the reverse pattern (i.e., privileging partisan identity). We test these general
hypotheses by modeling candidate evaluations, tax preferences, and health care preferences
across the 2008 campaign season.
Methods
Data
To test our hypotheses, we rely on data from the 2008 ANES Panel Study. Respondents
participated in one survey per month throughout 2008. Of these, six (January, February, June,
September, October, and November) were focused on political content related to the 2008
elections. The “off-wave” surveys consisted of non-political content selected by groups helping
to finance the study. We take advantage of three characteristics of this study. First, evaluations of
the two major party presidential candidates (McCain and Obama) were included in both the
February and October waves. By controlling for prior candidate evaluations, we can examine
how voters’ electoral preferences changed over the course of the campaign, and thus better
isolate the impact of different considerations on evaluations. Second, two policy issues which
were particularly salient during this election – health care reform and taxes – were included in
both the January and October waves of the study. As with the modeling of candidate evaluation,
this allows us to examine how respondents updated their policy preferences over the course of
the campaign, and to determine the extent to which such changes were driven by partisanship
Diagnosed Anxiety and Political Judgment,
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versus more substantive considerations. Third, in the March wave of the study, respondents were
asked whether they had been diagnosed with an anxiety disorder in the previous twelve months.
We utilize this dichotomous indicator to operationalize the key variable of anxiety.1
Anxiety. Anxiety was assessed with the following single item (with follow-up) “Have you
ever been told by a doctor or other health professional that you had an anxiety disorder?” If yes,
a follow-up question was asked: “During the past twelve months, have you had an anxiety
disorder?” Anxiety was coded one if the respondent reported being diagnosed with an anxiety
disorder in the previous twelve months (10% of the sample), and zero otherwise. Substantively,
this measure can be understood as increasing the probability of experiencing anxiety at any given
time during the campaign, and thus as an instrument for the actual experience of anxiety during
the process of political judgment.
To alleviate concerns that diagnosed anxiety is simply a proxy for cognitive ability or
political engagement (or other factors), in Figure A1 of the Appendix we provide correlations
between anxiety diagnosis and (1) education, (2) political knowledge, (3) income, and (4)
partisan strength. All of these correlations turn out to be negative in direction (i.e., anxiety is
linked to less education, income, weaker party ID, etc.). These correlations (which we control for
in any case) thus work against our hypotheses.
Comparative Candidate Evaluation. Evaluations of Obama and McCain – each measured
on 7-point scales – were solicited in the February and October waves of the panel study. In each
wave, we subtracted the Obama evaluation from the McCain evaluation, and recoded these to
range from zero to one.
1
For item wordings, see Appendix.
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Issue Orientation. Eight policy issues were included in the January wave of the survey:
gay marriage, tax increases on wealthy Americans, prescription drugs for senior citizens,
universal health care, suspension of habeas corpus for suspected terrorists, warrantless
wiretapping, temporary work visas for non-citizens, and an eventual path to citizenship for
illegal immigrants. Consistent with Marcus et al. (2000), we averaged responses to these issues
to create an overall scale representing the respondent’s issue orientation (alpha = .61 for the
January scale and .69 for the October scale).
Income and Perceptions of Health Care. We assessed respondents’ reliance on
substantive considerations in the updating of preferences over taxes and health care with two
measures. For the taxes model we utilize household income (measured prior to the start of the
panel study). For the healthcare model we use the respondent’s reported perception of the change
in healthcare quality from 2001 to 2008. We expect respondents who perceive a negative change
in quality to prefer liberal reforms to the health care system.
Partisanship. Respondents’ party identification was measured with the traditional sevenpoint scale in the January wave.
Controls. We control for several variables, including: age, gender (1=male), race (two
dummy variables, one for black and one for Hispanic respondents), education, income, and
political sophistication.
All variables were recoded to range from zero to one.
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Results
Candidate Evaluations
Based on AIT, we predicted that anxiety would decrease the influence of partisanship and
increase the influence of issue orientation on candidate evaluations. Using our measure of
diagnosed anxiety, we find an empirical pattern consistent with AIT. As shown in the first three
columns of Table 1, the interaction of anxiety with partisanship is negative and statistically
significant (B=-.11, p<.05), indicating that anxiety tempers the influence of partisanship on
comparative candidate evaluation over the course of the campaign.2 We also find support for
AIT’s compensatory dynamic, that anxiety significantly increases voters’ electoral reliance on
issue orientation (B=.18, p<.05). Thus, in a context in which (a) there is little concern for reverse
causality, and (b) anxiety is operationalized separately from candidate evaluation itself, we find
strong support for the central dynamics of AIT.
To better interpret the substance of these findings, in Figure 1 we plot the marginal
effects of partisanship and issue orientation across the two values of anxiety. For non-diagnosed
respondents, the expected change in relative preference for McCain, moving from strong
Democrat to strong Republican, is .28, or 28% of the range of the scale. This is a substantial
change, as it controls for initial candidate preferences in February. For diagnosed respondents,
however, this effect drops by 10 percentage points, to .18. With respect to issue orientation, the
effect among non-diagnosed respondents is .26, while for diagnosed respondents it increases by a
substantial 18 percentage points, to .44. In sum, among non-anxious individuals, partisanship
and issue orientation exert approximately equally strong effects on candidate evaluation; by
contrast, among anxious individuals, the effect for issues is about 2.5 times that of partisanship.
2
As we had strong priors (derived from AIT), we use one-tailed tests for our key hypotheses. However, we report
the two-tailed p-values for all variables in Table 1.
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Policy Preference Updating
We turn now to the results for policy preferences in October, controlling for preferences
in January. These results are shown in the middle and right columns of Table 1. For taxes, we
find a pattern consistent with AIT, although the results are somewhat weaker than for candidate
evaluations. First, as expected, anxiety significantly decreases the impact of partisanship on
preference updating over the course of the campaign (B=-.17, p<.05). Moreover, anxiety
increases the influence of income on tax preferences, but this effect does not quite reach
statistical significance (B=.19, p<.15). The overall pattern, however – graphed in Figure 2a – is
highly consistent AIT’s predictions. For non-anxious respondents, the predicted marginal effect
of partisanship (.24) is much larger than that of income (.04). As the theory predicts, this pattern
is reversed for anxious respondents, with predicted marginal effects of .24 and .07 for income
and partisanship, respectively.
Finally, we turn to the results for healthcare preferences, found in the last three columns
of Table 1, and plotted in Figure 2b. Although the relevant interactions fail to reach conventional
levels of statistical significance, the pattern of effects is in line with AIT. Specifically, for nonanxious respondents, the predicted marginal effect of partisanship (.22) is much larger than that
for healthcare perceptions (.08). For anxious respondents, however, the impact of perceptions
doubles (to .16) and the impact of partisanship decreases slightly (to .18).
Conclusion
An important thrust of contemporary work on mass politics is that political reasoning is
contingent: sometimes voters rely primarily on their partisan habits, ignoring more diagnostic
information (even if they are capable of acquiring it), while at other times they take in more (or
better) information and engage in more effortful reflection. The purpose of our investigation was
Diagnosed Anxiety and Political Judgment,
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to provide an improved test of the leading theory of process-related heterogeneity, affective
intelligence theory (Marcus et al. 2000). Given the prominence of the theory in political science,
as well as the increasing interest in emotion among social scientists generally, we believe it is
critical to test AIT within a more auspicious context, one that overcomes the twin problems of
endogeneity and confounds of measurement.
In three tests, we find meaningful support for AIT. In the core model predicting candidate
evaluations, we find strong support for AIT’s signature dynamics: anxiety decreases voters’
reliance on partisan habits, and increases their reliance on the more normatively pleasing (but
cognitively demanding) dimension of issue preferences. We conceptually replicated this dynamic
in our analysis of preference updating on salient policy issues. Over the course of the 2008
campaign, changes in preferences on taxes and healthcare were driven more by partisanship
among non-anxious individuals, and driven more by either material interest or perceptions of the
quality the policy status quo (healthcare) among anxious individuals. Taken together, this pattern
of findings – protected as they are from both endogeneity and concerns about the object-related
measurement of anxiety – provides strong evidence for one of the leading theories in the
discipline.
Our analyses are not, however, without their own limitations. First, in utilizing a panel
design, we are better able to isolate the causal effects of key variables, but this comes at the
expense of statistical power. By controlling for lagged values of key dependent variables,
especially during time spans of only several months, these tests are conservative. Second,
although our unique operationalization of diagnosed anxiety does not suffer from problems of
object association, it is more diffuse than candidate-centered measures. Specifically, we assume
that diagnosed anxiety is a proxy – albeit a rather inefficient one – for the probability of
Diagnosed Anxiety and Political Judgment,
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experiencing anxiety at any given time point; including during a context in which political
judgments are made in a campaign. These caveats should not be taken as apologies for the
limitations of the study, but simply as an attempt to place bounds on the interpretation of our
results.
In conclusion, this study, for its own limitations, provides a needed empirical reexamination of AIT. Our analysis, shorn of concerns with endogeneity and confounded
measurement, indicates that anxiety does indeed move citizens to eschew toeing the party line,
and to consult more substantive considerations in making their judgments. In a more general
vein, the concept of democracy implies the constraint of representatives by citizens. In a political
world where partisan cues are utilized unflinchingly, citizens may become the effective agents of
elite interests, as the latter learn that they may take actions (e.g. make policy statements) with
impunity. The surveillance system, and its manifestation in the form of the experience of anxiety,
as Marcus and his colleagues (1988, 1993, 2000, 2010) have long argued, represents a bulwark
against this form of tyranny.
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REFERENCES
Basinger, Scott, and Howard Lavine. 2005. “Ambivalence, Information and Electoral Choice.”
American Political Science Review 99(2): 169-184.
Brader, Ted. 2006. Campaigning for Hearts and Minds: How Emotional Appeals in Political Ads
Work. Chicago: The University of Chicago Press.
Chaiken, Shelly, Akiva Liberman, and Alice Eagly. 1989. Heuristic and Systematic Processing
Within and Beyond the Persuasion Context. In Unintended thought: Limits of awareness,
intention, and control, eds. Jim Uleman and John Bargh. New York: Guilford, 212-252.
Damasio, Antonio. 1994. Descartes Error: Emotion, Reason and the Human Brain. New York:
G.P. Putnams Sons.
Gray, Jeffrey A. 1981."The Psycho-physiology of Anxiety." In Dimensions of Personality, ed.
Richard Lynn. New York: Pergamon, 233-252.
Hillygus, D. Sunshine, and Todd G. Shields. 2008. The Persuadable Voter: Wedge Issues in
Presidential Campaigns. Princeton: Princeton University Press.
Ladd, Jonathan, and Gabriel Lenz. 2008. “Reassessing the Role of Anxiety in Vote Choice.”
Political Psychology 29(2): 275-296.
Lavine, Howard, Christopher Johnston, and Marco Steenbergen. 2012. The Ambivalent Partisan:
How Critical Loyalty Promotes Democracy. New York: Oxford University Press.
Ledoux, Joseph. 1996. The Emotional Brain: The Mysterious Underpinnings of Emotional Life.
New York: Simon and Schuster.
MacKuen, Michael, Jennifer Wolak, Luke Keele, and George E. Marcus. 2010. “Civic
Engagements: Resolute Partisanship or Reflective Deliberation.” American Journal of
Political Science 54 (2): 440-458.
Marcus, George. 1988. “The Structure of Emotional Response: 1984 Presidential Candidates.”
American Political Science Review 82(3): 737-761.
Marcus, George and Michael MacKuen. 1993. “Anxiety, Enthusiasm and the Vote: The
Emotional Underpinnings of Learning and Involvement During Presidential Campaigns.”
American Political Science Review 87(3): 672-685.
Marcus, George, W. Russell Neuman, and Michael Mackuen. 2000. Affective Intelligence and
Political Judgment. Chicago: The University of Chicago Press.
Valentino, Nicholas, Vincent Hutchings, Antoine Banks, and Anne Davis. 2008. “Is a Worried
Citizen a Good Citizen? Emotions, Political Information Seeking, and Learning Via the
Internet.” Political Psychology 29(2): 243-273.
Diagnosed Anxiety and Political Judgment,
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APPENDIX A. QUESTION WORDINGS FOR SELECTED MODEL VARIABLES
Candidate Evaluations
Do you like Barack Obama (John McCain), dislike him, or neither like nor dislike him?
Do you like/dislike him: a great deal, a moderate amount, or a little?
Policy Issues (All measured on a seven-point scale indicating degree of support or opposition)
Do you favor, oppose, or neither favor nor oppose an amendment to the U.S. Constitution
banning marriage between two people who are the same sex?
Do you favor, oppose, or neither favor nor oppose raising federal income taxes for people who
make more than $200,000 per year?
Do you favor, oppose, or neither favor nor oppose the U.S. government paying for all of the cost
of prescription drugs for senior citizens who are living on very little income?
Do you favor, oppose, or neither favor nor oppose the U.S. government paying for all necessary
medical care for all Americans?
Imagine that the U.S. government suspects a person in the United States of being a terrorist. Do
you favor, oppose, or neither favor nor oppose the government being able to put this person in
prison for months without ever bringing the person to court and charging him or her with a
crime?
Do you favor, oppose, or neither favor nor oppose the U.S. government being required to get a
court order before it can listen in on phone calls made by American citizens who are suspected of
being terrorists?
Citizens of other countries who have come to live in the United States without the permission of
the U.S. government are called "illegal immigrants."
Do you favor, oppose, or neither favor nor oppose allowing illegal immigrants to work in the
United States for up to three years, after which they would have to go back to their home
country?
Do you favor, oppose, or neither favor nor oppose the U.S. government making it possible for
illegal immigrants to become U.S. citizens?
Diagnosed Anxiety and Political Judgment,
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Health Care Perceptions
Compared to 2001, would you say the following is now: much better, somewhat better, about the
same, somewhat worse, or much worse? Health Care in the U.S.
Political Knowledge
Do you happen to know how many times an individual can be elected President of the United
States under current laws?
For how many years is a United States Senator elected – that is, how many years are there in one
full term of office for a U.S. Senator?
How many U.S. Senators are there from each state?
For how many years is a member of the United States House of Representatives elected – that is,
how many years are there in one full term of office for a U.S. House member?
According to federal law, if the President of the United States dies, is no longer willing or able to
serve, or is removed from office by Congress, the Vice President would become the President. If
the Vice President were unable or unwilling to serve, who would be eligible to become president
next? The Chief Justice of the Supreme Court, the Secretary of State, or the Speaker of the
House of Representatives?
What percentage vote of the House and the Senate is needed to override a Presidential veto? A
bare majority, two-thirds, three-fourths, or ninety percent?
Diagnosed Anxiety and Political Judgment,
APPENDIX B. CORRELATIONS OF DIAGNOSED ANXIETY WITH POLITICAL
MODERATORS
-.3
-.2
-.1
0
.1
.2
.3
Figure A1. Correlations of Diagnosed Anxiety with Political Variables
Education
Sophistication
Income
Notes: Bars are estimated gammas for each political variable with diagnosed anxiety.
Partisan Strength
16
Diagnosed Anxiety and Political Judgment,
Figure 1. Effects of PID and Issues on Candidate Evaluations
General Issue Preferences
0
.1
.2
.3
.4
.5
.6
Partisanship
No Anxiety
Notes: Bars represent predicted marginal effects. Spikes are 95% CIs.
Diagnosed Anxiety
17
Diagnosed Anxiety and Political Judgment,
Figure 2A. Effects of PID and Income on Tax Preferences
Income
-.2
0
.2
.4
.6
Partisanship
No Anxiety
Diagnosed Anxiety
Notes: Bars represent predicted marginal effects. Spikes are 95% CIs.
Figure 2B. Effects of PID and Perceptions on Health Care Preferences
Health Care Perceptions
No Anxiety
Diagnosed Anxiety
-.2
0
.2
.4
.6
Partisanship
Notes: Bars represent marginal effects. Spikes are 95% CIs.
18
Diagnosed Anxiety and Political Judgment,
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Table 1. Regression Estimates for All Models
______________________________________________________________________________
Evaluations
Taxes
Health Care
Variables
B
SE
p
B
SE
p
B
SE
p
______________________________________________________________________________
Partisanship
.25
.05
.00
-.07
.10
.48
.20
.11
.06
PID X Anxiety
-.11
.06
.07
-.17
.10
.08
-.06
.11
.59
Issues
.09
.10
.35
Issues X Anxiety
.18
.10
.08
Income
-.02
.03
.49
.09
.15
.56
.15
.06
.02
Income X Anxiety
.19
.17
.27
Health Perceptions
.13
.15
.40
Health X Anxiety
.10
.18
.58
Anxiety
-.03
.04
.41
-.05
.12
.66
-.04
.07
.57
Lagged
PID X Soph
.56
.05
.03
.07
.00
.53
.59
.47
.03
.13
.00
.00
.55
.04
.03
.15
.00
.81
Ideo/Income/Health
X Soph
.25
.14
.07
-.06
.21
.76
-.07
.23
.74
Sophistication
Age
Male
Black
Hispanic
Education
Constant
-.19
.00
-.01
-.07
-.04
-.06
.16
.05
.03
.01
.02
.03
.02
.05
.00
.95
.33
.00
.09
.01
.00
-.21
.05
.04
.03
-.01
.05
.08
.15
.05
.02
.05
.05
.04
.10
.15
.30
.07
.54
.80
.29
.44
.05
-.03
.03
-.05
-.08
.01
-.01
.10
.06
.02
.05
.06
.05
.08
.62
.63
.22
.36
.14
.82
.95
R2
.69
.58
.56
N
1026
531
529
______________________________________________________________________________
Notes: Data from 2008 ANES Panel Study. Entries are OLS estimates and standard errors. All
variables are coded on a zero to one scale.