AMBIVALENT SOCIAL NETWORKS AND THEIR CONSEQUENCES ...

International Journal of Public Opinion Research Vol. 17 No. 4 © The Author 2005. Published by Oxford University Press on behalf of The World Association for Public Opinion Research. All rights reserved. doi:10.1093/ijpor/edh069 Advanced Access Published on 17 February 2005

AMBIVALENT SOCIAL NETWORKS AND THEIR CONSEQUENCES FOR PARTICIPATION Lilach Nir A B ST RA C T Does perceived disagreement in political discussion help or hinder citizens’ political participation? Some argue that disagreement prompts reflection, perspective-taking, and tolerance. Challengers argue that disagreement fosters ambivalence and hinders participatory activities and turnout. One seminal study that tackled this dilemma formulated the ‘cross-pressures’ hypothesis (Lazarsfeld, Berelson, & Gaudet, 1944/1968), which posited that the more individuals are betwixt and between conflicting social positions, the longer the time for their vote intention to crystallize (and the lower the likelihood they would vote). This paper offers a critique and refinement of the cross-pressures hypothesis. First, previous studies confounded intra-individual and structural sources of cross-pressures. Second, past operationalizations of exposure to disagreement focused on the sheer amount of opposition to the individual’s point of view, rather than his or her exposure to two conflicting points of view. A new measure—network ambivalence—is proposed to capture the latter dynamic. Conceptual and methodological refinements of the cross-pressures hypothesis are tested on a representative sample of voting-age respondents in the United States, interviewed on the American National Election Study 2000 panel (N = 1,555). Results suggest that not only were these pressures hardly detrimental to participation, but they also facilitated the formation of considered electoral preferences.

The simultaneous presence of multiple perspectives is the defining characteristic of public life. Life in public, from the ancient Greek polis to modern-day democracies, derives its significance ‘from the fact that everybody sees and hears from a different position’ (Arendt, 1958, p. 57). The two facets of publicness—visibility and common access—define public life in its effect on collective choices in politics. Common access warrants that collectively-binding choices in a polity will be The author would like to thank Vincent Price, Elihu Katz, Diana Mutz, Yariv Tsfati, Marci McCoy-Roth, Dan Miodownik, IJPOR anonymous reviewers and the editor for valuable comments on earlier drafts of this article. Previous versions of this study were presented at the WAPOR 2003 conference (awarded the Naomi C. Turner prize), and at the ICA 2004 conference (awarded top student paper in political communication). The article was first submitted to IJPOR November 14, 2003. The final version was received October 5, 2004.

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shaped by inputs from multiple sources and will lead to reasoned compromises rather than coercion of a single view (Gutmann & Thompson, 1996, 2004). Visibility of different positions depicts the contours of public debate over these collective choices. Democratic decision making requires discussion of ‘political alternatives in a genuine effort to clarify and refine public policy’ (Berelson, 1952, p. 323, emphasis added). Plurality of alternative viewpoints ‘is the meaning of public life, compared to which even the richest and most satisfying family life can offer only the prolongation and multiplication of one’s own position, with its attending aspects and perspectives’ (Arendt, 1958, p. 57). Are visibility and common access complementary or contradictory normative ideals? If visibility (awareness of multiple viewpoints) comes at the expense of access (decreased participation), what are the implications for democratic life? This dilemma is central to the pioneering research endeavor of The People’s Choice (Lazarsfeld et al., 1968/1944) in which a panel of prospective voters in Erie County, Ohio was interviewed during the 1940 U.S. presidential election year. Lazarsfeld and his colleagues proposed that the clash of opposite viewpoints in one’s environment delay voting decision-time and depress turnout. The clash of oppositions individuals experienced was termed cross-pressures, defined as ‘the conflicts and inconsistencies among the factors which influence vote decision. Some of these factors in the environment of the voter may influence him toward the Republicans while others may operate in favor of the Democrats. In other words, cross-pressures upon the voter drive him in opposite directions’ (Lazarsfeld et al., 1968/1944, p. 53). The cross-pressures hypothesis stated that ‘the more evenly balanced these opposing pressures were, the longer the voter delayed in making up his mind’ (p. 56). Results that Lazarsfeld and others report show an association between the number of cross-pressures experienced and lateness of vote decision. Respondents who were cross-pressured formed their vote preference closer to Election Day. The oft-cited case of the 1940s white-collar Catholic voter, torn between conflicting vote-choices, based on his socioeconomic status (proRepublican) or his religious affiliation (pro-Democrat) served for decades as the prime exemplar of cross-pressures. Subsequent studies, however, did not produce conclusive empirical evidence as to the detrimental effects of cross-pressures on political participation. On the one hand, research suggests they deter participation: People who perceived disagreement within their political discussion networks (‘crosscutting networks,’ Mutz, 2002a) delayed their vote decision and were more reticent to participate than those who perceived agreement (Mutz, 2002b). Crosscutting networks were hypothesized to affect negatively participatory activities and voting, and to delay vote decision time, because people in crosscutting networks have multiple allegiances to different constituencies. On the other hand, considerable evidence suggests that cross-pressures have either no effect or even a positive effect on participatory outcomes. For example,

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a study of cross-pressures’ effects on turnout, which analyzed data from the Elmira, NY 1948 study and the 1956 U.S. presidential election, found no effect of cross-pressures on voting (Horan, 1971, analyzing Berelson, Lazarsfeld, & McPhee, 1954). Recent studies of the U.S. electorate similarly conclude that perceived disagreement within discussion networks does not appear to depress turnout (Huckfeldt, Johnson, & Sprague, 2004, p. 201; Huckfeldt, Mendez, & Osborn, 2004). Moreover, several studies evidence the positive effect of heterogeneous discussion networks on various forms of participation (McLeod et al., 1999; Moy, 1998; Scheufele, Nisbet, & Brossard, 2003; Scheufele, Nisbet, Brossard, & Nisbet, 2004). How might we reconcile these conflicting findings on the effects of crosspressures? In this study, I propose an explanation that integrates conflicting results by a refinement of the original cross-pressures hypothesis. First, I posit that the operational definition of cross-pressures in The people’s choice confounded internal and external sources of conflict. In other words, it is unclear from the study whether internal conflict or external cross-pressures hindered participation. Second, I posit that the operational definition of crosscutting networks captures isolation within one’s opinion environment rather than cross-pressures per se (the balance of exposure to two conflicting points of view). In the next sections, I elaborate on these points and state the study hypotheses.

C O N CE PT U A L I Z I N G C RO SS - P R E S SU R E S EXTERNAL VS. INTERNAL SOURCES OF CROSS-PRESSURES An important internal source of cross-pressures in The People’s Choice was attitudinal ambivalence toward the candidates and parties. For example, one indicator of cross-pressures was an inconsistency in vote choice between the 1936 and 1940 elections (p. 57). There is reason to suspect that the results Lazarsfeld and colleagues obtained, which demonstrated the effects of cross-pressures on decision time, were owed mostly to the individual-level ambivalence components in their cross-pressures index, rather than to its supra-individual components. Ambivalence is defined as the ‘individual’s endorsement of competing considerations relevant to evaluating an attitude object’ (Lavine, 2001, p. 915). It is distinguished from neutrality or indifference because its structural complexity represents ‘the problem of reconciling strongly held but conflicting principles and considerations simultaneously present in the political culture . . . rather than endorsing one side of a political debate and refuting the other, individuals often embrace central elements of both sides’ (p. 915; Alvarez & Brehm, 2002; Feldman & Zaller, 1992; Zaller, 1992). Evidence from past research suggests that attitudinal ambivalence was a consistent predictor of less crystallized vote preferences, whereas pressures stemming from discrepant social positions hardly


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affected voting. For example, ambivalence was a significant predictor of lateness of vote decision (Mutz, 2002b), and of more accurate, but less certain, judgments about candidates (Guge, 1999). While the conceptual definition of cross-pressures in The People’s Choice stressed the external component of pressures in the voter’s environment, the combined cross-pressures index captured intra-individual sources of inconsistency. This is more than a methodological point of contention. If the argument states that external cross-pressures hinder electoral decision-making and participation, but the evidence rests on both internal and external pressures that delay the decision, it is difficult to disentangle the effects of one source from another. A combined index of the two levels might have concealed the true contribution of individual-level ambivalence. To tap external cross-pressures, in contrast (‘sociological’ ambivalence, Merton & Barber, 1976), I propose a new measure of social network ambivalence. Network ambivalence is defined as the balance of competing considerations perceived by the individual within his or her social network. At one extreme, the network is homogeneously one-sided, while at the other extreme it is evenly divided between two candidate preferences. The measure resembles one of the indicators of ‘network heterogeneity’ (the latter takes into account various sources of heterogeneity such as gender, age, and political knowledge differences, in addition to ideological ones; see e.g., McLeod et al., 1999). Network ambivalence differs from previous operationalizations of cross-pressures, which confounded intraindividual (e.g. 1936 and 1940 vote mismatch) and extra-individual (e.g. religion and social class mismatch) sources of ambivalence. Based on the discussion, we would expect the following relationships between ambivalence, lateness of voting decision, participation, and voting: H1:

Individual-level ambivalence is inversely associated with participation and voting.

H2:

Individual-level ambivalence is positively associated with longer decisiontime.

H3:

Individual-level ambivalence has a stronger effect on participatory outcomes, compared to network-level ambivalence.

EXTERNAL C ROSS -P RESSURES : ISOLATION OR R ESISTANCE? What supportive evidence the cross-pressures hypothesis has received in recent years has come from studies that re-conceptualized cross-pressures as ‘crosscutting social networks’, denoting a person’s self-reported exposure to disagreement with his or her own point of view in his or her discussion networks. Crosscutting networks were measured as a 5-item cumulative index, which assessed the extent

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of perceived disagreement with up to three discussants on choice of candidate for president, party affiliation, and three additional items probing the perceived difference of opinion generally (Mutz, 2002b). Mutz hypothesized that crosscutting would be inversely associated with participatory activities and voting, and positively associated with a longer vote decision time. Results based on analyses of two representative survey samples support the hypotheses. Does cumulative disagreement adequately capture cross-pressures, however? There is a critical difference between encountering an environment that opposes the individual’s position, and encountering an environment that is split between supporters and opponents of the individual’s position. Measuring the sheer volume of perceived disagreement captures the first case, while the second case, two opposing factions within the network, is captured by network-level ambivalence. By definition, membership in an ambivalent network implies that a person perceives as many supporters as opponents to his or her position, a circumstance quite different from perception of a unanimous opposition to one’s own point of view.1 It is important, therefore, to recognize that the hypotheses I offer complement—rather than contradict—studies that modeled perceived disagreement as a predictor of lower participation. We have little reason to suppose that non-ambivalent individuals would become less committed to their position, when exposed to diverse information within a heterogeneous network. After all, these individuals have as many supporters as opponents to their position within their network. A person who is in the ‘habit of correcting and completing his own opinion by collating it with those of others . . . [is] . . . cognizant of all that can . . . be said against him, and . . . [knows] that he has sought for objections . . . instead of avoiding them’ (Mill, 1982/1859, p. 25) is likely to become more committed to his position. Indeed, studies suggest that individuals who were cross-pressured were likely to become more ideologically committed, not less, to one or other side of the political spectrum, as a consequence (Knoke, 1990, p. 45). Non-ambivalent individuals who are members of heterogeneous networks become more committed to their vote preference arguably because they have been ‘inoculated’ by a dose of counter-attitudinal information, as previous research on resistance to persuasion suggests (e.g. McGuire, 1962, 1964; Pfau & 1 A person who perceives he or she is isolated in a minority position within a network of discussants would be less assured of his position, less likely to form a stable preference, and less likely to participate. Previous research in the spiral of silence tradition (Noelle-Neumann, 1974, 1993) suggests that perceived opposition is associated with a lower rate of expressing one’s opinion (Glynn, Hayes, & Shanahan, 1997; for a recent review, see Scheufele & Moy, 2000). Note, however, the spiral of silence model differs in important ways from the current study (i.e. here candidates and parties rather than morally loaded issues are examined, and the outcomes are neither opinion change nor speaking up). The less one expresses one’s opinion, the less one is reflective and aware of reasons for one’s point of view, the less stable and constrained one’s preferences and the less predictive of participation. We would expect, therefore, that the more the information environment conveys opposition to one’s opinion, the less he or she is likely to engage in participatory activity.


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Burgoon, 1988; Pfau et al., 1997; Zaller, 1992). Anticipating interactions with those who hold opposite points of view, perhaps these individuals mentally rehearse justifications for holding their own—and an opposing—point of view (‘argument repertoire,’ Cappella, Price, & Nir, 2002; Price, Cappella, & Nir, 2002). In contrast to non-ambivalent individuals, ambivalent individuals would be receptive to information that supports either electoral preference within their networks. Thus—for ambivalent individuals only—the less ambivalent the network, the less time it takes their voting preference to crystallize. The more ambivalent the network, however, the longer the time it takes for their preference to crystallize. H4:

The effect of network ambivalence on decision time is conditional: it delays decision time for ambivalent individuals, but precipitates decision time for non-ambivalent individuals. METHOD

SAMPLE AND RESPONSE RATES This study uses publicly available panel data of a nationally representative sample of U.S. adults, who were surveyed during the course of an election campaign on numerous items pertaining to their political attitudes, beliefs, and behaviors. The 2000 American National Election Study (ANES) two-wave panel featured interviews with 1,807 respondents, drawn as a multistage representative crosssection sample of U.S. citizens of voting age, living in private households. The samples are representative of the four major regions (Northeast, North Central, South, and West) of the coterminous United States as defined by the Census Bureau (Burns, Kinder, Rosenstone, Sapiro, and the National Election Studies, 2002). The 2000 elections featured campaigns for office of leading presidential hopefuls Al Gore, Vice President of the United States, and George W. Bush, Governor of Texas. Respondents were randomly assigned to be interviewed either face-to-face or by telephone. Pre-election interviews were conducted between September 2, 2000, and November 6, 2000. Response rates were calculated by dividing the number of completed interviews by the total number of potential respondents, corresponding to AAPOR’s response rate RR1 (AAPOR, 2004) and reported by the ANES investigators (Burns et al., 2002, p. 27). The response rate for the face-to-face mode was 64.3 percent (n = 1,000) and for the telephone mode 56.5 percent (n = 803). The post-election wave included 1,555 of the pre-election respondents who were reinterviewed between November 8, 2000 and December 21, 2000. The response rate for the face-to-face mode was 86 percent (n = 694) and for the telephone mode 85.9 percent (n = 862). The analyses reported in this paper pertain, for the most part, to the respondents who participated in both waves (N = 1,555).

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MEASURES Individual-level ambivalence. The individual-level measures are modifications of the Griffin ambivalence index (Thompson, Zanna, & Griffin, 1995), which was also used by Lavine (2001), Mutz (2002b), Breckler (1994), Huckfeldt, Johnson, and Sprague (2004), and Huckfeldt, Mendez, and Osborn (2004). The first ambivalence measure was constructed by computing respondents’ combined thermometer ratings of the leading candidates, Bush (B) and Gore (G), on the preelection survey wave. Thermometer rating scores ranged from zero to a 100, with a ‘100’ indicating the most favorable rating and zero the least favorable. Each individual’s ambivalence score was computed by taking into account two attitudinal elements: (a) the average intensity of the two ratings, and (b) the disparity between them. In other words, the survey response a person gave articulated both the intensity and the polarity of his or her attitude toward the candidates. The theoretical range of these ambivalence scores was from −50 = least ambivalent, to +100 = most ambivalent respondents. For example, a person who rated Gore = ‘100’ and Bush = ‘0’, had the lowest ambivalence score of −50 by the computation in Equation 1. A person, who hypothetically rated both candidates ‘80’, in contrast, received an ambivalence score of +80. Equation 1. Ambivalence individual = (G + B) /2 − | G − B | Three additional measures of individual-level ambivalence were computed in the same way. The second was computed with the respondents’ post-election survey thermometer ratings of the candidates. The third was based on respondents’ preelection thermometer ratings of the Democrat and Republican parties. Lastly, the fourth individual-level ambivalence measure captured the balance of coded open-ended likes and dislikes toward the two leading candidates for office (up to five likes and five dislikes for each candidate). For example, a person who expressed only favorable statements about Bush and only unfavorable statements about Gore received a lower ambivalence score than a person who expressed a mix of likes and dislikes toward both candidates. Network size. ANES respondents were asked to name up to four people with whom they discussed elections and politics regularly. The size of the main respondent’s political discussion network was computed by adding the number of individuals with whom the respondent self-reported as having discussed politics. Network size ranged between ‘0’ and ‘4’, with more than 55 percent of the respondents having discussed politics with two or more persons, about 19 percent with only one person, and 25.7 percent with none (N = 1,551; Mdn = 2; M = 1.86; SD = 1.48). Perceived network-level ambivalence. The network-level measure was constructed by computing the balance of opinion distributions in the respondents’ political discussion network, using a method directly analogous to the individual-level Griffin


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index. On the matter of choice of candidate for office, respondents were asked to name their preference for a presidential candidate. In addition, the battery on network properties included an item about the main respondent’s perception of each and every discussant’s vote choice for president. I computed four dummy variables, whose values—computed by a combination of the main respondent vote preference and up to four of their discussion partners’ vote preferences—indicated whether the choice was similar (= 0), or different (= 1). Of the main respondents who had at least one person they discussed politics with on a regular basis (N = 1,152), 56 percent agreed with the first person they named, 40 percent with the second person, 25.5 percent with the third, and only 14.4 percent with the fourth person in their network. Two summative scales, agreement and disagreement (α = .65 and .55, respectively; N = 1,551) were computed. The balance of agreement (A) and disagreement (D) with the main discussant, capturing the perceived ‘cross-pressures’, was computed as shown in Equation 2. Equation 2. Ambivalence network = (A + D) /2 − | A − D | The theoretical range of network-level ambivalence was from −2 to +2, with the higher scores indicating greater ambivalence within the network or stronger cross-pressures. For example, a person who named four people and perceived all to agree with her on the preferred candidate, would have a score of −2 or the least bit of cross-pressure within her political discussion network. In contrast, a person who perceives that in his discussion network of four, two agree with him and two disagree will receive a network ambivalence score of +2. Note that this measure is not identical to disagreement; it emphasizes not the sheer volume of counterattitudinal information, but the mix of pro- and counter-attitudinal information, and so arguably captures empirically cross-pressures (M = −0.30; SD = 0.79; N = 1,551). Participation index. The post-election wave included standard ANES dichotomous participation items, prefaced by ‘We would like to find out about some of the things people do to help a party or a candidate win an election. During the campaign, did you talk to any people and try to show them why they should vote for or against one of the parties or candidates?’ (35 percent replied yes). ‘Did you wear a campaign button, put a campaign sticker on your car, or place a sign in your window or in front of your house?’ (10 percent replied they did). ‘Did you go to any political meetings, rallies, speeches, dinners, or things like that in support of a particular candidate?’ (5.5 percent yes). ‘Did you do any (other) work for one of the parties or candidates?’ (3 percent yes). In addition, two items inquired about campaign contributions, one for an individual candidate (6.6 percent contributed) and the other for a political party (6.4 percent contributed). Affirmative responses were summed to form an index of political participation for further analyses (Cronbach’s α = .63; N = 1,551; M = .70; SD = .99).

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Voting. Respondents were asked on the post-election wave whether they voted for president in the 2000 election. Of the 1,554 who responded, 76 percent were ‘sure I voted’, 11.9 percent did not vote, and about 12 percent either thought about voting or usually vote, but ‘not this time’. The item was dichotomized for further analyses (1 = voted, 0 = did not vote). Timing of vote decision. In addition to their vote choice, respondents were asked on the post-election wave to assess the time span it took them to reach the decision, in an open-ended item. Every person who voted was asked, ‘How long before the election did you decide that you were going to vote the way you did?’ An additional probe interviewers used: ‘Would that have been a few days before the election, a week, or longer than that?’ Open-ended responses were coded into categories, ranging from ‘knew all along’ (12.7 percent) to ‘on election day’ (4.9 percent). Responses were recoded into a 5-point ordinal scale whose anchors were early (=1) to late (=5) decision time for further analyses. Controls. Additional variables were included in the analyses as controls, in order to partial out the unique contribution of ambivalence, in regressions which modeled three different participatory outcomes: voting, participation, and timing of vote decision. The controls were demographics, political knowledge and involvement, and media exposure. Respondents’ education (10 percent less than high school, 28.8 percent high school graduates, 30 percent with some post high-school education, 20.7 percent with a Bachelor’s degree, and about 10 percent with an advanced degree), income (Mdn = $25,000 − $34,999), age (M = 47.2; SD = 17), racial group self-description (77 percent White, 11.6 percent Black, 5.2 percent Hispanic), and gender (43.7 percent men) were included as demographic controls. Political involvement controls included strength of partisanship, knowledge, and interest in the campaign. Partisanship, computed from the seven-point party scale in the original dataset, ranged from 0 (= apolitical, nonpartisan, or independent) to 3 (= strong Democrat or strong Republican). Political knowledge, asked on the pre-election panel wave, was summed across eight knowledge items pertaining to the presidential and vice-presidential candidates in 2000, as well as name-office recognition of the United States Attorney General, British Prime Minister, Chief Justice of the U.S. Supreme Court, and the U.S. Senate Majority leader (Cronbach’s α = .76; N = 1,807; M = 2.70; SD = 2.14). Political interest was a combined 4-point index of two items (r = .47, p < .001) inquiring about selfreported level of attention to the campaign and caring about the presidential elections (12.4 percent of respondents reported lowest interest, and 28 percent highest interest). Four items on mass media exposure and attention were included as controls as well. Respondents of the pre-election survey were asked about the number of days in the past week they read the newspaper (N = 1,807; M = 3.4; SD = 2.92), and viewed the national nightly news, early and late local news. Their responses were combined to an additive scale of television news exposure (Cronbach’s

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α = .68; N = 1,803; M = 9.1; SD = 6.56). An index of television news attention, computed from the combined scores of national and local campaign news attention (r = .57, p < .001) ranged from ‘0’ = low, to ‘5’ = high attention (M = 2.6; SD = 1.45; N = 1,807). A single item, attention to the campaign news in newspaper reports, which ranged from 0 to 5 = high, was included in the analyses as well (M = 1.5; SD = 1.8). R E S UL T S AMBIVALENCE: RELIABILITY AND VALIDITY A measure of individual-level ambivalence (based on thermometer ratings of the leading candidates) exhibited consistency across time and converged, as one would hope when performing tests of its validity, with three additional alternative measures of ambivalence. As Table 1 shows, the test-retest measures of ambivalence at time 1, the pre-election wave, and at time 2, the post-election wave, were moderately strongly correlated. In other words, individuals who were ambivalent toward Gore and Bush on the first survey wave were likely to be ambivalent toward them on the second wave as well.

TABLE 1 Bivariate correlation matrix: Indicators of ambivalence Indicators (1) (1) Gore-Bush thermometers ambivalence score, pre-election wave (2) Gore-Bush thermometers ambivalence score, post-election wave (3) Democrat-Republican parties thermometers ambivalence score, pre-election wave (4) Open-ended likes-dislikes for candidates ambivalence score, pre-election wave (5) Network-level ambivalence in perceived candidate choice, post-election wave

(2)

(3)

(4)

— .57 (1,489)

—

.61 (1,700)

.49 (1,478)

—

.38 (1,747)

.34 (1,528)

.31 (1,731)

—

.11 (1,503)

.10 (1,524)

.08 (1,491)

.11 (1,551)

Note: Data from ANES 2000 pre- and post-election survey panel. Entries are Pearson’s rs, Ns in parentheses. All correlations are significant at the p < .001 level.

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Ambivalence toward the candidates, on the pre-election wave, was also highly correlated with ambivalence toward the Democratic and Republican parties, measured by thermometer ratings of the parties. The more a person was conflicted between his or her ratings of the candidates, the more he or she was conflicted with rating the major parties. Finally, the balance of open-ended likes and dislikes for the candidates (on the pre-election wave) was correlated with an individual’s thermometer-rating ambivalence toward them, albeit not as strongly. The more even-handed the number of reasons in favor and against both candidates, the higher the ambivalence score. Perhaps the lower correlation suggests that the ‘likes and dislikes’ measure taps additional conceptual domains that are unique and not shared by the thermometer-rating ambivalence measures (such as even-handedness, reflection, or being cognizant of rationales for one’s own and opposing points of view). Nevertheless, the correlations suggest a considerable conceptual overlap as well between the thermometer-rating ambivalence and the coded open-ended likes and dislikes ambivalence. The network-level ambivalence, or perceived cross-pressures in one’s network of discussants on matters of vote choice, showed the lowest correlations with individual-level indicators of ambivalence (Table 1, Indicator 5). The finding suggests that the measure is not redundant with individual-level ambivalence, and that it may not even be predictive of it, once additional controls are applied. In fact, a partial correlation matrix of the five measures, controlling for the respondents’ education, income, gender, race, knowledge, interest, and partisanship (not shown), reduces the correlations of network ambivalence with the other constructs to partial correlation pr’s in the .02–.05 range, and the p-value of these correlations does not achieve the conventional level of statistical significance. The partial correlations between the remaining four indicators of individuallevel ambivalence, meanwhile, remain moderately strong and highly significant (p < .001), even after applying these controls. A factor analysis of the five items—four individual-level measures and one network-level measure—similarly suggests the former four and the latter one tap two distinctive conceptual domains. Standardized scores of all five items were entered into a factor analysis (Varimax rotation). Although a single factor emerged (47.8 percent variance explained), the communality of network ambivalence with the underlying factor was only .04, compared to much higher communalities of .63–.72 of most individual-level items. What’s more, a reliability analysis of the five-items scale revealed network-ambivalence had the lowest item-total correlation (r = .12) compared to the other four items (average r = .55). For both substantive and empirical reasons, therefore, the network-level measure proved distinctive from individual-level ambivalence. The latter was scaled from four items, forming a scale of individual-level ambivalence for further analyses (59 percent of variance explained, Cronbach’s α = .76; M = 0; SD = 2.93).

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EFFECTS OF A MBIVALENCE ON PARTICIPATION Having examined the measurement properties of ambivalence, it remains to be tested whether the variance in participatory outcomes is explained by either one or both individual-level and network-level ambivalence measures. Regression models presented in Table 2, Column 2 suggest the following points. First, the TABLE 2 Effects of network- and individual-level ambivalence on participation and voting for president Effects on participation (OLS regression)

Intercept Ambivalence Individual–level Network–level Network size Demographics Income Education Gender (male) Race (white) Age Political involvement Strength of party identification Political knowledge Political interest Participation Mass media use Television news exposure Newspaper exposure Television news attention Newspaper attention R2 Nagelkerke R2 Cox & Snell R2 -2 Log-likelihood Omnibus Model Chi2, significance N cases

Effects on voting (Logistic Regression) N(1 = voted) = 1,034

β

B

SE

Exp(B)

n.a.

−3.58***

.40

.03

−.15*** .00 .15***

−.04 −.07 .22***

.03 .12 .06

.96 .94 1.24

.08** .00 .02 .01 −.01

.04 .26*** −.22 −.09 .02**

.04 .06 .17 .19 .01

1.04 1.29 .80 .91 1.02

.01

.29***

.08

1.33

.07* .16*** n.a.

.24*** .28*** .16

.06 .05 .12

1.27 1.32 1.17

−.06 .02 .06 .10** .21 n.a. n.a. n.a. n.a. 1,374

−.03# .03 .16# .03

.02 .03 .09 .06 n.a. .40 .27 1103.946 433.613***

.97 1.03 1.17 1.03

1,374

Note: β : standardized regression coefficient in OLS regression; B: coefficient log-odds, SE: standard errors, EXP(B): exponentiated log-odds in logistic regression. #p < .1; *p < .05; **p < .01; ***p < .001 Data source: ANES 2000

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participatory activities index was associated most strongly with political involvement and demographics. Political interest was the single-best predictor of participatory activities, followed by newspaper attention, income, and political knowledge. Of particular significance was the extent to which the size of the political discussion network predicted participation. People with larger networks reported on average more participation. Second, intra-individual ambivalence was inversely associated with participatory activities. The more ambivalent a person was, the smaller the likelihood that he or she took part in a participatory activity such as going to rallies, working for a party, or trying to persuade someone to vote for a certain candidate. Third, and most importantly, perceptions of network-level ambivalence, as the indicator of cross-pressures proposed here, failed to predict participatory activities—in line with the expectations of this study (H3). Results presented in Table 2, Column 2, suggest that cross-pressures, operationalized exclusively at the social network-level, did not contribute to the explanation of participatory outcomes, once the variance in these outcomes was accounted for by a host of controls. Follow-up analyses (not shown), with individual-level ambivalence items entered as separate variables rather than a combined scale, produced a similar inverse association with participation, whereas the network-level ambivalence was not a significant predictor of participation. In other words, regardless of whether individual ambivalence was a scale or a single item, it still predicted participation better than network-level ambivalence. E FFECTS OF A MBIVALENCE ON VOTING The vote for a presidential candidate was associated most strongly with political involvement and demographics. Turnout in the presidential elections (Table 2, Columns 3–5) was best predicted by education, age, and the political involvement indicators: strength of party identification, political interest, and political knowledge. Once more, the size of the political discussion network predicted turnout. People with larger networks reported on average that they were more likely to vote. Neither individual- nor network-level ambivalence, however, predicted turnout. In other words, ambivalent respondents were no more and no less likely than non-ambivalent respondents to cast their vote in the presidential elections. In addition, the results suggest that cross-pressures at the social network level had no significant effect at all on turnout. EFFECTS OF A MBIVALENCE ON VOTE DECISION T IMING Previous research has demonstrated with the most empirical confidence the effects of cross-pressures and crosscutting networks on delaying vote decision time. Results of the analyses presented in Table 3 (Model 1), however, suggest that the

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TABLE 3 Effects of network- and individual-level ambivalence on timing of vote decision (OLS) Effects on timing of vote decision (β)

Ambivalence Individual–level Network–level Interaction (network ambivalence × individual ambivalence) Network size Demographics Income Education Gender (male) Race (white) Age Political involvement Strength of party identification Political knowledge Political interest Participation Mass media use Television news exposure Newspaper exposure Television news attention Newspaper attention R2 N

Model 1

Model 2

.36*** .01 n.a.

.37*** .02 .06*

−.06#

−.06*

−.03 −.01 .02 .04 −.04

−.03 −.01 .02 .04 −.04

−.17*** −.03 −.03 −.02

−.17*** −.03 −.03 −.02

−.05 −.01 .04 .01 .23 1,019

−.04 −.01 .03 .02 .24 1,019

Note: Higher values of the dependent variable indicate later decision time. Table entries are standardized OLS regression coefficients. #p < .1; *p < 0.5; *** p < .001 Data source: ANES 2000

respondents’ internal ambivalence, as measured by their combined thermometer ratings—and not cross-pressures at the network level—was the single best predictor of longer decision time. Cross-pressures, in contrast, were not significantly associated with decision time, once the individual-level source of variance was accounted for by the intra-individual ambivalence measures. The size of a person’s discussion network, on the other hand, was marginally associated with decision time. People who reported having larger networks, in which they discussed politics regularly, were likely to form their decision early compared to those who had smaller networks. Of several controls applied in the model, only the strength of party identification was significantly associated with decision-time, in the

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expected inverse direction. The stronger the conviction with which a person held his or her party affiliation, the earlier he or she formed their decision. None of the demographics or the media use variables associated with lateness of decision.2 So far, we have seen stronger evidence in favor of individual-level ambivalence than we did for extra-individual cross-pressures in their effect on decision time. Is the absence of an association between network-ambivalence and time of decision merely reflecting the previously noted theoretically plausible but empirically weak relationship between them, or is it concealing a more interesting differential pattern of effect on decision time? Model 2 in Table 3 (right-hand side, with interaction term) suggests the latter. Compared to the first model (with no interaction), the coefficients of the significant predictors of vote decision time in the second model (ambivalence, network size, and strength of partisanship) remained virtually the same in magnitude, network size achieving the conventional cut-off point for statistical significance. The additional interaction term, between cross-pressures and ambivalence, was significantly and positively associated with decision time. The interaction pattern was plotted to facilitate interpretation: Figure 1 shows the predicted values of decision time as a function of both cross-pressures and individual ambivalence. The higher the value of decision time, the later the decision was made, temporally closer to Election Day. The pattern revealed suggests that network cross-pressures affect some people differently than others, as predicted (H4). For those individuals who were highly ambivalent, increases in cross-pressures delayed their decision time, adding perhaps more sources of uncertainty and prompting yet more vacillation. This effect was by no means uniform for all respondents; the evidence suggests that for some, increases in cross-pressures were associated with more decisiveness. Those individuals, who were less ambivalent, were likely to form their voting decision much earlier when they were faced with more cross-pressures, compared to those who were less exposed to cross-pressures. Put differently, the rather weak or absent effect of cross-pressures in all models is perhaps explained by the last finding: While for some, cross-pressures were a delay and detriment to decision making, for others, exposure to conflicting pressures helped some to arrive at a decision earlier in the course of the campaign.3

2 Results obtained from ordinal regression models (not shown here) are consistent with the results reported from OLS regression. For ease of interpretation, OLS models are presented. 3 Follow-up analyses did not reveal a significant interaction pattern (individual × network ambivalence) in models predicting voting and participation.


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FIGURE 1 Predicted values of vote decision lateness as a function of network- and individual-level ambivalence

Timing of vote decision

5

4

3

2

Low ambivalence individuals Highly ambivalent individuals

1 Low network ambivalence

High network ambivalence

Note: Coefficients derive from Table 3, Model 2 (with interaction). Values of ambivalence terms are first and third quartile cut points, multiplied by their respective coefficients. Additional statistically significant covariates in the regression equation held constant at their means. Predicted values note early (=1) to late (=5) decision time. Data source: ANES, 2000.

D I S CU S SI O N Are cross-pressures detrimental to individuals’ political participation and decision making, and hence a double-edged sword to democracy? This paper has argued for the need to conceptualize cross-pressures solely on the network level (rather than a mix of individual- and network-level indicators of cross-pressures, as in some previous studies). In addition, this paper argued that cross-pressures should be conceptualized as the balance of oppositional or ‘pro’ and ‘con’ forces that are operating at the individual’s socio-political discursive environment, rather than the extent to which the individual is isolated from the environment in his or her opinion. Cross-pressures, re-conceptualized as ‘network-level ambivalence’, and measured separately from intra-individual ambivalence, proved here to be non-significant predictors of participation, voting for president in the U.S. 2000 elections, and vote decision timing. Individuals who perceived cross-pressures within their discussion network were no more and no less likely to engage in participatory activities, all else being equal. It was the individual-level ambivalence—a measure whose convergent validity and test–retest reliability this study tested on a nationally representative survey sample—which was associated with the respondents’ lower extent of participation and longer time for the crystallization of their electoral preference. This finding squares with former studies that attest to the effects

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of ambivalence (e.g. Lavine, 2001; Mutz, 2002b). At the same time, however, my findings also suggest an explanation as to why former studies of cross-pressures, which confounded network and individual-level ambivalence, concluded that cross-pressures exerted these effects (Lazarsfeld et al., 1968/1944; Berelson et al., 1968/1954). Presumably, the explained variance in these outcomes could be attributed exclusively to the individual-level internal conflict, but studies that confounded it with structural cross-pressures blurred the distinctive contribution of individuals’ ambivalence to the explained variance in these outcomes. Perhaps of most interest is the last finding reported here: Cross-pressures may actually help some potential voters make up their minds, rather than hinder the crystallization of their voting preferences. As Figure 1 has shown, those individuals who were less ambivalent were more likely to decide early on their voting preference if they faced cross-pressures, compared to those who did not face any cross-pressures. While this is only the very first step in exploring that pattern, it nonetheless sheds light on some previous findings (or rather, non-findings) regarding the effects of cross-pressures. The evidence here suggests that a non-association between cross-pressures and lateness of decision might have obscured a positive and negative pattern of association, contingent on another factor, patterns which were canceling each other out and thus were not evident in previous research. A number of limitations pose a threat to the inferences drawn in this study. The cross-sectional survey design cannot permit causal inferences. Here we have examined, under theoretical guidance, correlations that remain significant while ruling out some alternative explanations (control variables). The results are not to be interpreted, therefore, in the same vein as one would interpret experimental results. One of the most important controls was omitted from the cross-sectional analyses, for reasons that are methodological rather than theoretical. The ANES ordinal measure of the frequency of discussion was highly correlated with network size; so much so that they were a linear function of one another (r = .97, p < .001) and any estimation with both terms in the regression equation would have biased the coefficient estimates. Perhaps future investigation of the crosspressures interaction pattern should be studied in a laboratory-like setting, in which a political discussion environment is manipulated (homogeneous, or crosspressured) and its effects on participatory outcomes would be modeled as contingent on individual-level ambivalence. The advantages of the large-scale, nationally representative survey are a trade-off to these limitations: having a representative, and considerably more heterogeneous sample, on which to test the reliability and validity of a measure, which has been established theoretically and empirically as an important factor associated with participation and electoral decision making. On a final, normative note, perhaps we can benefit from revisiting the assumption about the consequences of political conviction for citizen’s competence in a democratic system. While conviction may hold an obvious advantage over ambivalence in its consequences for political action, there is a case to be made for


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ambivalence as an inevitable and desirable feature of reasoned decision making. An ambivalent attitude ‘suggests a willingness to take seriously the tenor and tide of political debate taking place within a democracy’ (Guge, 1999, p. 182); this willingness nourishes democratic processes: A democracy composed of consistent, tranquil, attitudinally constrained citizens is a democracy full of smug people with no incentive and perhaps no ability to think beyond their own circumstances. . . . Conversely, a democracy composed of citizens coping with disjunction and ambivalence is full of people who question their own rightness, who may entertain alternative viewpoints, and who, given the right conditions, are more driven to resolve problems than ignore them. . . . The former democracy may be more content but is too brittle to survive for long; the latter one is less happy but stronger (Hochschild, 1993, p. 206).

Holding an ambivalent attitude, in other words, counteracts the shortcoming of holding on too strongly to one’s convictions. Policy proposals are suggestions for alternative courses of action to resolve collective problems. Ambivalence toward them—in contrast to mere indifference—is the attitudinal manifestation of acknowledging the merits and demerits of each, prompting a person to engage in more thoughtful consideration of them. Ambivalence increases the likelihood that alternative viewpoints on policy will be monitored and taken into consideration when political circumstances persevere, or even more importantly, when these circumstances change. A second assumption worth revisiting regards the supposed detrimental effects attributed to the persistence of cross-pressures for individuals betwixt and between socio-political cleavages. Some scholars have expressed concerns over the retreat from participation that cross-pressures might cause, and consequently recommended consensus within networks as a catalyst for participation. Far from being detrimental to a healthy democratic order, however, a balance between political cleavage and consensus may be the precondition for it: ‘enough cleavage to stimulate debate and action, enough consensus to hold society together even under strain’ (Berelson, 1952, p. 328). Cross-pressures are, in Berelson’s view, the very products of social overlapping that link various groups together and prevent their further estrangement (p. 329). Thus cross-pressures are highly important to society, ever changing and adapting to new social conditions, through public discussion and debate. REFERENCES Alvarez, R. M., & Brehm, J. (2002). Hard choices, easy answers. Princeton, NJ: Princeton University Press. The American Association for Public Opinion Research. (2004). Standard definitions: Final dispositions of case codes and outcome rates for surveys (3rd ed.). Lenexa, KS: AAPOR.

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B IO GR A PH I CA L NO T E Lilach Nir (Ph.D., University of Pennsylvania, 2004) is assistant professor in the departments of communication and political science, the Hebrew University of Jerusalem. Her research interests are in the social psychology of public opinion and political communication. Nir

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has published in Public Opinion Quarterly and Political Communication articles on opinion formation and expression, disagreement, and public opinion quality. Address correspondence to Dr Lilach Nir, Department of Communication, The Hebrew University, Mt. Scopus, Jerusalem, 91905, Israel, E-mail: [email protected] or [email protected]