Does Disagreement Contribute to More Deliberative ...

Political Communication, 19:95–112, 2002 Copyright ã 2002 Taylor & Francis 1058-4609 /02 $12.00 + .00

Does Disagreement Contribute to More Deliberative Opinion? VINCENT PRICE, JOSEPH N. CAPPELLA, and LILACH NIR

Theorists have argued that discussion and disagreement are essential components of sound public opinion, and indeed that both are necessary for effective democracy. But their putative benefits have not been well tested. Consequently, this article examines whether disagreement in political conversation contributes to opinion quality— specifically, whether it expands one’s understanding of others’ perspectives. Data are drawn from a survey of the American public (N = 1,684) conducted in February and March 2000. Open-ended survey measures of “argument repertoire”—reasons people can give in support of their own opinions, as well as reasons they can offer to support opposing points of view—are examined in light of numerous explanatory variables, including the frequency of political conversation and exposure to disagreement. Results confirm the hypothesis that exposure to disagreement does indeed contribute to people’s ability to generate reasons, and in particular reasons why others might disagree with their own views. Keywords

disagreement, opinion quality, political discussion, public opinion

Theorists have long argued that discussion and disagreement are essential to public opinion. In its “classical” conception (see Berelson, 1950), public opinion is not a mere aggregation of mass attitudes bearing on political affairs, but instead the emergent product of widespread popular conversation. Theoretically, public opinion is a phenomenon that permits society to adapt to changing circumstances. It exists at the point where societal norms fail to give adequate guidance for collective action, where diverse, conflicting, and contradictory wishes for action are sorted out through discussion (Blumer, 1946). Like a crowd, the public is just an “empirical preliminary stage” in the process of social organization (Park, 1904/1972, p. 80). Whereas the crowd develops its action out of shared emotional experience, however, the public works out its action rationally, through opposition and discourse. Public opinion is by nature a form of communicative action (Habermas, 1962/1989).

Vincent Price is Associate Professor, Joseph N. Cappella is Professor, and Lilach Nir is a doctoral candidate, all in the Annenberg School for Communication at the University of Pennsylvania. This research is supported by grants to the first two authors from the Pew Charitable Trusts and the Annenberg Public Policy Center of the University of Pennsylvania. The authors thank Yariv Tsfati, Jenny Stromer-Galley, Danna Goldthwaite, Tresa Undem, Son-Ho Kim, Clarissa David, Emily West, and Lisa Rand for their assistance. Views expressed are those of the authors alone and do not necessarily reflect opinions of the sponsoring agencies. Address correspondence to Vincent Price, Annenberg School for Communication, University of Pennsylvania, 3620 Walnut Street, Philadelphia, PA 19104-6220, USA. E-mail: [email protected]

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This communicative action is, in much normative democratic theory, viewed as central to the production of sound public opinion and effective democracy. Not just any sorts of conversation will do for effective self-governance. Many theorists posit that public conversation must be deliberative, meaning that it airs disagreements, bringing into play a wide range of alternative perspectives and viewpoints (Fishkin, 1991, 1995; Gutmann & Thompson, 1996). The ideal democratic community is one that resolves its action through free and equal exchange, invites and encourages arguments for all sides, and grants to argument, rather than to coercion, the power to shape collective choices (Arendt, 1958; Habermas, 1962/1989, 1981/1984). Why is disagreement so vital? The reason is that it forces more careful consideration by challenging points of view—hence, those who deliberate form better reasoned opinions. Moreover, deliberation expands the repertoire of considerations and arguments, and thus it fosters understanding, among participants, of multiple points of view (Gutmann & Thompson, 1996). Notwithstanding this theoretical pedigree, disagreement’s effects remain relatively untested. Moreover, what little research we have to date raises some questions about the putative benefits of exposure to disagreement in public conversation (Mutz, 1998, 2000). Consequently, this paper examines whether disagreement in political conversation contributes to opinion quality—specifically, whether it expands understanding of others’ perspectives. Data are drawn from a survey of the American public (N = 1,684) conducted in February and March 2000. Open-ended survey measures of “argument repertoire”—reasons people can give in support of their own opinions, as well as reasons they can offer to support opposing points of view—are examined in light of numerous explanatory variables, including the frequency of political conversation and exposure to disagreement. Before describing our methods and findings, however, we should consider more carefully the kinds of outcomes we might expect from deliberation and disagreement and what we know from current research about the linkages between political conversation and opinion quality.

Deliberation and Deliberative Opinion What kinds of outcomes should deliberation produce? Price and Neijens (1997, 1998) have identified a number of different conceptualizations of opinion quality, arguing that public opinion research—despite the burgeoning interest in deliberation and arguments for new techniques, such as deliberative polling—has failed to conceptualize and measure quality outcomes adequately. Fortunately, there have been some promising efforts recently to assess the coherence and argumentative quality of opinions (e.g., Gastil & Dillard, 1999; Kim, Wyatt, & Katz, 1999; Wyatt, Katz, & Kim, 2000). Our major interest here, however, focuses on particular aspects of opinion quality thought to be a distinct product of disagreement: the range of a person’s understanding of some public matter and, especially, his or her understanding of what other people who disagree might think. These qualities of opinion correspond to what Park (2000), in a recent theoretical review of deliberation and its outcomes, has termed “civility.” As noted above, deliberation forces more careful consideration by challenging points of view and consequently fosters opinions that are more soundly reasoned and buttressed by arguments. This outcome corresponds generally with the quality of “consideredness” investigated by Kim, Wyatt, and Katz (1999). Park (2000) considers these features of opinion as reflecting “individuality,” for they relate to the sophistication, consistency, and certainty of one’s personal views and, behaviorally, to one’s ability to argue those preferences assertively. A second aspect of opinion—in Park’s view

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neglected but even more central to theories of deliberation and democracy—relates not to one’s own views but instead to one’s understanding of others. This he calls “civility.” He interprets Habermas’s (1962/1989) theory of communicative action as emphasizing mutual understanding and civility. A basic democratic trait, Park submits, is awareness of what other people think, coupled with some understanding of why others think the way they do. Whereas speaking develops strength and individuality in opinion, it is hearing others speak that develops civility. Deliberation requires both: It is a “dual process of speaking and listening” (Park, 2000, p. 5). Put another way, what makes opinion deliberative is not merely that it has been built upon careful contemplation, evidence, and supportive arguments, but also that it has grasped and taken into consideration the opposing view of others. Park identifies a range of cognitive, attitudinal, and behavioral aspects of civility. The cognitive elements of civility include breadth of understanding, perspective taking, and understanding of others’ views. Attitudinal components include empathy, tolerance, trust in others, and reciprocity. Behavioral components include speaking “with” (as opposed to speaking “for” oneself or “against” others), compromise, and consensus building. We focus here on what we take to be one of the core cognitive components of civility—namely, the consideredness of one’s opinion, both in the sense of having anchored a viewpoint in argument and in the sense of having considered other, opposing views. We investigate “argument repertoires” (see Lustick & Miodownik, 2000)—the range of arguments people hold both in support of and against their favored position on a particular political issue or toward some political object. Our conceptualization and measurement approach follows in some respects the consideredness measure used by Kim and colleagues (Kim, Wyatt, & Katz, 1999; Wyatt, Katz, & Kim, 2000). However, while the Kim et al. measure basically assesses the ability of respondents to argue their views, our assessment takes direct empirical account of their understanding of others’ points of view.

Recent Research on Political Conversation Not all discussion, perhaps not even a large share of ordinary political conversation, is deliberative in the sense related above (see, e.g., Bennett, Flickinger, & Rhine, 2000). In the first instance, many people rarely talk about public affairs. And when they do, they often encounter other people who largely resemble themselves. Most people, most of the time, discuss politics within their primary groups of family and close friends. Wyatt, Katz, and Kim (2000) recently examined the topics, sites, and frequency of popular conversations about public affairs. They found that conversations clustered into “private” talk, which tended to focus on things like crime, education, religion, entertainment, and various personal matters, and “public” talk, centered on national and local government, the economy, and foreign news. Even the latter conversations, however, usually occur in the “private” space of home, or among close friends. These intimate associates may be, more often than not, similar in many of their political views. What’s more, a number of researchers have proposed that fears of potential social discomfort can inhibit or pressures toward conformity can stifle expressions of dissenting points of view, even when people do privately disagree (e.g., MacKuen, 1990; Sheufele, 1999). These findings suggest that the likelihood of encountering disagreement in everyday conversation about public affairs may be relatively low. This is not to say that close friends and family members do not disagree among themselves—certainly they can and

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do. However, research has confirmed that the likelihood of encountering disagreement rises considerably as conversational networks become larger in size and more heterogeneous in composition. Dissimilar ideas move through society by bridging cohesive groups, carried by those people who are “boundary spanners,” who have diverse social networks. The more ties a person has, and the weaker and more heterogeneous the ties, the higher the likelihood that a person will serve as a conduit of novel information (Granovetter, 1973; Weimann, 1982). People who converse in extensive social networks—who talk to others in both “private” and “public” spaces—have been shown to be distinctive. Citizens in nonoverlapping social groups are better educated, more attentive to media messages, more knowledgeable about politics, and more politically involved (Robinson & Levy, 1986; Weimann, 1982). They talk more often, and they also encounter more disagreement. Consistent with their exposure to diverse views, people with large and heterogeneous social networks are better able to assess the distribution of opinion than are people in more homogeneous networks (Huckfeldt, Beck, Dalton, & Levine, 1995). They tend to accrue more “social capital” than their less talkative counterparts (La Due Lake & Huckfeldt, 1998), and recent studies suggest that network heterogeneity predicts civic participation (McLeod et al., 1999).

Hypotheses Because exposure to disagreement travels in this company—higher education, greater political knowledge, more intense political interest, higher rates of political participation (in short, all of the sorts of variables we expect to find associated with more deliberative opinion)—it will be difficult to sort out whether it is indeed disagreement that contributes to opinion quality. Yet, the notion that disagreement is indeed a central and critical factor in producing deliberative opinion is at the very heart of the normative theories outlined above. Hence we propose to test the following hypotheses: first, that both the frequency of political discussion and levels of exposure to disagreement will be associated with possession of a larger argument repertoire; second, that levels of exposure to disagreement will predict awareness of arguments that others might make in opposition to one’s personal views; and, third, that these associations will not be explained by other relevant factors, including education, political knowledge, interest, participation, and the like.

Method Sample Data are taken from a multiwave survey of 1,684 adult Americans conducted in February and March 2000. These were baseline surveys gathered as part of a year-long panel study of the 2000 presidential elections (the Electronic Dialogue Project of the Annenberg Public Policy Center at the University of Pennsylvania, funded by the Pew Charitable Trusts). Respondents came from a random sample of American citizens age 18 and older drawn from a nationally representative panel of survey respondents maintained by Knowledge Networks, Inc., of Menlo Park, California. The Knowledge Networks panel includes a large number of households (in the tens of thousands) that have been selected through random digit dialing (RDD) and agreed to accept free WebTV equipment and service in exchange for completing periodic surveys on-line.1


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Dependent Variable: Argument Repertoire Respondents were asked whether they were favorable or unfavorable toward the Democratic and Republican parties. Responses were given on a 4-point ordinal scale ranging from “very favorable” to “very unfavorable.” Following these questions, a series of four open-ended questions asked respondents to give reasons why they were favorable (or unfavorable) toward each party, followed by a question asking for reasons why other people might have the opposing view (see the Appendix for exact question wording). Responses to these open-ended questions were coded in the following manner. When the answer was irrelevant, did not make sense, merely restated the opinion, indicated that the person did not know why he or she held that opinion, alluded in a vague way to the parties’ positions, or was a statement about party membership only, it was coded as zero. For example, statements such as “I like the Democratic party,” and “The Democrats smell” were coded as zero. For each substantive answer, one point was given for every reason the respondent wrote. For example, the following response received a score of six. Question: What are the reasons you have for feeling very unfavorable toward the Republican party? Answer: Views on abortion (1), too close ties to business interests (2), fight against raising minimum wage (3) and other ways to help the poor and working class Americans (4), insistence on tax cuts (5), fight against making health care benefits more available and affordable (6). We assessed intercoder reliability between three coders on a subsample of 50 openended responses. Cohen’s kappa values for the coders’ agreement above chance ranged from .70 to 1.00, mostly in the .80 to .89 range. Reasons for own opinion. In the example above, a person who is unfavorable toward the Republican party is stating six different reasons that justify his or her unfavorable views; hence, these would be coded as reasons for his or her own opinion. Similarly, reasons given by this same person for why he or she might feel favorably toward the Democratic party would also be coded as reasons in support of his or her own opinion. No restrictions were applied to symmetry in liking of the two parties. That is, people could report liking both or disliking both; in all cases, reasons given in support of their feelings were coded as reasons for their own opinions. A combined index of the coded responses was constructed as the sum of respondents’ reasons for holding their own opinions (a = .77, one factor accounting for 81% of the variance). The number of reasons for own opinion index ranged from 0 to 20, with about 16% giving no reasons and about 46% giving 2 to 5 reasons (Mdn = 3, M = 3.94, SD = 3.25). Reasons why others might disagree. After being asked to supply reasons for their own opinions, respondents were asked to offer reasons why other people might hold the opposite view. Someone who indicated favorability toward the Republican party, for instance, was then asked to provide reasons why others might be unfavorable toward the Republican party. Similarly, a person who was unfavorable toward the Democratic party was asked to state reasons why others might be favorable toward Democrats. A combined index of the coded responses was constructed as the sum of respondents’ listed reasons (a = .80, one factor accounting for 83% of the variance). The index for the

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number of reasons for why others might disagree ranged from 0 to 16, with about 28% giving no reasons and slightly more than 40% giving 1 to 3 reasons (Mdn = 2, M = 2.66, SD = 2.64). Independent Variables Political conversation. Respondents were asked to name (by giving initials) up to two close friends or family members with whom they discussed public affairs. They were then asked to identify several features of these discussions, including their relationship to the named person, the typical number of days per week they talked with the person about politics, and the extent to which they tended to disagree (see Appendix for question wording). A second battery asked respondents to name (again by giving initials) two acquaintances, such as “people at work or simply people you see going about your day,” with whom they discussed public affairs. The same follow-up questions were used for these named discussants as well. An additive scale was constructed as a count of the potential 0 to 4 discussion partners. Slightly less than 11% did not name any discussion partners, and about 56% (n = 940) named four discussion partners.2 Frequency of discussion. The respondents reported how many days in the past week (0 to 7) they discussed political issues with each of the four named discussion partners. An additive scale of the respondents’ answers to the four questions was computed (a = .74; M = 5.94, SD = 4.93), with those who did not name any discussants coded as 0. In addition, following the distinction between discussion within and across cohesive networks, we computed two subscales—days the respondent discussed politics with family/ friends and days he or she discussed politics with acquaintances. Disagreement. For each of the four discussion partners, the respondents reported the extent to which the named discussant tended to disagree with the respondents’ own views. Disagreement was measured on a 5-point ordinal scale ranging from “never” to “almost all the time.” We computed an additive scale, since disagreement within one’s network of discussants should have a cumulative effect (a = .72; M = 7.34, SD = 4.16). Those who did not name any discussants were coded as 0. In addition, following the distinction between discussion within and across cohesive networks, we computed two subscales—the extent of disagreement with family/friends and the extent of disagreement with acquaintances. Political Involvement Strength of political leanings. Participants were asked about their party identification and its strength. They were also asked about their overall ideological leanings, on a continuum from strong liberal to strong conservative. The two components, which were highly correlated, were combined to form an 11-point scale with “strong liberals–strong Democrats” coded as 5, “strong conservatives–strong Republicans” coded as –5, and “moderates-Independents” coded as 0 (M = –0.26; SD = 3.18). This scale was then folded at its center point so as to create a unipolar measure of the strength of respondents’ partisan identifications. Political knowledge. Various dimensions of political knowledge were combined to form a single scale measure. Items included 10 general political and civics knowledge


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questions (e.g., Who has the final responsibility to decide if a law is constitutional or not?), 7 questions about the personal backgrounds of the presidential candidates (e.g., Which one of the Democratic candidates was a professional basketball player? Which one of the GOP candidates is a former POW?), and an additional 7 questions about issue positions of candidates in the Democratic and Republican presidential primaries (e.g., Which one of the Democratic candidates supports universal health care? Which of the Republican candidates supports vouchers?). All 24 items were scored 1 for correct answers and 0 for incorrect answers. The items were averaged to create a scale (Cronbach alpha = .82; M = .62; SD = .19). Political interest. Two questionnaire items comprised a political interest scale. The questions, measured on a 4-point ordinal scale, inquired about habitual following of public affairs and caring which party wins in the 2000 elections. The majority of respondents (79%) reported that they followed public affairs either “most” or “some” of the time. About 50% of the respondents replied they cared “a great deal” which party wins the elections. Both items loaded on a single factor that explained 73% of the variance. A scale averaging the two responses was computed (a = .62; M = 3.20, SD = 0.71). Political participation. Respondents were asked whether or not in the last 12 months they had participated in various political activities (e.g., attended any political meetings or rallies, done any other work for a candidate, given money, worn a campaign button, put a campaign sticker on their car, contacted or written a public official, or tried to persuade others). Each of the eight items was coded dichotomously (0 = no, 1 = yes) and averaged (a = .62; M = .11, SD = .15). Mass Media Use Exposure. Exposure to mass-mediated current events content was measured by five different items inquiring about the respondents’ self-reported media use in days during the past week (0 to 7). Newspaper reading and political talk radio exposure were measured as single items. Three items—exposure to television national network news, cable news, and local news—were scaled together. A factor analysis of the three items yielded a single factor explaining 59% of the variance in responses. A scale averaging the scores was computed (a = .66; M = 3.46, SD = 1.93). Attention. Two questions asked respondents how much attention they had paid to articles about the presidential campaign in newspapers, and to reports about the campaign on television, during the past week. For each medium, responses were measured on a 5-point scale ranging from “a great deal” to “none.” Of the people who completed both waves of the baseline survey, about 18% (n = 294) did not report any exposure to newspapers, and 7.2% (n = 122) did not report exposure to television news content. These responses were coded as 0 on the attention measures. Newspaper attention ranged from 0 to 5 (M = 2.49, SD = 1.5), as did television news attention (M = 3.04, SD = 1.34). Demographic Characteristics Respondents reported their age (M = 44.5 years, SD = 15.8), level of education (coded in years; M = 13.5, SD = 1.81), gender (46% female, 54% male), and race (83% White, 7% Black, 2% Asian, 2% Native American, 6% “other”).

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Results Frequency of Conversation, Disagreement, and Reasoning The frequency of discussing politics was significantly correlated with the number of reasons the respondents provided for their own opinion (r = .24, p < .001), as well as with the number of reasons they provided in support of the opposing view (r = .22, p < .001). Similarly, the disagreement score was significantly correlated with the number of reasons the respondents provided for their own view (r = .21, p < .001), as well as with the number of reasons they provided for the opposing opinion (r = .24, p < .001). Frequency of conversation and disagreement were themselves somewhat highly interrelated (r = .62, p < .001). Table 1 presents a three-way comparison of the mean number of reasons the respondents gave for their own opinion and for others’ opinions, across levels of discussion frequency and levels of disagreement. Overall, and perhaps not surprisingly, the respondents gave more reasons in support of their own opinions than reasons as to why others might disagree with them. The average number of reasons, however, was clearly contingent upon both discussion frequency and disagreement level. The higher the frequency of political conversation, the higher the number of reasons provided (p < .05), both for respondents’ own views and for others who might disagree with them. For example, people who discussed politics at a high frequency gave almost twice as many reasons for their own opinions (M = 4.72) as did people who discussed politics most infrequently (M = 2.61). Similarly, people who discussed politics at a high frequency gave on average twice as many reasons in support of an opposing opinion (M = 3.00) as people who discussed politics most infrequently (M = 1.53). Level of disagreement was associated with argument repertoire as well, after controlling for frequency of discussion. The higher the disagreement with discussion Table 1 Reasons given, by frequency of discussion and level of disagreement Reasons for own opinion

Reasons why others disagree

Low disagreement (N = 773)

High disagreement (N = 911)

Low disagreement (N = 774)

High disagreement (N = 909)

Low

2.61 (470)

3.99 (78)

1.53 (471)

3.14 (78)

Medium

3.86 (197)

4.33 (378)

2.34 (198)

3.20 (378)

Frequency of discussion

High

4.72

4.84

3.00

3.37

(106)

(455)

(105)

(453)

Note. Entries are means of the number of reasons. Sample sizes are in parentheses. N (listwise) = 1,684. Low and high disagreement groups are based on a median split, while low, medium, and high frequency of discussion groups are based on a tercile split.


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partners, the higher the number of reasons provided (p < .05 for both sets of reasons). Among respondents who infrequently discussed politics, those who encountered a higher level of disagreement provided on average nearly 4 reasons for their own opinion, as compared with an average of just 2.61 reasons from those who encountered less disagreement. As expected, an even stronger relationship emerged in the case of reasons given in support of opponents’ points of view, particularly among those respondents who do not talk much about politics. Among such people, those who encountered high levels of disagreement were able to provide on average twice as many reasons in support of others’ opinions (M = 3.14) as those who did not encounter much disagreement (M = 1.53). Predictors of Reasoned Opinion Table 1 provides some evidence of positive associations between discussion, disagreement, and reasoned or “deliberative” opinion. This is at least preliminary support for our hypotheses. However, antecedent variables such as political involvement, mass media exposure, and demographics might well account for these basic relationships. We therefore tested whether they continue to hold under multivariate controls. Table 2 presents a comparison of two ordinary least squares (OLS) regression models, one predicting the number of reasons respondents provide in support of their own opinions (left-hand column) and another predicting the number of reasons they can generate in support of opposing points of view (right-hand column). Reasons for own opinions. The first model, regressing the number of reasons given in support of respondents’ own opinions on demographics, political involvement, media use, and political conversation, explained 31% of the variance. The lone demographic variable predicting the number of reasons provided is education. The strongest predictor in the model is political knowledge (b = .33, p < .001), followed by political participation (b = .15, p < .001). The more knowledgeable the respondent, and the more engaged in political activities, the more reasons she or he gave for her or his opinions. Strength of partisanship (b = .10, p < .001) and political interest ( b = .08, p < .01) also produce significant coefficients. Political conversation variables (see the lower block of predictors) continue to predict the number of reasons, even after these extensive controls. The frequency of discussing politics with family members and close friends emerged as a significant predictor of the reasons for one’s own opinion (b = .07, p < .01). Perceived disagreement of acquaintances was also significantly, and positively, associated with the respondent’s ability to provide reasons. Notably, it is disagreement among acquaintances, not among close friends and family, that demonstrates this effect (b = .13, p < .001). Mass media use, for its part, appears to play a more limited role in explaining the number of reasons respondents are able to provide. Attention to campaign reports on television almost reached the acceptable significance level and was associated in the expected direction. Television exposure, in contrast, was significantly and inversely associated with the number of arguments produced (b = –.10, p < .001), after controlling for knowledge, interest, strength of partisanship, and political conversations. The greater the number of hours respondents watched television news, the fewer reasons they provided for their own opinion. To summarize, the first model in Table 2 suggests that political conversation and political involvement variables together explain most of the variance in argument

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Table 2 Predictors of reasons given for respondents’ own opinions and why others disagree Reasons for own opinion B Intercept Demographics Gender (male) Race (White) Education (years) Age (years) Political involvement Strength of party identification Political knowledge Political participation Political interest Mass media exposure Television Newspaper Political talk radio Attention to politics in the news Television Newspaper Political conversation Frequency: family & close friends Disagreement: family & close friends Frequency: acquaintances Disagreement: acquaintances

b

–4.15***

Reasons why others might disagree B

b

–4.61***

–.10 .16 .13** .01

–.01 .02 .07 .03

–.20 .23 .20*** .01

–.04 .03 .13 .04

1.02*** 5.60*** 2.18*** .40**

.10 .33 .15 .08

.11 4.49*** 1.48*** .24*

.01 .32 .12 .06

–.17*** –.05 .00

–.10 –.04 .00

–.16*** –.03 .02

–.12 –.03 .02

.14 † .04

.06 .02

.08** –.04 –.05 .15***

.07 –.02 –.04 .13

.08 .03 .07** –.01 –.09* .17***

R2

.31

.30

N

1,447

1,447

.04 .02 .08 –.01 –.08 .18

†p < .1; *p < .05; **p < .01; ***p < .001.

repertoire. Even after controlling for involvement, demographics, and mass media use, the relationship between frequency of discussing politics, disagreement, and reasoned opinions still holds. Reasons why others disagree. What of mutual understanding, and awareness of others’ perspectives? The second model in Table 2, predicting reasons given to explain why others might disagree with the respondent, explained 30% of the variance. In many respects, the model estimates are quite similar. Among demographic variables, again education was the lone significant predictor. Knowledge, political participation, and interest produce coefficients similar to those estimated in the first model. Television exposure again produces a negative coefficient. Strength of party identification, however, fails to predict the number of reasons respondents give for opposing views.


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Political conversation variables, meanwhile, remained significant predictors of the ability to give reasons for opposing points of view, controlling for all of the other predictors in the model. The frequency of discussing politics with family and close friends was positively associated with generating reasons (b = .08, p < .01). So was disagreement: Reported level of disagreement—again, with acquaintances—was positively associated with number of reasons (b = .18, p < .001). Indeed, next to knowledge it is the strongest predictor in the model. The frequency of discussing politics with acquaintances, however, was inversely associated with generating reasons (b = –.08, p < .05), suggesting that the extent of disagreement encountered among acquaintances, but not the sheer amount of contact, contributes to knowing reasons for others’ points of view. Alternative Interpretations The multivariate results confirm our expectation that encountering disagreement, particularly in extended discussion networks outside of the “private sphere” of family and close friends, is positively associated with deliberative opinion, that is, views that are grounded not only in supportive arguments but also recognition of counterarguments. But there are several factors that may confound our interpretation of these particular results. First, it may be that disagreement with acquaintances is spuriously associated with argument repertoire, because it is confounded with network size. Larger networks are likely to be more heterogeneous, and persons who have larger networks are thus likely to encounter more disagreement. It may be larger networks—not disagreement itself— that contribute to the argument repertoire measures. We therefore entered network size into the model as well. Results of these analyses (not shown) rule out this competing hypothesis. Controlling for network size, the relationship between disagreement with acquaintances and reasons why others disagree still holds (b = .11, p < .01). This finding suggests that encountering disagreement when discussing politics with acquaintances bears a positive effect on generating reasons for an opposing point of view, independent of network size. Another, perhaps more serious problem with the findings presented in Table 2 is the possibility that our argument repertoire measures reflect not the actual ability of people to reason their own opinions and to understand others’ perspectives, but instead some individual difference in the tendency to list reasons in response to open-ended questions. It could also be that our two classes of reasons—in support of one’s own views and in support of opposing views—do not have discriminant validity. They may both reflect the same underlying phenomenon, and perhaps a methodological one at that. It is notable, in this regard, that the two reasons measures are positively correlated and have very similar predictors (compare models in Table 2). Our count of reasons given in support of others’ opposing opinions might reflect not mutual understanding or “deliberative” opinion, as we have suggested, but instead simply a general proclivity to give arguments. We therefore controlled for that tendency by entering the number of reasons why others disagree as a covariate in the model predicting reasons for one’s own opinion, and vice versa (entering the number of reasons for one’s own opinion as a covariate in the model predicting the number of reasons why others might disagree). The logic is that their shared variance reflects simple argument generation, while the residual variation reflects what is truly unique to that particular component of the argument repertoire: reasoning one’s own opinion, on the one hand, or understanding the rationale for opposing points of view, on the other. Results of this analysis are presented in Table 3.

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Table 3 Predictors of reasons given for respondents’ own opinions and why others disagree, controlling for tendency to generate reasons Reasons for own opinion B Intercept Reasons for own opinion Reasons why others might disagree Demographics Gender (male) Race (White) Education (years) Age (years) Political involvement Strength of party identification Political knowledge Political participation Political interest Mass media exposure Television Newspaper Political talk radio Attention to politics in the news Television Newspaper Political conversation Frequency: family & close friends Disagreement: family & close friends Frequency: acquaintances Disagreement: acquaintances

–.97 n.a. .69*** .04 .00 –.01 .00 .94*** 2.49*** 1.16*** .23*

b

Reasons why others might disagree B

b .58

.57

–4.61*** .48*** n.a.

.01 .00 –.01 .01

–.16 .16 .14*** .00

–.03 .02 .09 .02

.09 .14 .08 .05

–.38* 1.81*** .43 † .05

–.05 .13 .04 .01

–.06 –.03 –.01

–.03 –.02 –.01

–.08* .00 .02

–.06 .00 .02

.09 .02

.03 .01

.01 .01

.01 .01

.03 –.03 .01 .03

.03 –.02 .00 .03

.03 .01 –.06* .10***

R2

.54

.53

N

1,447

1,447

.03 .01 –.06 .11

Note. n.a. = not applicable. †p < .1; *p < .05; **p < .01; ***p < .001.

Reasons for own opinion. The first of the two models, predicting the number of reasons the respondents provided for their own position, accounts for 54% of the variance. Not surprisingly, the number of reasons why others disagree has the greatest predictive power (b = .57, p < .001). Despite this strong control, however, additional variables continue to account for the remaining variance. The political involvement variables were all positively and significantly associated with reasons for one’s own opinion. None of the demographic variables, including education, were significantly associated with providing reasons for one’s own opinion. Similarly, neither mass media use nor interpersonal


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communication about political issues was associated with argument repertoire when we controlled for reasons supporting an opposing point of view. Reasons why others disagree. The second model in Table 3 predicts the number of reasons the respondents provided for opposing opinions, controlling for the number of reasons they gave for their own. The model accounts for 53% of the variance, with the number of reasons for one’s own opinion having the most predictive power (b = .58, p < .001). Despite the strong control for argument generation, however, other variables accounted for the remaining variance. Similar to the first model, political knowledge was the second strongest predictor of argument repertoire (b = .13, p < .001). Of great interest—and a finding giving some testimony to the discriminant validity of the two reasons measures—is the estimated impact of partisan strength. While strength of party identification is positively associated with reasons given to support one’s own viewpoints (b = .09, p < .001), it is inversely associated with the ability to cite reasons why others might disagree (b = –.05, p < .05). This finding indicates that the more intense the party identification, the more able the respondent to reason her or his own feelings toward the two parties. On the other hand, the more intense the identification, the fewer the reasons offered to support opposing positions that others might take. Put another way, people who characterized themselves as less extreme were able to generate more reasons for supporting their less favored party than those who described themselves as strong partisans. Here we find some empirical reflection of the notion that civility and moderation travel together (once we have controlled for education, political knowledge, involvement, other key correlates of ideological strength, and the tendency to generate arguments). Unlike the first model in Table 3, respondents’ education level was positively and significantly associated with reasoning the opposing point of view, controlling for the tendency to generate reasons (b = .09, p < .001). Of the mass media use variables, only television exposure was significantly (and again, inversely) associated with generating arguments for an opposing point of view (b = –.06, p < .05). Most important, disagreement in political conversation with acquaintances remains significantly associated with the number of reasons generated for others’ point of view. Indeed, behind reasons for one’s own view and political knowledge, disagreement in extended communication networks has the next largest estimated effect (b = .11, p < .001). The more political disagreement respondents encountered in talking with acquaintances, the more reasons they provided in support of opposing points of view, even after controlling for the tendency to generate reasons.3 This finding is contrasted to the first model in Table 3, where disagreement was not significantly associated with reasons given for one’s personal position after controlling for the tendency to generate reasons per se.

Discussion In general, these findings offer support for the hypothesis that encountering disagreement in political conversation contributes to more deliberative opinion. By deliberative opinion, we mean the ability to ground one’s viewpoints, not only in supportive arguments but also in an understanding of the kinds of arguments that others might make in taking an opposite stand. Our analyses confirmed a positive association between exposure to disagreement

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and respondents’ ability to generate reasons why other people might disagree with them. This association withstood the application of strong controls in multivariate analyses, and indeed appeared only more consistent as we investigated alternative explanations. Of particular note is the fact that disagreement with acquaintances—disagreement encountered not among family and close friends in one’s “private” sphere, but instead in the “public” sphere outside the home—is the apparent stimulus to forming an understanding of others. There are of course a number of limitations to this study. The data analyzed here are cross sectional in nature, leaving issues of causality (or reciprocal causality) completely ambiguous. The independent variables used in our multivariate model (namely, knowledge, political involvement, and conversation) are themselves interrelated, most likely in complex causal fashion. At this point, we lack the necessary empirical data, and perhaps sufficient theoretical guidance as well, to sort out issues of causal direction with great clarity. The larger project from which these baseline data were drawn—Electronic Deliberation 2000, a project of the Annenberg Public Policy Center at the University of Pennsylvania—has been designed as a year-long experimental study to gain greater empirical leverage on the hypothesis that public deliberation and disagreement foster higher quality opinion. Central to the project is the creation of on-line discussion groups, among a representative sample of Americans, with the aim of creating variance in group disagreement that can be directly observed and tested as a source of subsequent gains in argument repertoire, understanding of the “other,” and related aspects of deliberative opinion. We view our present work as a first step in a more prolonged investigation of deliberation and its effects. Despite its various limitations, this analysis leaves us with some initial indications that conversation, deliberation, and disagreement may indeed be related in ways predicted by democratic theorists. Nor do we believe the pattern of findings observed is a unique product of our Web-based survey techniques: Indeed, analyses of telephone survey data reported by Mutz (2001), which drew from similar albeit not identical measures and models, largely conform to our observations. The results appear entirely consistent with the argument that “civility,” in particular the ability to produce arguments that others might offer against one’s personal point of view, may indeed be fostered by heightened exposure to disagreement. These findings comprise a reasonable argument in support of what we might call the “value-of-disagreement” thesis. Still, as the evidence is far from conclusive, it seems a matter well suited to further research, discussion, and deliberation.

Notes 1. The Knowledge Networks panel sample begins with a list-assisted RDD sample provided by Survey Sampling, Inc. (SSI). Samples are acquired approximately once a month to ensure that they are drawn from up-to-date databases. Numbers in the SSI sample are then matched against a database of numbers known to be in the WebTV network. These numbers are then contacted, and households are asked to participate as members of the Knowledge Networks panel. In exchange for completing surveys (approximately 40 minutes of cumulative survey time per household per month), panelists receive WebTV equipment and access free of charge. The recruitment process results in a response rate of approximately 55% to 60%. It produces a sample of American households that closely approximates the population at large, with a very slight underrepresentation of minorities and the elderly (Knowledge Networks, 2000). In February 2000, a random sample of American citizens age 18 and older (N = 3,967) was drawn from the Knowledge Networks panel, with the intention of recruiting participants for the Electronic Dialogue 2000 project. The initial, recruitment survey provided a brief description of the project, emphasized the need for a represen-


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tative sample, and included a form indicating a respondent’s consent to participate. Overall, 51% of those recruited agreed to participate. Those who consented were then sent two baseline surveys, the first from February 8 to March 10 and the second from March 10 to March 23. The surveys included extensive measures of media use, interest in the presidential campaign, general political knowledge and knowledge of the campaign, political discussion, and a wide variety of political attitudes and opinions. Completion rates for each of the baselines were approximately 90%. 2. In addition to simply counting the number of discussants, we also took into account their relationship to the main respondent. We checked whether most respondents named friends or family members as the first two discussion partners, and acquaintances as the third and fourth discussion partners. Our analysis suggests this is mostly the case. For example, of the respondents who named just one partner (n = 76), 90% named a family member or a friend, while only 8 named an acquaintance. Conversely, of the respondents who named four discussion partners (n = 940), the great majority (82%) had talked with both family members and acquaintances. 3. We obtained a similar pattern of a positive and significant association between disagreement with acquaintances and reasoning the opposite position (b = .09, p < .001) even after controlling for respondents’ network size, as a possible confound of disagreement. We interpret this finding as supporting evidence for our claim as to disagreement’s effects on reasoning diverging points of view, independent of network size.

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Appendix: Question Wording Argument Repertoire Reasons for Own Opinion and Reasons Why Others Might Disagree R1. How favorable in general are you toward the Democratic party? 1. Very favorable 2. Somewhat favorable 3. Somewhat unfavorable 4. Very unfavorable Ask R1a-b if favorable [R1(1-2)] R1a. What are the reasons you have for feeling (very/somewhat) favorable toward the Democratic party? (Please list all the reasons that come to mind) [textbox]


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R1b. What reasons do you think other people might have for feeling unfavorable toward the Democratic party? (Please list all the reasons that come to mind) [textbox] Ask R1c-d if unfavorable [R1(3-4)] R1c. What are the reasons you have for feeling (very/somewhat) unfavorable toward the Democratic party? (Please list all the reasons that come to mind) [textbox] R1d. What reasons do you think other people might have for feeling favorable toward the Democratic party? (Please list all the reasons that come to mind) [textbox] R2. How favorable in general are you toward the Republican party? 1. Very favorable 2. Somewhat favorable 3. Somewhat unfavorable 4. Very unfavorable Ask R2a-b if favorable [R2(1-2)] R2a. What are the reasons you have for feeling (very/somewhat) favorable toward the Republican party? (Please list all the reasons that come to mind) [textbox] R2b. What reasons do you think other people might have for feeling unfavorable toward the Republican party? (Please list all the reasons that come to mind) [textbox] Ask R2c-d if unfavorable [R2(3-4)] R2c. What are the reasons you have for feeling (very/somewhat) unfavorable toward the Republican party? (Please list all the reasons that come to mind) [textbox] R2d. What reasons do you think other people might have for feeling favorable toward the Republican party? (Please list all the reasons that come to mind) [textbox] Political Conversation Discussion Partners We are interested in the sorts of political information people get from talking to each other. Q1. Please give the initials of the two close friends or family members you talk with most about politics or public affairs. [4 textboxes] First person: [ ]. [ ]. Second person: [ ]. [ ]. Ask Q1a–Q1e for EACH discussant named Q1a. Would (insert initials) be best described as your . . . 1. Spouse 2. Sibling 3. Relative 4. Roommate 5. Close friend 6. Acquaintance Q2. Thinking about your other acquaintances B people at work or others you see just going about your day—please give the initials of the two acquaintances you talk with most about politics or public affairs. [4 textboxes]

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Vincent Price et al. First person: [ ]. [ ]. Second person: [ ]. [ ]. Ask Q2a–Q2d for EACH discussant named

Q2a. Would (insert initials) be best described as . . . 1. Someone at work 2. Someone at school 3. Someone from my church 4. A neighbor 5. Someone from an organization I belong to 6. A close friend or family member 7. A roommate 8. Some other acquaintance Frequency of Discussing Politics Q1b, Q2b. How many days in a typical week do you usually discuss politics with (insert initials)? [textbox, under label “Days per week,” within 0–7 range] *Asked of each one of the four discussion partners. Disagreement Q1c, Q2c. When you talk with (insert initials), how often do you disagree with their points of view? Just give us your best estimate. 1. Almost all the time 2. Most of the time 3. About half the time 4. Not much of the time 5. Almost never *Asked of each one of the four discussion partners (reverse coded).