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Polit Behav DOI 10.1007/s11109-012-9199-8 ORIGINAL PAPER 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. Introduction 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] 123 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. 1 In this study ‘‘moderation’’ refers to opinions that tend toward the center of a bipolar attitude scale, not an interaction between variables. 2 The BHCM may be appropriate in other contexts, such as when a choice is inherently binary. 123 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. 123 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 3 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). 123 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 P si wi r¼ P : ð1Þ wi 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. 4 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. 123 Polit Behav To understand how, consider first the general form of the homoskedastic binary response model: Prðyi ¼ 1Þ ¼ gðxi bÞ; ð2Þ 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 observation: xi b Pr ðyi ¼ 1Þ ¼ g ð3Þ r The value of g is arbitrary so long as it is a constant. For ease of estimation, the logit qffiffiffiffi 2 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 ; ð4Þ 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 : ð5Þ 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 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 http://press.princeton.edu/alvarez/. The mechanics of the ordered HCM are discussed in Alvarez and Brehm (1998). 123 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 123 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. 6 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). 7 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.’’ 8 See Achen 2002, p. 445 and Braumoeller 2006, p. 273, for brief theoretical mentions of this interpretation problem. 123 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. 9 See Alvarez and Brehm 1998 and Technical Appendix D of 2002 for a derivation of the OHCM. 123 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. Analysis 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 Service. 123 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 Strongly.’’ 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 ð6Þ 2 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 10 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. 123 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) N 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) Overall .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 123 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 11 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. 12 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. 123 123 232 .00 (.01) .00 (.01) .01 (.01) -.96* (.43) -.50 (.45) 232 -.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 232 -.04 (.09) -.04 (.09) Security frame Both frame N .08 (.13) Freedom frame 410 -.03 (.04) .28 (.16) -.02 (.01) Education -.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) Ideology .02 (.02) -.02 (.02) Political interest .33 (.52) Freedom National security -.46 (.67) -1.74* (.84) -.33 (.18) Choice model (HCM’s only) Education -.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) BHCM Extremity BHCM OHCM Survey data Lab data Table 2 Opinions of wiretapping as response to terrorism 449 .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) OHCM 492 .01 (.19) -2.09** (.42) Extremity 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 race. 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 13 This does not not necessarily imply that the attitudes of highly-ambivalent respondents are more predictable than respondents with extreme opinions. 14 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. 123 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’’ condition.16 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 15 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. 16 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. 123 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, 17 The appendix is available online at http://press.princeton.edu/alvarez/appd.pd. 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. 123 123 -.07 (.11) -.18 (.10) -.11 (.10) Domain knowledge Ideology Financial status 1,865 N 1,760 -.48** (.08) -.38** (.08) 1,860 .27 (.08) -.25* (.13) .05 (.14) 1,755 -.31 (.18) .37* (.18) .43** (.11) -.06 (.12) .46** (.10) .30 (.20) Extremity 1,638 .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) OHCM Suburbs 1,551 -.08 (.08) -.06 (.08) .00 (.19) .47** (.19) .49** (.11) -.16 (.13) .25* (.11) -1.05** (.22) Extremity 1,868 .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) OHCM Interference 1,761 1.07** (.18) .13 (.18) .24* (.11) -.20 (.13) .28** (.11) -.78** (.20) Extremity 1,873 -.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) OHCM Overboard In .15 (.18) .93** (.18) .26** (.10) -.17 (.13) -.01 (.11) -1.34** (.20) Extremity ** 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) Ideology Financial status -.24** (.05) .35** (.10) Anti-semitism Dummy 1 .22 (.08) .31** (.11) Authoritarianism Dummy 2 .70** (.00) -.16 (.11) Anti-black stereotypes -.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) Individualism Equality Choice model (OHCM only) -.14 (.09) -.37** (.13) Political knowledge Ambivalence Error variance model (OHCM only) OHCM OHCM Extremity Housing Discrimination Table 3 Racial Policy Opinions Polit Behav 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 laws’’ 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. 123 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 123 -.61 (.32) Fairness -2.19** (.60) 920 914 997 .43 (.29) -.11** (.04) -2.95* (.59) -1.38** (.53) -1.20** (.60) Extremity Data from 1987 Taxpayer Opinion Survey Extremity models are ordered probits of opinion items folded at midpoint 871 .14 (.15) -.02 (.02) -.57 (.33) -.76* (.31) -.99** (.33) OHCM Honest ** p B .01, * p B .05, two-tailed, SE’s in parentheses. OHCM’s are ordered heteroskedastic probit N .01 (.02) -.01 (.01) .00 (.01) .05* (.02) Female Minority -.00 (.02) -.08 (.06) .08 (.06) .44** (.17) .21 (.14) -.05** (.02) -.89** (.33) .02 (.30) -1.04** (.32) -.02 (.01) 1,006 -.57 (.29) -.15** (.04) -1.56** (.57) -1.91** (.54) -.03* (.01) IRS contact .00 (.01) .11* (.05) -.03 (.05) Honesty Audit .29** (.11) -.07 (.14) Responsiveness Choice model (OHCM only) Soft information -.05** (.02) Fairness/Responsiveness Education -.05 (.28) -1.46** (.31) Fairness/Honest Responsiveness/Honesty Error variance model (OHCM only) OHCM OHCM Extremity Equitable Accurate Table 4 Opinions toward the IRS, part 1 950 .11 (.29) -.02 (.04) -1.31* (.59) -2.38** (.56) -1.80** (.61) Extremity 882 .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) OHCM Integrity 963 .15 (.31) -.19* (.04) -1.48** (.60) -3.04** (.57) -1.90** (.61) Extremity Polit Behav 123 123 .03* (.02) Minority 987 .17 (.29) -.12** (.04) -3.61** (.60) -.61 (.56) -1.37** (.61) 831 .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) 911 -.00 (.30) -.05 (.04) -2.16** (.61) -1.89** (.58) -2.15** (.63) Extremity Data from 1987 Taxpayer Opinion Survey Extremity models are ordered probits of opinion items folded at midpoint 905 .03 (.02) .01 (.01) -.05* (.02) -.00 (.01) -.14 (.07) .08 (.05) .51** (.18) -.09 (.15) -.05** (.02) -.50 (.32) -.37 (.27) -.98** (.32) OHCM Reasonable ** p B .01, * p B .05, two-tailed, SE’s in parentheses. OHCM’s are ordered heteroskedastic probit 904 -.00 (.01) Female N -.02 (.01) IRS contact .03 (.04) -.00 (.01) Fairness Audit .03 (.03) .23** (.09) .27 (.15) Honesty Responsiveness Choice model (OHCM only) Soft information -1.57* (.32) -.05** (.02) Fairness/Responsiveness -.29 (.27) -.71** (.33) Education Fairness/Honest Responsiveness/Honesty Error variance model (OHCM only) OHCM OHCM Extremity Own Knowledgeable Table 5 Opinions toward the IRS, part 2 990 .36 (.30) -.12** (.04 -3.56** (.60) -1.68** (.56) -1.13 (.61) Extremity 913 -.00 (.02) .00 (.01) -.04 (.02) -.01 (.02) .06 (.08) -.01 (.06) -.13 (.08) .20 (.14) -.07** (.02) -.75* (.31) -.55 (.29) -.12 (.31) OHCM Snooping 1,000 .40 (.28) -.15** (.04) -2.07** (.58) -1.29* (.55) .50 (.60) Extremity 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. Discussion 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; 123 Polit Behav 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. 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