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Polit Behav
DOI 10.1007/s11109-012-9199-8
Variability or Moderation? The Effects of Ambivalence
on Political Opinions
Kenneth Mulligan
Springer Science+Business Media, LLC 2012
Abstract Prior research theorizes that ambivalence makes opinions about an
object unreliable in the sense of being haphazard, unpredictable, or variable. As
such, ambivalence is a prominent explanation for seeming nonattitudes in opinion
surveys. This study proposes an alternative account of the effects of ambivalence on
attitudes. It posits that people who are ambivalent about an issue split the difference
between their conflicting considerations by taking a position near the middle of the
bipolar opinion scale, which reflects a moderate attitude. I show how the widelyused method of modeling the supposed variability of ambivalent opinions conflates
variability and moderation. This problem is addressed by modeling variability and
moderation of attitudes separately, without this confound. Using this strategy in
analyses involving four datasets and three policy domains, the results show that
ambivalence is associated with moderate, not variable, attitudes. Ambivalence does
not increase the variability of opinions but, rather, moves them quite predictably
toward the middle of the response scale. The results recast our understanding of the
effects of ambivalence on political opinions and raise questions about the ability of
ambivalence to explain nonattitudes in surveys.
Politics in democratic societies calls on citizens to make tough choices. These
choices are hard not just because they are complicated but also because they often
create ambivalence. Ambivalence occurs when a person has mixed feelings and
beliefs about an object. When asked to give an opinion about an issue, the
conflicting considerations make it difficult for the individual to take a stand on one
K. Mulligan (&)
Department of Political Science, Southern Illinois University Carbondale, Mailcode 4501,
Carbondale, IL 62901-4501, USA
e-mail: [email protected]
Polit Behav
side or the other. Ambivalence has been shown to be common (Basinger and Lavine
2005). Zaller and Feldman (1992) argue that it is the norm for most people, on most
issues, in American politics. Many studies theorize that ambivalence leads to the
expression of attitudes that are variable in the sense of appearing haphazard or
unpredictable in opinion surveys. In this respect, ambivalence is widely viewed as
an explanation for the seemingly random responses that individuals often given
when answering questions about their political attitudes.
In this study, I offer an alternative account of the effects of ambivalence on
opinions. Consistent with theory, as well as empirical evidence, I argue that when
presented with evaluating an issue, individuals who are ambivalent about it average
across their mix of positive and negative considerations at the time of judgment. But
instead of leading to opinion reports that are variable or unpredictable, this
averaging process leads ambivalent respondents to adopt a moderate or middle-ofthe-road stance on the issue.
The problem this account presents is that the method typically used to model the
cross-sectional variability of ambivalent attitudes, the binary heteroskedastic choice
model (hereafter, BHCM), confounds variability and moderation.1 This is
problematic for the study of political behavior because theory links ambivalence
to variable opinions as well as moderate ones but the BHCM cannot differentiate
between these two theorized effects of ambivalence on attitudes. More importantly,
if ambivalence is associated with moderate opinions, rather than variable ones, then
the results of prior research are ambiguous, and our understanding of the
consequences of ambivalence for political opinions may be wrong.
This is addressed by modeling separately the variance of opinions and their
degree of moderation, without conflating them. The unobserved variance of
attitudes is modeled using ordered heteroskedastic choice models (hereafter,
OHCM’s). As I will demonstrate, the primary benefit of the OHCM is that it models
the variance of the attitude without this confound. Moderation is modeled in ordered
probability models. These two tests—one for variability, another for moderation—
are conducted on 15 opinion items in four datasets, including an analysis of opinions
toward wiretapping in response to terrorism and replications of Alvarez and
Brehm’s (1998, 2002) studies of opinions about racial issues (2002) and the IRS.
In doing so, this study makes three contributions to our understanding of public
opinion. First, it shows the misleading inadequacy of the BHCM as a means of
modeling ambivalent attitudes and recommends the OHCM as an appropriate
alternative.2 It also shows the potential problem of using dichotomous questions,
and utility of multichotomous ones, to tap ambivalent opinions. More importantly,
the study shows that instead of making attitudes variable or unpredictable,
ambivalence makes them predictably moderate. When respondents are offered more
than two response options, ambivalent opinions tend to fall quite predictably toward
the middle of the road. Given this, scholars should reevaluate the role of
ambivalence as an explanation for nonattitudes in opinion surveys.
In this study ‘‘moderation’’ refers to opinions that tend toward the center of a bipolar attitude scale, not
an interaction between variables.
The BHCM may be appropriate in other contexts, such as when a choice is inherently binary.
Polit Behav
The Psychology of Ambivalence
I begin with theory on the psychology of attitudes that has motivated research on
ambivalence. This theory is rooted in our understanding of how people respond to
opinion questions in surveys and on questionnaires. When asked to report an attitude
on some political topic, most people do not have an opinion stored in long-term
memory that they can simply retrieve and report. On most issues, people do not have
one single attitude but a distribution of considerations from which they construct an
opinion. Responding to an opinion item, the individual canvasses memory for
relevant feelings, beliefs, and values, and then averages across those that are
associated with the attitude object and accessible at the time the question is asked.
This theory of attitudes is sometimes referred to as the ‘‘constructionist’’ or
‘‘memory based’’ model, because people are said to construct opinions based on
considerations that are salient or accessible in memory when reporting the attitude
(see Zaller and Feldman 1992 for a classic statement of the theory and Schwartz
2007 for a more recent review).
The memory based model implies that the opinion an individual might give about a
topic is not a fixed position. If attitudes toward an issue were arrayed on a latent
continuum that runs from extremely positive to extremely negative, then people who
have many positive considerations and few negative ones would tend to adopt a
position on the positive side of the scale, while those with entirely negative
considerations would likely take a position on the other side. The valence of a
respondent’s distribution of feelings and beliefs about the issue—tending positive,
negative, or somewhere in between—constrains the range of responses he or she might
give on the issue in a single survey, or multiple surveys over time. In theory, if one were
to ask a respondent the same survey question over and over, in an infinite number of
interviews, the person’s reported opinions on the issue would approximate his or her
distribution of considerations. This theoretical sampling distribution of a respondent’s
attitude would have a central tendency and a spread. At the level of the individual, then,
we can conceive of an attitude toward an object not as a fixed location but as having
both a mean and a variance (Alvarez and Brehm 2002; Eagly and Chaiken 2007;
Feldman 1989; Page and Shapiro 1992; Zaller and Feldman 1992).
The Variability Hypothesis
Scholars of ambivalence have derived from the memory based model the hypothesis
that ambivalence makes attitudes highly stochastic. The memory based model
implies that people who are ambivalent about an issue have a wide distribution of
potential opinions that they could give in any single survey, which is to say, these
individuals have a greater variance of opinion on the topic at hand (Alvarez and
Brehm 1995; Feldman 1995). In repeated surveys, they would be expected to give a
wider distribution of opinions—some positive, others negative—making their
responses to the same opinion item, either at a single point or across time, appear
haphazard relative to people whose considerations place them consistently on one
side of the issue. This is the variability hypothesis: Ambivalence makes attitudes
variable in the sense of being unpredictable.
Polit Behav
The variability hypothesis has motivated many ambivalence studies. These
studies suggest that ambivalence is associated with variable opinions related to
partisanship (Keele and Wolak 2006), social groups (Rudolph 2005), presidential
approval (Kriner 2006), and issues such as abortion (Alvarez and Brehm 1995), the
morality of suicide (Alvarez and Brehm 2002), and English-only laws (Alvarez
et al. 2003). However, they have shown mixed results about ambivalence toward
racial issues (Alvarez and Brehm 1997; Alvarez et al. 2003), and no discernable
variability of attitudes toward government spending (Jacoby 2006), euthanasia
(Alvarez and Brehm 2002), or school prayer (Alvarez et al. 2003).
The Moderation Hypothesis
From the memory based model one might derive an alternative hypothesis of the
effects of ambivalence on opinions. Consistent with most studies of ambivalence in
political and social psychology, ambivalence is conceptualized here as a transient
state that occurs when the individual evaluates an attitude object. Typically this
occurs when he or she is asked to make a judgment about a policy issue or political
candidate. At the moment this occurs, some considerations relevant to the judgment
may be highly accessible while others are less so. When the considerations that come
to mind have both positive and negative implications for the choice, this indicates
ambivalence (Hochschild 1981; Riketta 2000; Steenbergen and Brewer 2004;
Thompson et al. 1995).3 In this respect, ambivalence is something that happens in the
here-and-now, when the inconsistent considerations about the attitude object are
brought to mind. It is an episodic event rather than a chronic disposition or trait
(Breckler 2004; Craig and Martinez 2005; Lavine 2004; McGraw et al. 2003).
Also derived from the memory based model, I assume that individuals respond to
opinion items about political issues by averaging across salient considerations at the
time of judgment. This is particularly likely to occur among people who are
ambivalent about an issue, because ambivalence has been shown to motivate
individuals to think about issues more deeply (Lavine et al. forthcoming). If
respondents have accessible thoughts about the issue that are predominantly
positive, and they take a position in favor, while those with mostly negative
considerations adopt a position on the other side, it follows that individuals who are
ambivalent about the issue, averaging across their mix of positive and negative
feelings and beliefs, would be expected to take a stand between the two extremes, in
the vicinity of the middle of the response scale. This is the moderation hypothesis:
Ambivalence makes opinions moderate in the sense of being middle-of-the-road.
The moderation hypothesis is consistent with classic models of voting and
attitudes. In Kelley and Mirer’s (1974) ‘‘simple act of voting,’’ citizens weigh their
likes and dislikes about the candidates and cast their ballots for the one with the
highest net favorability. Related to attitudes, Anderson’s theory of information
integration (e.g., Anderson 1973, 1981), deals with how people combine
Most studies of ambivalence address the causes or consequences of ambivalence as it occurs at a single
point in time. Scholars have only recently begun to evaluate how and why ambivalence might change
over time (see Rudolph 2011).
Polit Behav
considerations about an object into an opinion. Each piece of information i is
assigned a scale value si, which reflects its degree of favorableness or unfavorableness for the individual. The scale value si is multiplied by a weight wi, which is
the salience or importance of that consideration for the person who forms the
judgment. When asked to give an opinion on an issue r, individuals average across
the considerations such that
si wi
r¼ P
According to information integration theory—and consistent with the moderation
hypothesis—individuals who have a nearly even mix of positive and negative
considerations that are salient would be expected to offer a moderate opinion.4
The moderation hypothesis is also consistent with the findings of several other
studies of voting and opinions. They show that individuals whose considerations
about a domain are consistently positive or negative tend to express opinions about
the topic that are very positive or negative (Judd and Lusk 1984; Millar and Tesser
1986), while those who have a more even mix of positive and negative information
tend to offer more moderate points of view (Federico 2004; Linville 1982). In the
domain of electoral politics, Kam (2006) found that voters who have both positive
and negative thoughts about senatorial candidates express more moderate evaluations of the candidates than voters whose considerations are one-sided. Similarly,
dealing specifically with ambivalence, Meffert et al. (2004) show that voters who
are ambivalent about political candidates give moderate candidate evaluations.
Results like these have been shown in studies of conflicting values. Work by
Fletcher and Chalmers (1991) and Peterson (1994) shows that people who are
conflicted over the principles of equality and individualism tend to express more
moderate opinions about affirmative action programs, while Liberman and
Chaiken’s (1991) respondents who were conflicted between the values of individual
freedom and national security were more middle-of-the-road in their views about
aggressive efforts of the CIA to collect intelligence. Both theory and related
research are consistent with the moderation hypothesis.
Variability Moderation, or Both?
The present study addresses whether ambivalence is associated with variability,
moderation, or both at the same time. Although they have been discussed here as
alternatives, it is entirely possible that ambivalence makes attitudes both variable
and moderate simultaneously. However, testing this possibility is made difficult by
the fact that most tests of the variability hypothesis use the BHCM, and the results
of the BHCM, in the context of public opinion, conflate variability and moderation.
This may have led to a misinterpretation of the effects of ambivalence on attitudes.
Anderson’s model would be a better reflection of the memory based model if r were an average based
on a sampling of considerations rather than deterministic.
Polit Behav
To understand how, consider first the general form of the homoskedastic binary
response model:
Prðyi ¼ 1Þ ¼ gðxi bÞ;
where the probability that yi = 1 is a function of a set of explanatory variables xi, a
set of parameters b, and a link function g that represents either the logistic or probit
distribution. The model assumes the errors are equally distributed or homoskedastic,
otherwise the parameter estimates may be biased, inconsistent, and inefficient. The
homoskedasticity assumption is reflected in the right side of the equation, which is
implicitly divided by the standard deviation of the error distribution for the ith
xi b
Pr ðyi ¼ 1Þ ¼ g
The value of g is arbitrary so long as it is a constant. For ease of estimation, the logit
model assumes that r ¼ p3 and the probit model assumes that r = 1.
The difference between the traditional homoscedastic logit or probit and the
heteroskedastic versions of these models is that the BHCM relaxes the assumption
of homoskedasticity using the variance formula developed by Harvey (1976):
Var½i ¼ expðczi Þ2 ;
where z is a vector of variables that explain the error variances in the underlying
latent variable y* and c is a vector of coefficients that estimate the effect of z on the
error variances.
The BHCM does this by replacing the r in the ordinary binary response model
with exp (c zi)2:
xi b
Pr ðyi ¼ 1Þ ¼ g
expðczi Þ2
This transforms the general binary response model into two separate, but interrelated, equations. The numerator or choice component models the dependent variable
as a function of a set of explanatory variables xi. The denominator or variance
component models the error variance as a function of its own set of predictors. In
this variance equation (the log of) ri is modeled as a set of variables zi theorized to
increase or decrease the variance. Substantively, as Alvarez and Brehm discuss in
their works that use this method, where a variable m in zi is positively associated
with the error variance, this indicates that the attitudes of respondents high in m are
less predictable, and inferentially, more variable, than those low in m (see Alvarez
and Brehm 2002 for a review).5
Readers interested in a more detailed discussion should consult Alvarez and Brehm’s first (1995) study
using this method, the online technical appendices of their book (2002), or Greene (1993). The
appendices to Alvarez and Brehm (2002) are available online at The
mechanics of the ordered HCM are discussed in Alvarez and Brehm (1998).
Polit Behav
Example of a Variable Attitude
What might such a variable attitude look like? In Figure 1 hypothetical Respondent
A’s attitude toward some issue is arrayed on y*, a latent attitude dimension. The
scale of y* ranges from -?, the most negative attitude possible on this issue, to
?, the most positive. A dichotomous opinion item is imposed on y*. A is given just
two choices, ‘‘favor’’ and ‘‘oppose.’’ The two response options are separated by the
threshold jb as shown. The mean of A’s attitude is to the right of jb, which means
that A would be expected to respond ‘‘favor’’ on this question. However, the
variance of A’s attitude, plotted as a density function above her predicted response,
is wide, extending well across the threshold. A’s variable opinion would be poorly
predicted by the BHCM. If her variable attitude is associated with ambivalence, it
would be evident in the BHCM as a positive, statistically significant coefficient on
the measure of ambivalence in the variance model. Where this occurs, it has been
interpreted as inferential evidence of a variable attitude (e.g., Alvarez and Brehm
1997, p. 354; 1998, 425; 2002, Chap. 4). This is the standard interpretation of the
BHCM in ambivalence studies.
Alternative Interpretation
The problem here is that there is a second interpretation that is equally plausible and
also consistent with theory. Consider Respondent B, also in Fig. 1. His attitude
toward the same issue is not discernably ‘‘favor’’ or ‘‘oppose’’ because it is at the
center of the distribution. It straddles the threshold between the two response
options. He would appear to be neutral, neither favoring nor opposing. The variance
of his attitude, plotted as a distribution over his expected response, is narrow relative
to A. But because he is in the middle, the variance extends across the ‘‘favor’’ and
‘‘oppose’’ sides of the scale about equally. Just like A’s attitude, the BHCM would
be hard pressed to predict which side B will take.
B’s middle-of-the-road stance has implications for our interpretation of the
BHCM. As Glasgow’s (2008) Monte Carlo simulations demonstrate, an attitude
may be less predictable in the BHCM either because the underlying variance of the
attitude is wide (respondent A) or because it is close to the threshold (B) (see also
Braumoeller 2006). In other words, where A’s attitude is poorly predicted by the
Fig. 1 Two attitudes modeled with a binary heteroskedastic choice model
Polit Behav
choice model, resulting in a large error variance, because her attitude is itself
variable, B’s attitude is also poorly predicted, also resulting in a large error variance,
not because his attitude is variable, but because he is moderate on the issue. An
attitude that is moderate, in this context, is the opposite of one that is extreme.
Attitude extremity reflects the intensity, unqualifiedness, and commitment that a
person has in his or her attitude about a particular object (Abelson 1995).6 A
moderate attitude is one that is less intense, more qualified, and to which the
individual is less committed.7 Political and social psychologists measure moderation
on an extremity scale than ranges from moderate to extreme. Usually this is done by
taking a bipolar response scale and folding it at its midpoint, with low values
reflecting moderate opinions and high values extreme ones.
Studies of ambivalence that use the BHCM interpret a positive, significant
coefficient on ambivalence to indicate a wider variance of the attitude. What this
discussion shows is that instead of increasing the unobserved variance of attitudes, a
positive coefficient could just as easily indicate that ambivalence moves the mean of
the opinion—not the variance—away from the extremes of the attitude scale, such
as ‘‘strongly favor’’ and ‘‘strongly oppose,’’ toward the center of the scale, as in
‘‘somewhat’’ favor or oppose, or a neutral response, ‘‘neither favor nor oppose.’’ In
other words, rather than making attitudes more variable, ambivalence could be
making them more moderate. The problem with using the BHCM to study
ambivalence is that it cannot differentiate between variability and moderation.8
Testing the Hypotheses
Because of this inherent ambiguity, we cannot say whether ambivalence is
associated with variable attitudes, moderate ones, or both. I address this issue using
two types of models, one that allows me to test the variability hypothesis without the
confound, and another that tests directly for moderation.
Attitude extremity, like ambivalence, is one of several dimensions of attitude strength. Others include
subjective certainty, personal importance, and accessibility, to name a few (see Miller and Peterson 2004
for a review). Research shows that these and other dimensions of attitude strength are often correlated,
and sometimes causally related, but are empirically distinct: They do not reflect a single underlying
‘‘attitude strength’’ construct (Krosnick et al. 1993).
Research on moderate opinions has raised questions about the meaning of middle responses in
particular (e.g., ‘‘neither favor nor oppose’’). Some have suggested that middle responses could reflect
something other than neutrality, such as indifference, ‘‘don’t know,’’ an attempt to avoid taking a
potentially controversial position, or satisficing which, in this context, means offering an opinion that will
appear reasonable without having to put much thought into it (Krosnick 1991). However, methodological
research that addresses these potential alternative interpretations of middle responses fails to support
them, suggesting instead that most respondents who adopt a middle position really are neutral
(O’Muircheartaigh et al. 2000). In one recent study, Malhotra et al. (2009, p. 317) conclude that ‘‘on
balance, respondents who placed themselves at the midpoint belonged there.’’
See Achen 2002, p. 445 and Braumoeller 2006, p. 273, for brief theoretical mentions of this
interpretation problem.
Polit Behav
Fig. 2 Same two attitudes modeled with an ordered heteroskedastic choice model
Identifying Variability
The test for variability uses an ordered heteroskedastic choice model (again,
OHCM). This model, like its binary counterpart, models both the choice and the
variance simultaneously. But the OHCM is different in three key respects. One is
that the OHCM, by offering respondents more than two response options, does not
force them to take a clear stand on one side of the issue or the other. It allows
participants to choose from three or more ordinal categories and adopt a position
that indicates the extremity, as well as direction, of their opinion. Much of the
research that uses the BHCM to study ambivalence, including Alvarez and Brehm’s
original work on abortion attitudes (1995) and racial opinions (1997), has used
archived opinion data in which ordinal items were not available. These studies are
necessarily limited to dichotomous items and the BHCM. However, for the study of
ambivalence, these additional response options in an ordinal dependent variable are
crucial because ambivalence may move opinions toward a position of moderation.
A second difference is that the BHCM, with only two response options in the
choice model, has been criticized for being tenuously identified and, in
consequence, producing potentially misleading results (Keele and Park 2006a).
But the OHCM, with its multiple responses, is less likely to be problematic in this
respect (Keele and Park 2006b; Williams 2009).
A third—and for this analysis, more important—difference is that the OHCM
does not confound the variance of the attitude with its degree of moderation.9
Consider again the hypothetical opinions of respondents A and B, this time in
Fig. 2, where their same two attitudes are mapped onto an ordinal, rather than a
dichotomous, scale. Here, instead of allowing them only two response options, they
choose among five. They can indicate that they ‘‘strongly’’ or ‘‘somewhat’’ favor/
oppose, or ‘‘neither favor nor oppose.’’ A’s attitude still falls on the ‘‘favor’’ side of
the issue, and the variance extends substantially across at least three of the four
category thresholds. Just as her opinion was variable as measured with the
dichotomous item using the BHCM in Fig. 1, so it is also variable as measured
using the ordinal question, and modeled with the OHCM, in Fig. 2.
See Alvarez and Brehm 1998 and Technical Appendix D of 2002 for a derivation of the OHCM.
Polit Behav
But how does the OHCM avoid confounding variability and moderation? To
answer this question I turn to B’s same opinion, this time plotted in Fig. 2. On this
ordinal scale, it is again at the center of y*—it is still moderate—but notice how the
distribution of the variance no longer straddles a threshold between response
options. The distribution of Opinion B in Fig. 2 fits well within the two cutpoints
that delineate ‘‘neither favor nor oppose.’’ Only in the tails does it extend slightly
beyond the thresholds of this middle response. Although hypothetical, this example
illustrates how a moderate attitude, because it is expected to fall near the center of
the scale, is likely to be more predictable by the OHCM choice model, not less.
Because of this, the variance model of the OHCM, uncontaminated by moderation,
provides a better test of variability.
This leads directly to the test of the variability hypothesis. If ambivalence is
associated with a variable attitude, this will be indicated by a positive, statistically
significant coefficient on the measure of ambivalence in the variance model of the
OHCM. So, in the OHCM, a positive coefficient on ambivalence supports the
standard interpretation and the variability hypothesis.
Identifying Moderation
On the other hand, a negative and significant coefficient on ambivalence would
suggest the alternative interpretation, that ambivalence produces moderation. This is
tested directly by folding an ordinal opinion item at its midpoint, so that higher
values reflect a more extreme opinion and lower values a more moderate one. The
moderation hypothesis anticipates a negative association between ambivalence and
extremity. If the ambivalence hypothesis holds, this will be shown in a negative
coefficient on ambivalence in the extremity model.
Identifying Variability and Moderation at the Same Time
If ambivalence increases both variability and moderation, this will be evident in a
positive coefficient on ambivalence in the variance model of the OHCM and a
negative one in the extremity model of the same opinion item. If this were to occur,
both hypotheses would be supported.
The analysis begins with the test of the effects of ambivalence on attitudes toward
wiretapping as a response to combating terrorism. This involves two datasets. One is
a sample of college students and the other is a representative sample of Ohio
residents. Then, in a replication of Alvarez and Brehm (2002), I look at ambivalence
about racial issues using data from the 1991 Race and Politics survey. Finally, also
replicating these authors (1998, 2002), data from the 1987 Taxpayer Opinion
Survey are used to test the effects of ambivalence about the Internal Revenue
Polit Behav
Opinions Toward Government Wiretapping
The student sample data were collected from 232 undergraduates in the political
psychology lab at a university in the Midwest. The statewide sample is from a
telephone survey of a random sample of 503 Ohio residents.10 In both samples the
wiretapping question was the same. It asked, ‘‘As part of the war on terrorism,
would you favor or oppose allowing the government to listen to people’s telephone
calls without their knowledge?’’ Response options were ‘‘Favor Strongly,’’ ‘‘Favor
Somewhat,’’ ‘‘Neither Favor Nor Oppose,’’ ‘‘Oppose Somewhat,’’ and ‘‘Oppose
The lab study included two measures of ambivalence, ‘‘objective’’ and ‘‘felt.’’
Objective ambivalence is the norm in political science. The measure here is based
on a series of closed-ended questions that tap respondents’ positive and negative
feelings and beliefs about government wiretapping. These items are listed in the
web Appendix. They were combined into a scale using Thompson and her
colleagues’ (1995) ‘‘Griffin’’ formula, which models ambivalence as a function of
the intensity and similarity of the positive and negative considerations.
Positive þ Negative
jPositive Negativej
By this measure, ambivalence increases as the positive and negative considerations associated with the attitude object are numerous or intense (the first term in
the equation) and nearly equally so (the second term). Recent studies that have used
this formula to measure objective ambivalence are many (Armitage and Conner
2000; Citrin and Luks 2005; Craig et al. 2002, 2005a, b; Federico 2005; Glasgow
2008; Greene 2005; Keele and Wolak 2008; Lavine 2004; Lavine et al. 2005;
McGraw and Bartels 2005; McGraw et al. 2003; Meffert et al. 2004; Mutz 2002a,
b), making it the current standard measure of objective ambivalence in political
psychology (Basinger and Lavine 2005). Felt ambivalence is less typical, although
several studies have used it. It is the subjective perception of being pulled in
different directions by conflicting considerations (e.g., Feldman and Zaller 1992;
Hochschild 1981; Holbrook and Krosnick 2005; Martinez et al. 2007; McGraw et al.
2003; Tormala and DeSensi 2008). The present analysis measured felt ambivalence
about wiretapping in response to terrorism using the following question:
Some people feel that there are only good things or bad things about this issue.
Their feelings are consistent. Other people feel that there are both good things
and bad things about this issue. Their feelings are INconsistent. Thinking
about your own views, would you say that your feelings about this issue are
extremely consistent, very consistent, somewhat consistent, somewhat
INconsistent, very INconsistent, or extremely INconsistent?
The student-sample study included this measure and seven other felt ambivalence
items, all of which were agree–disagree questions, and combined them into an
Participants in the lab study were recruited from political science courses. The telephone survey was
conducted by a professional interviewers who work for the Ohio State Center for Survey Research.
Polit Behav
index. The wording of the other seven items is listed in the web Appendix. The
random sample phone survey was limited to this categorical question.
Descriptive Statistics
Table 1 shows descriptive statistics of the wiretapping ambivalence measures for
both samples. It displays the level of ambivalence in each category of the
wiretapping opinion scale. The purpose here is to get a sense of whether or not
respondents highest in ambivalence tend to gravitate toward the middle. Ambivalence is scored on a 0–1 metric. The average level of ambivalence in each response
category is ordered about as one might expect. For the measures of objective and
felt ambivalence in the lab data, those who favor and oppose strongly are the least
ambivalent, while those who favor and oppose somewhat are more ambivalent, and
those who ‘‘neither favor or oppose’’ are the most ambivalent. In the survey data, the
mean level of ambivalence on ‘‘neither favor nor oppose’’ is about the same as that
of ‘‘oppose somewhat.’’ This may be due to the fact that a large majority of
participants chose one of the two ‘‘oppose’’ options or that the measure of
ambivalence in the survey is based on a single item, and may be less reliable than
the subjective ambivalence scale in the lab data. In any case, these preliminary
results, which do not control for information or other factors, suggest that people
higher in ambivalence tend to adopt responses toward the middle of the scale.
Wiretapping Model Specification
Wiretapping HCMs: The dependent variable is responses to the wiretapping opinion
question. Higher values reflect greater support for wiretapping in response to
terrorism. The predictors of the error variance include the measures of objective and
felt ambivalence and, in the lab data, controls for general and domain specific
political knowledge. The phone survey data did not include measures of political
knowledge per se so there I control for education, interest in politics, and how often
the respondent reads a daily newspaper. These have been shown to be good proxies
for political information (Zaller 1992). The predictors in the choice model include
the values of freedom and security, which have been shown to be associated with
Table 1 Mean levels of ambivalence
Objective (Lab data)
Felt (Lab data)
Felt (Survey data)
Favor strongly
.65 (.08)
.42 (.08)
.16 (.03)
Favor somewhat
.69 (.08)
.56 (.07)
.28 (.03)
49 (86)
Neither favor nor oppose
.72 (.16)
.66 (.15)
.36 (.05)
20 (48)
68 (124)
65 (35)
Oppose somewhat
.64 (.09)
.59 (.08)
.37 (.03)
Oppose strongly
.43 (.05)
.39 (.05)
.25 (.02)
30 (201)
.60 (.04)
.51 (.03)
.29 (.01)
232 (494)
Ambivalence coded 0–1. Standard error in parenthesis (1st 3 columns)
Last column in parenthesis is survey data N
Polit Behav
opinions toward wiretapping (Liberman and Chaiken 1991; Tetlock 1986). People
who strongly endorse freedom tend to oppose wiretapping while those who strongly
support security tend to favor it. In addition to these two values, the choice model
includes measures of ideology, party identification, general and domain-specific
political knowledge in the student data and, in the statewide data, education,
interest, and newspaper reading. The models control for a framing experiment that
was designed to test the effects of framing the issue of wiretapping in terms of the
values (freedom and security). The student-sample study included three conditions
and the survey study had two. While I control for the manipulations, their effects
were generally nonsignificant. All predictor variables in this analysis, and in all the
analyses of this article, were coded to range from 0 to 1.11
Wiretapping Extremity Models: The measure of extremity is a three point scale
coded 1=neither favor nor oppose, 2=somewhat favor/oppose, and 3=strongly favor/
oppose. The focal predictors are the measures of ambivalence, with controls for the
framing manipulation, political knowledge in the student sample and, in the
statewide survey, education, interest, and newspaper reading.
Wiretapping Analysis
To address the potential problem with using the BHCM to model ambivalent
attitudes, the results of a binary HCM are compared with those of an ordinal HCM.
As the question is ordinal, the dichotomous item for the BHCM is created by
omitting the ‘‘neither favor nor oppose’’ responses, combining the two ‘‘favor’’
responses into one category, and combining the two ‘‘oppose’’ responses into the
other. Omitting respondents who chose the middle category is problematic here
because these are the respondents who are the most ambivalent.12 Therefore, in
these BHCM’s, one would expect the effects of ambivalence to be weak at best.
Nevertheless, using the BHCM on these data might begin to cast light on whether
ambivalence is associated with variability or moderation. Here, and throughout this
article, in discussing the results of the HCM’s, the focus is on the variance model
and, in particular, the effects of ambivalence on the variance.
The left side of Table 2 presents the results for the lab sample. The first column
shows that in the BHCM the coefficient on objective ambivalence is close to zero
and nonsignificant and the felt ambivalence coefficient is large, positive, and highly
significant. The latter is consistent with either hypothesis—that ambivalence
enhances variability or moderation. Which is it? The results of the ordered
heteroskedastic probit in the second column might help to discern between the these
two possibilities. They show that the coefficients on both types of ambivalence are
statistically significant at p B .01 and negative. This is the opposite of what one
would expect if ambivalence produces variable opinions. But it is consistent with
Attempts to discern other ways that the framing manipulations might have been systematically
significant, such as including them in the variance component of the OHCM, and through interactions,
proved fruitless, both in the OHCM’s and other models.
One could randomly assign these omitted respondents to the ‘‘favor’’ and ‘‘oppose’’ categories, but
since they did not choose these responses, this is the equivalent of adding a proportional amount of
random noise to the data.
.00 (.01)
.00 (.01)
.01 (.01)
-.96* (.43)
-.50 (.45)
-.45 (.43)
Extremity model DV is opinion toward wiretapping folded at midpoint, estimated using ordered logit
** p B .01, * p B .05, two-tailed, standard errors in parentheses
-.04 (.09)
-.04 (.09)
Security frame
Both frame
.08 (.13)
Freedom frame
-.03 (.04)
.28 (.16)
-.02 (.01)
-.16 (.27)
Domain knowledge
.03 (.02)
.08 (.08)
.17 (.13)
.09 (.09)
.63 (.80)
Political knowledge
.02 (.02)
-.03 (.02)
.13 (.12)
.41 (.58)
Party identification
.53 (.36)
-.17 (.15)
Read newspaper
.58 (-.42)
.02 (.02)
-.02 (.02)
Political interest
.33 (.52)
National security
-.46 (.67)
-1.74* (.84)
-.33 (.18)
Choice model (HCM’s only)
-.13 (.40)
.01 (.68)
-.15 (.57)
.17 (.15)
-.72 (.62)
1.62** (.79)
.25 (.22)
-.75* (.31)
-.86* (.39)
-4.76** (1.02)
-3.26** (.82)
Read newspaper
-2.49* (1.24)
Domain knowledge
-2.47** (.60)
-1.19** (.47)
Political interest
-1.00 (1.13)
Political knowledge
-.17 (1.55)
3.27** (1.26)
Felt ambivalence
Objective ambivalence
Error variance model (HCMs only)
Survey data
Lab data
Table 2 Opinions of wiretapping as response to terrorism
.06 (.07)
.04 (.12)
.07 (.09)
.12 (.15)
.17 (.11)
.34* (.15)
.82** (.25)
-.32 (.21)
-.07 (.28)
-.13 (.22)
.19 (.36)
-1.36** (.26)
.01 (.19)
-2.09** (.42)
Polit Behav
Polit Behav
the moderation hypothesis. Instead of making attitudes less predictable, the OHCM
actually fits participants high in ambivalence better than those with low levels of
ambivalence.13 Column 3 tests the moderation supposition directly for this sample.
Consistent with the hypothesis, the coefficients on objective and felt ambivalence
are both negative and statistically significant at p B .01.14
The results on the right side of Table 1 are based on the random sample of
Ohioans. The coefficient on felt ambivalence (the only measure of ambivalence
available in this data set) in the BHCM in column 4 is not significant. However, the
OHCM in column 5 shows that the coefficient on felt ambivalence is significant at
p B .01 and, here again, negative, which again is the opposite of what the variability
hypothesis predicts and consistent with moderation. The last column presents the
results of the extremity model for this sample. The coefficient on felt ambivalence is
again negative and highly significant, supporting the moderation explanation.
Considering that the most ambivalent respondents were omitted in the BHCM’s,
their lack of uniform significance is not surprising. Based on the results of the
OHCM’s and extremity models it seems reasonable to conclude that the analysis of
these two samples is consistent with the moderation hypothesis and lends no support
to the alternative.
Racial Policy Opinions
This is explored further by replicating Alvarez and Brehm’s (2002, see also 1997)
analysis of attitudes toward racial issues. Research in political psychology suggests
that many whites are ambivalent about policies that implicate race. Whites are said
to feel sympathetic toward the plight of African Americans, but perceive blacks as
prone to violating the principle of individualism, which esteems self-reliance, work,
and achievement. These conflicting considerations create ambivalence about racebased policies (Federico 2005; Hass et al. 1991, 1992; Katz and Hass 1988).
In their first study of this topic, Alvarez and Brehm (1997) use a series of
BHCM’s to argue that most whites are not ambivalent about race. They show that
ambivalence is not associated with the error variance, while information is, and
suggest that variability of opinions toward racial issues stems from lack of
information rather than ambivalence. Here I revisit the effects of racial ambivalence
on attitudes toward racially-charged issues. The purpose is to test whether the
conclusions drawn from the wiretapping analysis might also hold for opinions about
In their original article on this topic, Alvarez and Brehm’s models were based on
dichotomous dependent variables and thus BHCM’s (1997). In their subsequent
work (2002) they supplement their BHCM’s with five additional racial policy issues
from the same dataset that are based on ordinal DV’s, and thus OHCM’s. Here I
This does not not necessarily imply that the attitudes of highly-ambivalent respondents are more
predictable than respondents with extreme opinions.
In this analysis, felt ambivalence might be conflated with the extent to which individuals have
considered the issue in the past. In the lab data there is a measure of prior thought about the issue that
allows me to address this. When prior thought is included in the model, its effects are nonsignificant and
the coefficient on felt ambivalence increases rather than decreases.
Polit Behav
replicate these five OHCM’s. An extremity model is also estimated for each of these
five issues.
Model Specification of Racial Opinions
The five items from the 1991 Race and Politics Survey are:
Discrimination. This question included a question-wording experiment. It asked:
‘‘How about laws protecting people, many of whom are ... [Version 1] ... blacks
... [Version 2] ... Asian Americans ... [Version 3] ... women ..., from
discrimination in hiring and promotion? Are you strongly in favor, somewhat
in favor, somewhat opposed, [or] strongly opposed to that kind of law?’’ Like
Alvarez and Brehm, I use a pair of dummy variables to isolate those participants
who responded to version 1 (‘‘blacks’’).15
Housing. ‘‘How do you feel about blacks buying houses in white suburbs? Are
you strongly in favor, somewhat in favor, somewhat opposed, [or] strongly
opposed to that?’’
Suburbs. This item also included a question-wording experiment. It asked: ‘‘And
how do you feel about... [Version 1] ... programs set up by religious and business
groups that... [Version 2] ... government subsidized housing ssto ... [Version 3]
... the government putting its weight behind programs to ... encourage blacks to
buy homes in white suburbs? Are you ... strongly in favor, somewhat in favor,
somewhat opposed [or] strongly opposed to that?’’ Again like Alvarez and
Brehm the different question wordings are controlled here with two indicator
variables to isolate respondents in the ‘‘government subsidized housing’’
Interference. ‘‘The government in Washington tries to do too many things that
should be left up to individuals and private businesses. Do you agree strongly,
agree somewhat, disagree somewhat, [or] disagree strongly?’’
Overboard. ‘‘This country sometimes goes overboard in its efforts to fight
racism these days. Do you agree strongly, agree somewhat, disagree somewhat,
[or] disagree strongly?’’
Racial Opinion OHCMs. In each OHCM, the variance model includes a measure
of ambivalence and controls for general political knowledge, domain-specific
political knowledge, ideology, and financial status. The choice model includes
measures of support for equality and individualism and measures of modern racism,
anti-black stereotypes, authoritarianism, anti-semitism, ideology, financial status,
and, in the ‘‘discrimination’’ and ‘‘suburbs’’ questions, the dummy variables for
question wording. Details on the motivations behind these models, their
Respondents in the Blacks condition were coded Dummy1 = 1 and Dummy2 = 0. Those in the Asian
Americans condition were coded Dummy1 = 0 and Dummy2 = 1. Those in the women condition were
coded Dummy1 = 0 and Dummy2 = 0.
Respondents in condition ‘‘a’’ were coded Dummy1 = 1 and Dummy2 = 0. Those in condition ‘‘b’’
were coded Dummy1 = 0 and Dummy2 = 1. And those in condition ‘‘c’’ were coded Dummy1 = 0 and
Dummy2 = 0.
Polit Behav
specification, and estimation, can be found in the text and Technical Appendix D of
Alvarez and Brehm (2002).17
Racial Opinion Extremity Models. The DV’s are the five race opinion items
folded at their midpoints. Here again, higher values reflect a more extreme attitude
and lower values a more moderate one. Each of the five extremity models included
the measure of ambivalence and controls for equality, individualism, political
knowledge, domain-specific political knowledge, ideology and, where applicable,
the dummy variables for question wording.
Racial Issues Analysis
The variability hypothesis anticipates a positive coefficient on ambivalence in the
OHCM’s while the moderation hypothesis expects a negative one. Consistent with
the latter, and the results of the wiretapping analysis, the results of all five models
in Table 3 show that the coefficient on ambivalence is negative and significant at
p B .01. Testing for moderation directly, in four of the five extremity models the
coefficient on ambivalence is negative and highly significant. Only one is not
statistically significant, and the wording of this one, ‘‘housing,’’ is very similar to
that of another, ‘‘suburbs,’’ which is significant. Overall, the results of this
analysis of racial attitudes are similar to those of wiretapping. Instead of
increasing the variance of attitudes, ambivalence moves them predictably toward
the center.
Opinions of the IRS
In contrast to their earlier studies of ambivalence over abortion (1995) and racial
attitudes (1997), in their subsequent analysis of public opinion toward the IRS,
Alvarez and Brehm (1998, 2002) use ordinal, rather than dichotomous, opinion
items. They find that ambivalence about the IRS reduces the variance of attitudes
toward this bureaucratic agency. They do not consider the possibility that the
variability hypothesis might be wrong. Instead they posit that sometimes
ambivalence increases the variance—the variability hypothesis—and other times
decreases it—a psychological theory they label ‘‘equivocation.’’ As they explain it,
The appendix is available online at The five models were
replicated as faithfully as possible based on the descriptions of them in the book and technical appendix.
Even so, there were some differences between the results presented here and those in the book. They may
have resulted from an ambiguity in the description of Alvarez and Brehm’s equality scale, which was
used to construct the measure of ambivalence between equality and individualism. Their measure of
equality is a scale of three items. However, they only identify one of the three. The question they
identified asked how much people favor or oppose ‘‘more money being spent to reduce unemployment.’’
The two other items that I identified independently as indicators of support for equality, and included in
the scale used here, were taken from a series of questions that asked about the importance of various
‘‘goals for America.’’ These two items were based on responses to the questions that asked about the
importance of ‘‘equality for women’’ and ‘‘equality for Blacks.’’ Among all the other items in the survey,
these two seem the most reasonable and appropriate as measures of support for equality. Another small
ambiguity was that Alvarez and Brehm include in their variance models a measure of ‘‘financial status’’
that is not discussed in the text or tables. For this reason, it is not clear how this variable is measured. In
this replication financial status was measured using a question about family income.
-.07 (.11)
-.18 (.10)
-.11 (.10)
Ideology Financial status
-.48** (.08)
-.38** (.08)
.27 (.08)
-.25* (.13)
.05 (.14)
-.31 (.18)
.37* (.18)
.43** (.11)
-.06 (.12)
.46** (.10)
.30 (.20)
.02 (.05)
.05 (.05)
-.28 (.08)
.35 (.09)
.04 (.12)
.21 (.14)
.05 (.14)
-1.16** (.18)
-.19 (.13)
.40** (.11)
-.06 (.09)
.02 (.09)
-.15 (.10)
.10 (.09)
-.33** (.13)
-.08 (.08)
-.06 (.08)
.00 (.19)
.47** (.19)
.49** (.11)
-.16 (.13)
.25* (.11)
-1.05** (.22)
.03 (.06)
-.06 (.08)
-.33** (.09)
-.02 (.10)
-.10 (.10)
.59** (.10)
.80** (.12)
-.32** (.08)
-.14 (.09)
-.06 (.08)
.05 (.09)
.16 (.08)
-.39** (.12)
1.07** (.18)
.13 (.18)
.24* (.11)
-.20 (.13)
.28** (.11)
-.78** (.20)
-.16** (.05)
-.25** (.06)
-.36** (.09)
.11 (.10)
.24* (.10)
-1.10** (.13)
.18* (.09)
-.36** (.08)
-.20* (.08)
.15 (.08)
-.01 (.09)
-.30** (.08)
-.44** (.10)
.15 (.18)
.93** (.18)
.26** (.10)
-.17 (.13)
-.01 (.11)
-1.34** (.20)
** p B .01, * p B .05, two-tailed, SE’s in parentheses. OHCM’s are ordered heteroskedastic probit. Extremity models are probits of opinion items folded at midpoint.
extremity models only, ‘‘ideology’’ is ideological extremity. Data from 1991 Race and Politics Survey
-.30 (.06)
.27 (.08)
.10 (.06)
Financial status
-.24** (.05)
.35** (.10)
Dummy 1
.22 (.08)
.31** (.11)
Dummy 2
.70** (.00)
-.16 (.11)
-.53** (.11)
-.72 (.12)
.08 (.08)
.22** (.11)
Modern racism
.05 (.18)
.89** (.18)
-.09 (.09)
.02 (.10)
-.03 (.11)
-.25* (.09)
-.34** (.13)
.15 (.10)
.36** (.09)
.17 (.11)
-.11 (.12)
.13 (.11)
-.75** (.21)
Choice model (OHCM only)
-.14 (.09)
-.37** (.13)
Error variance model (OHCM only)
Table 3 Racial Policy Opinions
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Polit Behav
equivocation occurs when a person has considerations that would seem likely to
conflict, but unlike ambivalence, there is no inconsistency within the mind of the
individual. When equivocation happens, they say, the conflicting considerations
actually reinforce the opinion.
The purpose here is to replicate the analysis of attitudes toward the IRS in order
to address the possibility ambivalence about the IRS is no different from
ambivalence about wiretapping or race in the sense that it leads to the expression
of attitudes that are predictably moderate.
Model Specification of IRS Opinions
The data come from the 1987 Taxpayer Opinion Survey, which was sponsored
by the IRS. Alvarez and Brehm (1998) estimate eight models, all of them
OHCM’s. All eight DV’s are based on agree–disagree questions that asked
respondents to place statements on a six-point scale, labeled only at the extremes,
that ran from 1 (‘‘Strongly Disagree’’) to 6 (‘‘Strongly Agree’’). They are worded
as follows.
Accurate. ‘‘You can depend on the IRS to keep accurate tax records.’’
Equitable. ‘‘I am confident that the IRS would never try to take more money
from me than they should.’’
Honest. ‘‘The IRS employees are honest—you could never bribe them.’’
Integrity. ‘‘IRS employees have an unusual amount of honesty and integrity.’’
Knowledgeable. ‘‘IRS Employees [are] extremely knowledgeable about tax
Own. ‘‘When it comes to investigating their own people, the IRS is as thorough
as they are with everyone else,’’
Reasonable. ‘‘IRS procedures and practices are fair and reasonable ones that
respect the rights of taxpayers.’’
Snooping. ‘‘That the IRS automatically withholds some of my income and even
get copies of my W-2 forms and interest statements sometimes makes me feel
they are always nearby and watching.’’
IRS Opinion OHCM’s. The predictors in the variance equation include three
measures of ambivalence about the IRS: responsiveness vs. honesty, fairness vs.
honesty, and fairness vs. responsiveness. They also include controls for education
and domain-specific knowledge about tax laws (which, in their study, they call ‘‘soft
information’’). The choice model includes measures of responsiveness, honesty, and
fairness of the IRS, as well as controls for whether the respondent has initiated
contact with the IRS, been audited, is female, or a racial minority. Details on the
motivation, derivation, and estimation of these replicated models can be found in
Alvarez and Brehm (1998, 2002).
IRS Extremity Models. The DV’s are the eight agree–disagree questions folded at
their midpoints. Here again, higher values reflect more extreme opinions and lower
values, more moderate ones. Predictors include the three measures of ambivalence
and controls for education and soft information.
Polit Behav
IRS Opinions Analysis
There are three measures of ambivalence in each of eight OHCM’s and eight
extremity models. The results are spread across Tables 4 and 5. They are discussed
by each measure of ambivalence, first in the OHCM’s, and then in the extremity
models. The first measure of ambivalence is responsiveness vs. honesty of IRS
agents. Consistent with what we have seen thus far, the coefficient is negative and
highly significant in seven of the eight OHCM’s. The second type of ambivalence,
however, over fairness vs. honesty of IRS employees, is negative and significant in
only one of the eight OHCM’s. In the other seven models it is nonsignificant. One
could speculate that these nonsignificant results could have occurred because these
beliefs do not produce ambivalence, or because ambivalence over fairness vs.
honesty is very similar to that involving responsiveness vs. honesty and the models
are over-specified. The effects of the third measure of ambivalence, fairness vs.
responsiveness, are mixed. The coefficients are negative and significant in four of
the eight models. Overall, although not as consistent as the results of opinions of
wiretapping and race, these OHCM’s suggest lead to a similar conclusion. In every
one of the eight OHCM’s at least one measure of ambivalence decreases the
variance. In four of the eight, at least two measures of ambivalence have this effect.
Given the redundancy of ambivalence measures in each model, the results are about
as robust as one could expect.
Despite the redundancy, the results of the eight extremity models are even
stronger. Ambivalence over responsiveness vs. honesty is associated with moderate
attitudes in six of the eight models. The second type of ambivalence, fairness vs.
honesty, leads to moderation in seven of the eight. The effects of the third are
consistent across all eight, leading in every case to moderate opinions. Overall, 21
of the 24 ambivalence coefficients in these eight models are negative and
statistically significant. Between the OHCM’s and extremity models the results
point to the same conclusion as those of wiretapping and race in the sense that
ambivalence leads to the expression of attitudes that are more predictable because
they are more moderate.
Equivocal or Predictably Moderate?
While consistent with this explanation, the negative coefficients on ambivalence in
the OHCM’s are also consistent with Alvarez and Brehm’s theory of equivocation.
Is it possible to adjudicate between these two explanations? Consider what it would
mean if the equivocation explanation were true. Given the consistency of the results
throughout this article, it would suggest that all these studies, and most studies of
ambivalence, tap equivocation rather than ambivalence. Even if this were true, it
cannot account for the measures of felt ambivalence in the wiretapping analysis,
which also diminish the error variance. Moreover, the measures of objective
ambivalence in question are based on methodological studies which validate
objective ambivalence based on its association with felt ambivalence (e.g.,
Thompson et al. 1995). Lastly, it is difficult, if not impossible, to rule out the
possibility of equivocation here because the only empirical evidence of it is based
-.61 (.32)
-2.19** (.60)
.43 (.29)
-.11** (.04)
-2.95* (.59)
-1.38** (.53)
-1.20** (.60)
Data from 1987 Taxpayer Opinion Survey
Extremity models are ordered probits of opinion items folded at midpoint
.14 (.15)
-.02 (.02)
-.57 (.33)
-.76* (.31)
-.99** (.33)
** p B .01, * p B .05, two-tailed, SE’s in parentheses. OHCM’s are ordered heteroskedastic probit
.01 (.02)
-.01 (.01)
.00 (.01)
.05* (.02)
-.00 (.02)
-.08 (.06)
.08 (.06)
.44** (.17)
.21 (.14)
-.05** (.02)
-.89** (.33)
.02 (.30)
-1.04** (.32)
-.02 (.01)
-.57 (.29)
-.15** (.04)
-1.56** (.57)
-1.91** (.54)
-.03* (.01)
IRS contact
.00 (.01)
.11* (.05)
-.03 (.05)
.29** (.11)
-.07 (.14)
Choice model (OHCM only)
Soft information
-.05** (.02)
-.05 (.28)
-1.46** (.31)
Error variance model (OHCM only)
Table 4 Opinions toward the IRS, part 1
.11 (.29)
-.02 (.04)
-1.31* (.59)
-2.38** (.56)
-1.80** (.61)
.02 (.02)
-.01 (.01)
-.04* (.02)
.00 (.02)
.33** (.13)
.33** (.13)
.42** (.15)
-.10 (.13)
-.05 (.02)
-.40 (.30)
.03 (.29)
-1.17** (.29)
.15 (.31)
-.19* (.04)
-1.48** (.60)
-3.04** (.57)
-1.90** (.61)
Polit Behav
.03* (.02)
.17 (.29)
-.12** (.04)
-3.61** (.60)
-.61 (.56)
-1.37** (.61)
.01 (.02)
.00 (.01)
-.01 (.01)
.02 (.02)
-.01 (.05)
.15 (.07)
.24* (.10)
.28 (.15)
-.04* (.02)
-.71* (.33)
-.29 (.30)
-1.15** (.32)
-.00 (.30)
-.05 (.04)
-2.16** (.61)
-1.89** (.58)
-2.15** (.63)
Data from 1987 Taxpayer Opinion Survey
Extremity models are ordered probits of opinion items folded at midpoint
.03 (.02)
.01 (.01)
-.05* (.02)
-.00 (.01)
-.14 (.07)
.08 (.05)
.51** (.18)
-.09 (.15)
-.05** (.02)
-.50 (.32)
-.37 (.27)
-.98** (.32)
** p B .01, * p B .05, two-tailed, SE’s in parentheses. OHCM’s are ordered heteroskedastic probit
-.00 (.01)
-.02 (.01)
IRS contact
.03 (.04)
-.00 (.01)
.03 (.03)
.23** (.09)
.27 (.15)
Choice model (OHCM only)
Soft information
-1.57* (.32)
-.05** (.02)
-.29 (.27)
-.71** (.33)
Error variance model (OHCM only)
Table 5 Opinions toward the IRS, part 2
.36 (.30)
-.12** (.04
-3.56** (.60)
-1.68** (.56)
-1.13 (.61)
-.00 (.02)
.00 (.01)
-.04 (.02)
-.01 (.02)
.06 (.08)
-.01 (.06)
-.13 (.08)
.20 (.14)
-.07** (.02)
-.75* (.31)
-.55 (.29)
-.12 (.31)
.40 (.28)
-.15** (.04)
-2.07** (.58)
-1.29* (.55)
.50 (.60)
Polit Behav
Polit Behav
on inferences drawn from the error variances of these same IRS models. The most
parsimonious explanation, and the one with consistent empirical support is that
ambivalence does not lead to variability but does move attitudes toward the center
of the opinion scale.
For close to half a century scholars of public opinion have been keenly aware that
survey responses are often plagued by large amounts of what appear to be random
error. The question at hand has been how to explain what Zaller and Feldman (1992,
p. 579) call ‘‘this anomalous error variance’’ that is so prevalent in opinion data.
Perhaps the most prominent explanation at present is that what looks like haphazard
responding often reflects ambivalence, where an individual’s conflicting considerations produce a wider distribution of potential opinions that he or she might give in
response to a survey question. Because ambivalence produces ‘‘multiple and often
conflicting’’ attitudes (Zaller and Feldman 1992, p. 584) even within individuals, it
is said to make opinions variable and, as a result, apparently haphazard.
This study argued that ambivalence tends not to make attitudes variable but,
instead, moderate. It theorized that individuals whose considerations about an
attitude object are inconsistently valenced in effect split the difference between their
opposing feelings and beliefs, and consequently tend to adopt middle-of-the-road
opinions. It showed how the method most widely used to tap variability of
ambivalent opinions, the binary heteroskedastic choice model, is problematic in this
context because it conflates variability and moderation. The ordered heteroskedastic
choice model provided an alternative method of modeling variability without this
problem. Modeling moderation separately, it tested whether ambivalence makes
attitudes variable, moderate, or both. Drawing on four datasets and three different
policy domains, the results showed that ambivalence is not associated with the
expression of variable opinions. Just as consistently, the estimates demonstrated that
ambivalence moves opinions toward a moderate stance on political questions.
Overall, these results have implications for our understanding of questionnaire
design, ambivalence, and nonattitudes. With respect to the design of survey
questions, they suggest that where a choice is not inherently binary, ambivalent
attitudes are better measured using ordinal opinion items because people who are
ambivalent about an issue tend to gravitate toward the center. Regarding
ambivalence, whereas it is clear that it leads to cross-sectional moderation and
not variability, the implications for attitude stability are less so. This analysis dealt
with the cross-sectional variability of individuals’ attitudes. It did not address, and
thus cannot account for, the several studies which suggest that ambivalence is
associated with opinion change or instability in repeated interviews of the same
individuals over time. It is thus not clear how the present study can be reconciled
with those that show that ambivalence and the related concept of value conflict are
associated with actual across-time instability in the expression of candidate
evaluations (Lavine 2001; Meffert et al. 2004), party identification (Keele and
Wolak 2006), and policy attitudes (Craig et al. 2005a; Eagly and Chaiken 1993;
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Zaller and Feldman 1992), or responsiveness to persuasion (Bassili and Fletcher
1991; Holbrook and Krosnick 2005; McGraw 1995; Peffley et al. 2001; Sniderman
et al. 1996).
One possibility is that ambivalence is associated with both cross-sectional
moderation and, in repeated surveys, attitude instability. This would be true if
ambivalent attitudes are unstable but vary over time around a moderate mean.
Responding to the same opinion item at different points, an ambivalent individual
might ‘‘Favor Somewhat’’ an issue at time 1, ‘‘Oppose Somewhat’’ at time 2, and
‘‘Neither Favor Nor Oppose,’’ at time 3. Such an individual would be both moderate
and unstable in his or her attitude toward the issue.
Most significantly, this study has implications for our understanding of
nonattitudes in opinion surveys. Where public opinion researchers today take it as
given that ambivalence is a primary source of the anomalous error variance in
opinion data, the results presented here suggest otherwise. Ambivalent attitudes are
anything but random. They fall predictably between the extremes.
Acknowledgments For helpful comments and suggestions I wish to thank Robert Franzese, Tobin
Grant, Phil Habel, Howie Lavine, Scott McClurg, Kathleen McGraw, Fred Solt, Marco Steenbergen, and
Joe Young. I thank Drew Seib for research assistance.
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