Opinions, Perceptions, and Networks: Opinion Types and Their Origins

Peter William Hurford

Project Advisor: Paul A. Djupe Department of Political Science

Denison University Summer Scholars Project Date: 2011

Abstract: Many previous pieces in the political science literature have studied the

causes that drive political opinions in respondents, but have not checked to see

whether these cause differ when the content of the opinion differs. In this paper,

we look to see if there are different kinds of political opinions with different

underlying causes, and then look at what those causes are, with a special focus on

the influence interpersonal networks of discussion partners.

1

Peter Hurford

Opinions, Perceptions, and Networks: Opinion Types and Their Origins

Political Science has long been interested in how voters acquire political

information and use it to generate opinions and judgments about issues and people

in politics. This study of opinionation represents the most basic level of political

behavior, because it is impossible to do other political activities like vote or

discuss politics unless you have judgments and opinions to vote by or discuss.

Locating the sources of opinions would also define the field of politics much

further, such as determing whether voters get opinions from outside groups, such

from the media and campaign literature, or from discussing politics with

acquaintances.

All prior studies in opinionation have treated it as a single phenomena (but

see Djupe 2011; Gilens 2001). Most have employed a composite index formed of

opinions about policies and groups (Atkeson and Rapoport 2003; Berinsky 2002;

Delli Carpini and Keeter 1993), but some have used the absence of party

likes/dislikes (Atkeson and Rapoport 2003; Leighley 1991), some have evaluated

opinions about political figures specifically (Gimpel and Wolpert 1996), and

another has used non-neutral responses to issue questions (Jacoby 1995).

Different studies have analyzed opinionation on multiple spectrums varying

from whether the respondent will self-report certain characteristics of himself or

herself (Kim, Wyatt, and Katz 1999), the respondent’s views on how the

government should act on certain issues (Berinsky 2002; Kim, Wyatt, and Katz

1999), whether the respondent will rate a group (Djupe 2009), and whether a

respondent will like or dislike a candidate (Atkeson and Rapoport 2003; Gimpel

and Wolpert 1995) or a political party (Leighly 1991). Some studies, such as

Francis and Busch (1975) and Rapoport (1985), looked at opinions on almost all

questions in a survey that call for an opinion, regardless of what kind of

information the question is specifically calling for. In each of these studies, the

specific kinds of opinions used were stated to be representative of opinionation in

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general.

Generalizing opinionation carries the assumption that the factors causing

people to state opinions on widely different questions are the same – that

opinionation is a measure of confidence, willingness, and ability to state an

opinion, regardless of what that opinion is. However, this may not be the case.

At least one scholar has called on researchers to study different types of

opinionation, stating that “different scholars employ various measures of

information recall without considering the possibility (or the likelihood) that some

kinds of information may be more desirable or consequential than others”

(Druckman 2005: 517; also see Gilens 2001 and Djupe 2009).

It could be instead that what factors lead a respondent to feel confident

enough to rate a group may not be the same factors that lead a respondent to like a

candidate or state a view on how the government should act. If there are different

kinds of opinionation, then certain characteristics of the respondent might lead to

holding opinions on some questions but not on others. Furthermore, if different

studies into opinionation assume they are analyzing the same general concept of

opinionation, but really are looking into different types of opinions, this could

cause inconsistencies in the literature that would be resolved by recognizing

different opinion types.

An analysis of the existing literature may point to some of these differences.

For instance, in some studies the race of the respondent had no statistically

significant effect on opinionation (Gimpel and Wolpert 1995; Kim, Wyatt, and

Katz 1999), but in others being nonwhite was found to negatively affect

opinionation (Atkeson and Rapoport 2003; Francis and Busch 1975)1. Also,

some studies found age to correlate positively with opinionation (Atkeson and

Rapoport 2003; Gimpel and Wolpert 1995; Leighly 1991), one study found it to

1 Another difference that divides the two studies is that Atkeson and Rapoport (2003) and Francis and Busch (1975) include income, whereas Gimpel and Wolpert (1995) and Kim, Wyatt and Katz (1999) do not.

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correlate negatively with opinionation (Kim, Wyatt, and Katz 1999), and other

study found it to make no statistically significant change (Francis and Busch

1975). Lastly, consider gender – while being male has been found to promote

opinionation (Atkeson and Rapoport 2003; Djupe 2009; Francis and Busch

1975), in one study it was found unimportant (Kim, Wyatt, and Katz 1999).

Therefore, we pose two central questions regarding opinionation: (1) are

there different types of opinionation and (2) if so, what factors drive opinionation

in these different types? However, in order to get a meaningful answer to both

these questions, they will have to be answered simultaneously because we cannot

know for sure if there are different types of opinionation unless the different

types have different causes2, and such an analysis of the causes of opinionation

would answer the second question.

I’d suggest that this would be a good place for something like, “We focus

attention on one particularly useful determinant – personal social networks.

Drawing on a long line of literature on the political effects of social networks, we

test the specific, competing assumptions made about the provision of information

that map onto the dimensions of opinionation we assess. This also constitutes the

first tests of the effect of networks on opinionation, which also has led us to

confront some of the specific challenges network research faces in studying public

opinion.

In what follows, we will look at survey questions from the 2000 ANES that

call for opinions on different subjects, such as personal opinions on campaign

issues, perceptions of where presidential candidates (Bush and Gore) stand on

those campaign issues, personal opinions on retrospective issues, personal

opinions on government spending, and personal opinions of different groups. We

will assess if there are any differences both in amounts of opinion types held and

in the underlying causes of opinionation. We will also specifically look not just at

2 We can, however, have initial evidence if it turns out that the amount of opinionation across different questions have different means.

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the resources and characteristics of a respondent, but at their interpersonal

networks to see if the socially supplied information from political discussion

supports learning on all subjects, just some, or none at all.

Personal Resources as an Explanation for Opinionation

A search for political information is demanding to the point that few people

will undertake it (Downs 1957). This means that those who have more resources

will face a less costly search and therefore will be more likely to possess political

information and the political opinions that follow from that information.

Studies in opinionation have repeatedly confirmed this. People with lower

resources, such as less education and less income, have been found to have fewer

opinions (Krosnick and Milburn 1990; Verba, Schlozman, and Brady 1995; Zaller

1992). Nonwhites have also been known as being disadvantaged with regard to

resources and therefore also have been less likely to express opinions (Francis and

Busch 1975).

Sometimes resource differences leading to lower opinionation are

sociological and psychological rather than material, and persist even when

controlling for education and income. For instance, women also hold fewer

opinions than men and this trend has continued despite women having much

greater access to political resources and engaging in far more political activity

than in previous decades (Atkeson and Rapoport 2003; Burns, Schlozman, and

Verba 2001; Djupe 2011; Rapoport 1982, 1985).

Environmental Factors as Causes of Opinionation

Additionally, opinionation is related to not just resources but characteristics

of the environment in which the opinions are formed. For instance, opinionation

rises when people pay more attention to campaigns – opinionation is not constant

from election to election and instead varies based on the vibrancy of the campaign

environment (Atkeson and Rapoport 2003; Caldeira and Smith 1996; Krosnick

and Milburn 1990; Zaller 1992). Opinionation also increases when people pay

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more attention to media (Kim, Wyatt, and Katz 1999).

Interpersonal Networks as Causes of Opinionation

While the existing literature has discussed how environmental factors can

lead to opinionation and has touched on how political discussion correlates with

opinionation and opinion quality (Djupe 2009; Kim, Wyatt, and Katz 1999), little

has been written on how a respondent’s interpersonal network, the groups of

people which the respondent discusses politics with, could promote opinion

holding.

This is not to say that there hasn’t been discussion of connections between

opinions and interpersonal networks. Analysis of networks has also typically

focused on how network factors such as amount of general disagreement (Djupe

2009; Huckfeldt et al. 2004; Huckfeldt and Mendez 2005; Kenny 1998; Mutz

2006; Sokhey and McClurg 2008), amount of political expertise (Huckfelt 2001;

McClurg 2006), frequency of political discussion (McClurg 2003), and size

(Kenny 1992; McClurg 2003; McClurg 2006) have affected opinion latency,

opinion direction, and political participation.

Results have shown those with larger and more politically knowledgeable

networks strongly correlates with political participation (Kenny 1992; McClurg

2003; McClurg 2006) and that political disagreement strongly influences voting

for the candidate who matches the voter’s preferences (Sokhey and McClurg

2008) but may also cause people to avoid political activity altogether (Huckfeldt

and Mendez 2005; Mutz 2006). These measures, especially participation, are

correlated with opinionation, but no study of social networks has investigated

opinionation specifically.

It turns out that studying the effects of networks on different types of

opinionation is useful in helping to sort out how networks affect political

behavior. There are competing assumptions made in the literature about how, for

instance, networks affect political participation. Given the conceptual differences

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between opinionation types, we can assess whether networks help provide

information about transient figures in electoral politics, clarify stances on policy,

help position the voter amidst a sea of reference groups, and/or help them

understand the role of political institutions in shaping the current world.

While the search for information is aided by more resources, it can also be

aided by additional heuristics that an otherwise disadvantaged respondent could

use to approximate this information, such as using endorsements from interest

groups to emulate the behavior of well-informed voters (Lupia 1994) or socially

supplied information from political discussion (Djupe 2009; Downs 1957; Kim,

Wyatt, and Katz 1999; Sokhey and Djupe 2010).

The key focus regarding networks is the suggestion that individuals

involved in the political discussion will acquire political information (Berelson et

al. 1954; Huckfeldt et al. 1995; Levine 2005) with the implication that this

information can then be used to form opinions (Djupe 2009) – the idea that

people learn from their networks.

However, this brings up several questions, such as what exactly people are

learning from these networks, and which, if any, kinds of opinions networks

promote. Do networks help build opinions regardless of what kind of content the

question calls for, or are networks only helpful in acquiring certain kinds of

information sufficient to hold opinions?

Data and Measurement

To find out whether or not there are multiple kinds of opinionation, and to

find out what the underlying causes of opinionation are, data was collected from

the 2000 National Election Survey3. If there truly are multiple, distinct kinds

of opinionation, these factors will drive each kind of opinionation differently. For

instance, perhaps some factor will correlate significantly with personal

opinionation, but won’t matter at all when it comes to determining candidate 3 We chose the 2000 NES specifically because it contains questions that allow us to analyze network effects.

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opinionation.

Personal Opinionation

In this survey respondents were asked, among other things, to provide

numerous opinions on a wide variety of questions. One set of questions asked

respondents for their personal stances on many campaign issues, such as whether

or not abortion should be legal, whether it should be more or less difficult to get a

gun, whether environmental regulation should be increased or decreased, whether

defense spending should be increased or decreased, whether or not the

government should guarantee jobs, whether the government should provide aid to

blacks, and the ideology of the respondent.

In addition to the standard “don’t know” and “refuse” options, on the last

five issues respondents were specifically given the choice of saying they “haven’t

thought much” on the issue instead of giving a stance, which we counted as no

opinion. When “haven’t thought much” was an option for the issue, the amount of

respondents failing to report an opinion increased, except on ideology, consistent

with other studies that show an increase in nonresponse when such an option is

added (Bishop et al. 1980; Shuman and Presser 1981).

Candidate Opinionation

Additionally, all of the respondents were asked of their perception of the

candidates – George W. Bush and Al Gore – on these same seven issues, for a

total of fourteen possible perceptions. On these fourteen questions, there was no

“haven’t thought much” option, so all fourteen questions were identical in form.

Table 1 shows the levels of personal and candidate opinionation on each

issue. Overall, there are no statistically significant differences between

perceptions of the two candidates on the same issue, but there is a statistically

significant difference between the amount of respondents holding a candidate

perception on an issue and the amount of respondents holding a personal opinion

on the issue.

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Retrospective Opinionation

Another source of questions that call for opinions involve issues where the

voter is asked about retrospective issues; questions about how America has

changed, for better or for worse. The questions in the 2000 NES focused

specifically on four areas – the economy, national security, the amount of crime,

and “moral decay” – asking the respondent to report whether America got better

or worse in this area, and then asking whether the respondent thinks Clinton made

America better or worse in this area.

Voting behavior literature has long suggested that elections are referendums

on the current administration, where citizens cast their vote based on how the

most recent administration preformed (Key 1965). However, if voters do not

have opinions on these retrospective issues, for example failing to connect the

Clinton administration to how America has changed one way or another, they

cannot vote retrospectively.

Table 2 shows the amount of retrospective opinionation viewed with and

without considering Clinton. Respondents appear highly opinionated on these

issues and there is no statistically significant difference between opinionation

levels on the initial retrospective issues and those that ask the respondent to assign

blame or praise to Clinton.

Spending Opinionation

A fourth set of questions that call for opinions is the battery of questions

asking for the respondent’s opinion on government spending – whether spending

should be raised or lowered on highway repair, welfare, AIDS research, foreign

aid, food stamps, aid to the poor, social security, environmental regulation, public

schools, crime prevention, child care, illegal immigration prevention, and black

aid. On these issues, respondents also appear very highly opinionated, with

opinion levels on individual issues seen in Table 3.

Group Opinionation

A fifth set of questions call for opinions on groups using thermometers that

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measure the respondent’s “warmness” or “coolness” to 24 different groups of

people, whether that group is an institution of the government, an organization, or

defined by a common characteristic, such as liberals, people on welfare, or blacks.

Table 4 shows a complete list of these groups and the opinionation on each of

them, which run above 90% on every group except fundamentalists (%) and The

Christian Coalition (%).

Measuring Means

An initial way to determine whether these four types of opinionation really

are different is to see if the mean amount of opinions held in each type is different

to a statistically significant degree. Because all four types had a different amount

of questions asked, the only way to do this is to convert each group into a

percentage, where respondents are measured by a percent of all possible opinions

on each of the four types.

The mean percentage of opinionation held across all five types is shown in

Figure 1, which reveals that there are statistically significant differences among

personal opinionation, candidate opinionation, and group opinionation; with each

those three different from retrospective opinionation and spending opinionation,

which do not themselves have statistically significant differences in the mean

amount of opinions held.4 However, a difference in means is not enough to

indicate that each type of opinionation is distinct, because each type could have

the same causes, just with different strengths.

Looking at Networks

The 2000 NES data allows us to evaluate the networks of the respondents,

by asking the respondents to report up to four people they discuss politics with.

74.27% of respondents have a network, which means they indicated political

discussions with at least one person. Additional questions ask the respondent how

much they discuss politics with each person they identified, giving us a measure

of discussion frequency; whether the respondent knew other people in the

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network, giving us a measure of network insularity; how much they thought each

partner knew about politics, giving us a measure of expertise within the network;

and who their partner voted for president, which indicates disagreement if the

vote is different from the respondent.

However, in creating these variables we have a choice – do we include in

our measurements of network characteristics all of those who have no network,

and therefore would experience no discussion frequency, no network insularity,

no network expertise, and no network disagreement? This choice causes a

dilemma, for if we do include these people without networks, all of our

measurements become collinear to the point that they cannot all be included in the

same model without invalidating each other. However, if we do not include these

people without networks, we won’t have any idea whether the possession of a

network causes a respondent to have access to political information or not, and

therefore indicate whether networks can stimulate opinion holding.

Networks and Resource Effects

In addition to the dilemma surrounding those without networks, we have

another problem – while ideally network analysis would provide a way to look at

how respondents learn information independent from resource effects, it doesn’t

work this way as people have a tendency to associate with people like themselves

(Huckfeldt and Sprague 1995; Lazarsfeld and Merton 1954), thus forming social

networks with similar demographics (Berelson et al. 1954). This means that any

resource effects present within the respondent will be present within the network

as well. For instance, those with low education will have less access to educated

respondents, and thus miss out on network expertise. Also, those who are

uninvolved in politics as a whole are less likely to have sought out and created a

political network.

This problem can be seen in the data. Figure 2 shows that those respondents

who have a network also consume significantly more media, are more partisan,

and are significantly more likely to be politically interested. This is currently an

unsolved problem in political science for us to note, and work is underway to

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explore more advanced models to take this into account, such as models that

consider “treatment effects” in non-experimental data.

Creating Models

To look at the actual causes among opinionation, we will create and

compare five different models, with each model containing one type of

opinionation as the dependent variable and the same potential causes as the

independent variables.

Because of the skewed distribution of opinionation among all five, with

many more holding all possible opinions than no opinions, we will represent

personal opinionation5, candidate opinionation, and group opinionation in

quartiles and create ordered logistic models for these. Retrospective opinionation

and spending opinionation, where more than 80% of respondents held all possible

opinions, will be dichotomized into a variable that represents whether the

respondent held all possible opinions or not.

The independent variables will include many of the resources discussed

earlier and thought to affect opinionation, such as the respondent’s age (eg.,

Atkeson and Rapoport 2003; Huckfeldt and Mendez 2004; Gimpel and Wolpert

1995), gender (e.g., Djupe 2009; Gimpel and Wolpert 1995; Leighly 1991),

education (eg., Atkeson et al. 2003; Djupe 2009; Huckfeldt and Mendez 2004),

whether the respondent is employed, whether the respondent is a homemaker

(Atkeson and Rapoport 2003), the respondent’s income (Atkeson and Rapoport

2003), the respondent’s race (Atkeson and Rapoport 2003; Kim, Wyatt, and Katz

1999; Leighly 1991), the respondent’s sense of political interest (eg. Atkeson and

Rapoport 2003; Djupe 2009; Leighley 1991), the extremity of the respondent’s

partisanship (eg., Atkeson and Rapoport 2003; Djupe 2009; Huckfeldt and

Mendez 2004), and political activity levels (Atkeson and Rapoport 2003).

5 Only the “haven’t thought much” questions (environmental regulation, defense spending, ideology, government jobs, and black aid) were considered because they are methodologically distinct from the two questions where “haven’t thought much” was not an option (abortion and gun control), which could skew the results. Since the questions involving “haven’t thought much” were more numerous, they made for a better model.

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Additionally, the independent variables will include measures of the

respondent’s political environment, such as how often the respondent was

contacted by campaigners (Djupe 2011), how much media the respondent

consumes (eg. Djupe 2009; Huckfeldt and Mendez 2004; Leighley 1991; Wyatt

and whoever), and whether or not the respondent lives in a battleground state – a

state where the election was decided by less than 5% – which would typically be a

more vibrant and politicized environment (Zaller 1992).

Analyzing Model Results

When it comes to candidate opinionation, shown in Table 5, the amount of

perceptions held about candidate opinions is increased by age, being male, having a

higher income, being more partisan, consuming media, self-reporting political

interest, and participating in politics. Being more educated, living in a

battleground state, being employed, being a homemaker, being contacted by

outside groups, and having a network do nothing statistically significant to affect

candidate opinionation.

Personal opinionation (Table 6), however, is increased by being male, being

educated, having a higher income, being white, consuming media, self-reporting

political interest, and having a network; with age, being partisan, participating

politically, living in a battleground state, being employed, being a homemaker, and

being contacted by outside groups having no statistically significant effect.

Retrospective opinionation (Table 7) is increased by being male, consuming

media, and having a network; and is not affected by age, income, race, political

interest, partisanship, participation, battleground state status, employment,

homemaker status, or being contacted.

The model of spending opinionation (Table 8) was much worse and no

variables could be shown to have a statistically significant effect, with the

exception of age, which makes spending opinionation go up.

Lastly, group opinionation (Table 9) is increased by being male, being

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educated, consuming media, participating in politics, and having a network;

decreased by age; and not effected by income, race, political interest, partisanship,

battleground state status, employment, homemaker status, or outside contact.

Conclusions

Overall, our results answer both our questions. First, we demonstrate that

since all five types of opinionation have distinct underlying causes, there are

multiple types of opinionation and future analysis must take this into account.

However, when we look at which factors correlate with which types of

opinionation, we can reveal an explanatory narrative of how information is used to

form different types of opinionation, answering our second question. The three

big players when it comes to explaining opinionation seems to be resource effects,

media consumption, and having a network.

Resource Effects

Resource effects seem to increase opinionation regardless of the kind,

demonstrating that information truly is less difficult to obtain for the advantaged

members of society, regardless of what that information is. Democracy is

profoundly affected by inequalities in opinionation brought on by resource

effects, since those who lack opinions will not be able to deliberate over policy

and make informed choices (Atkeson and Rapoport 2003; Huckfeldt and Sprague

1995), which will prevent them from participating in politics (Atkeson and

Rappoport 2003; Leighley 1991).

Additionally, these deep roots in resource inequalities strongly indicate that

people who are resource disadvantaged will also be politically disadvantaged,

unable to politically participate. This creates a vicious cycle, as those who are

disadvantaged are the ones who most need to use the political process to get help,

yet are also most likely to be unable to make use of the political process. Our

data confirms that disadvantaged people are also less likely to be able to form

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social networks, and when they do, they are less likely to have a network that is

as effective, lacking in discussion frequency and political expertise.

Media Effects

With the exception of spending opinionation which remains inexplicable for

reasons unknown, media consumption also increases opinions across the board,

regardless of the content of opinion called for by the question. This may either

indicate that the media is effective in disseminating the wide variety of information

necessary to form all sorts of opinions, that the people who consume media are

just more likely to be the type of people interested in forming opinions (Kim,

Wyatt, and Katz 1, or some combination of the two.

Network Effects

Kim, Wyatt, and Katz (1999) wrote that “conversation is the soul of

democracy”, and this seems true here as well, though with some reservation.

Having a network increases group, retrospective, and personal opinionation, but

has no effect on spending or candidate opinionation.

This helps confirm the theory that political discussion does not help people

gain general information in the same way that media consumption does, but that

personal deliberation helps those involved connect their values to a rating of key

campaign issues, retrospective issues, or various groups. Specifically, a

discussant may know some facts about abortion, but may not turn these facts into

a personally held opinion until he or she is asked to declare his or her opinion to

others, or adopts opinions from others that are consistent with known facts (see

McPhee 1963).

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Verba, Sidney, Kay Lehman Schlozman, and Henry E. Brady. 1995. Voice and

equality: Civic voluntarism in American politics. Cambridge, MA:

Harvard University Press.

Zaller, John R. 1992. The nature and origins of mass opinion. Cambridge, UK:

Cambridge University Press.

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Table 1: Percent of Respondents Holding a Personal/Candidate Opinion on Seven Issues, with T-Tests

Issue Personal Opinion?

Gore Perception?

Bush Perception?

t-test (Per/Gore)

t-test (Gore/Bush)

Gun Control 99.45 83.45 82.84 <0.0001 0.6251 Abortion 99.00 74.76 72.61 <0.0001 0.1408 Defense Spending 78.64 77.64 79.03 0.4689 0.3129 Environmental Reg. 74.65 74.99 72.61 0.8192 0.1039 Ideology 98.95 86.83 86.55 <0.0001 0.8066 Providing Jobs 88.27 80.13 79.58 <0.0001 0.6784 Providing Black Aid 86.39 83.45 82.84 <0.0001 0.3401 N=1807, Source: 2000 NES; The bottom five issues have “haven’t thought much” as a response option whereas the top two do not.

Table 2: Percent of Respondents Holding an Opinion on Retrospective Issues

Perception? Issue Personal Clinton t-test

State of Economy 98.51 97.40 0.0188 State of Security 97.07 97.01 0.9219 Amount of Crime 97.40 96.29 0.0570 Moral Decay 98.28 97.73 0.2340 N=1678, Source: 2000 NES

Table 3: Percent of Respondents Holding an Opinion on Spending Issues Issue % of Resp.

Highway Spending 98.9 Welfare 98.23 AIDS Research 97.95 Foreign Aid 97.57 Food Stamps 97.18 Aid to the Poor 98.07 Social Security 97.90 Environmental Reg. 98.11 Public Schools 99.50 Crime Prevention 99.06 Child Care 97.78 Illegal Immigration 97.18 Black Aid 95.08

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N=1807, Source: 2000 NES

Table 4: Percent of Respondents Holding an Opinion on Groups Issue % of Resp.

Supreme Court 95.43 Congress 95.82 Military 97.56 Federal Government 96.21 Blacks 93.38 Whites 93.83 Conservatives 92.54 Liberals 92.35 Unions 94.41 Big Business 96.14 Poor 92.99 People on Welfare 92.99 Hispanics 92.48 Fundamentalists 82.77 Women’s Movement 94.92 Old People 97.17 Environmentalists 95.43 Gays 93.12 The Christian Coalition 81.35 Catholics 92.93 Jews 91.25 Protestants 91.25 Feminists 91.77 Asians 91.13 N=1555, Source: 2000 NES Table 5: Ordered Logistic Model of Total Amount of Perceptions Stated of Candidates on the Seven Issues (Split into quartiles)

Variable Coeff. Std. Err. p-value R.’s Age -0.014 0.004 <0.001 Is R. Male? +0.861 0.115 <0.001 R.’s Education +0.039 0.025 0.118 R.’s Income +0.060 0.022 0.007 Is R. White? -0.245 0.135 0.069 R.’s Political Interest +0.165 0.043 <0.001 R.’s Partisanship +0.197 0.054 <0.001

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R.’s Media Consumption +0.212 0.058 <0.001 R.’s # of Votes +0.251 0.084 0.003 Is R. In Battleground? -0.139 0.113 0.221 Is R. Employed? -0.216 0.139 0.115 Is R. a Homemaker? -0.017 0.200 0.931 R.’s Contact -0.011 0.047 0.822 Does R. Have Network? +0.177 0.132 0.181 /cut1 -0.771 0.458 /cut2 +0.310 0.457 /cut3 +1.348 0.458

N=1357, LogLikelihood: -1615.6593, LRX2: 274.31, Sig-X2: <0.0001, Psuedo-R2: 0.0782, Source: 2000 NES Table 6: Ordered Logisitic Model of Total Opinions Stated on the Five Issues Where “Haven’t Thought Much” Was an Option (Split into quartiles)

Variable Coeff. Std. Err. p-value R.’s Age +0.002 0.004 0.702 Is R. Male? +0.738 0.125 <0.001 R.’s Education +0.099 0.026 <0.001 R.’s Income +0.066 0.027 0.012 Is R. White? +0.629 0.137 <0.001 R.’s Political Interest +0.132 0.048 0.005 R.’s Partisanship -0.069 0.059 0.242 R.’s Media Consumption +0.204 0.063 0.001 R.’s # of Votes +0.082 0.087 0.342 Is R. In Battleground? -0.192 0.124 0.122 Is R. Employed? -0.078 0.148 0.600 Is R. a Homemaker? +0.268 0.213 0.208 R.’s Contact +0.015 0.051 0.758 Does R. Have Network? +0.447 0.137 0.001 /cut1 -0.116 0.485 /cut2 +1.223 0.481 /cut3 +2.552 0.486

N=1357, LogLikelihood: -1322.6592, LRX2: 239.47, Sig-X2: <0.0001, Psuedo-R2: 0.0830, Source: 2000 NES Table 7: Logistic Model of Total Perceptions Stated on Retrospective Issues (Dichotomized)

Variable Coeff. Std. Err. p-value R.’s Age -0.013 0.007 0.052

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Is R. Male? +0.826 0.234 <0.001 R.’s Education +0.040 0.042 0.345 R.’s Income +0.014 0.046 0.758 Is R. White? -0.156 0.250 0.533 R.’s Political Interest +0.096 0.081 0.233 R.’s Partisanship -0.068 0.102 0.506 R.’s Media Consumption +0.247 0.109 0.023 R.’s # of Votes +0.278 0.143 0.053 Is R. In Battleground? +0.263 0.223 0.237 Is R. Employed? -0.142 0.264 0.589 Is R. a Homemaker? -0.326 0.323 0.313 R.’s Contact -0.097 0.090 0.280 Does R. Have Network? +0.551 0.222 0.013 _cons +1.469 0.792 0.064

N=1357, LogLikelihood = -384.38809, LRX2: 74.64, Sig-X2: <0.0001, Pseudo-R2: 0.0885 Table 8: Logistic Model of Total Perceptions Stated on Spending Issues (Dichotomized)

Variable Coeff. Std. Err. p-value R.’s Age -0.025 0.006 <0.001 Is R. Male? +0.242 0.172 0.160 R.’s Education -0.043 0.036 0.232 R.’s Income -0.051 0.028 0.072 Is R. White? -0.027 0.212 0.898 R.’s Political Interest -0.009 0.066 0.894 R.’s Partisanship +0.014 0.083 0.869 R.’s Media Consumption +0.052 0.088 0.550 R.’s # of Votes +0.173 0.126 0.169 Is R. In Battleground? -0.402 0.166 0.016 Is R. Employed? +0.059 0.209 0.780 Is R. a Homemaker? -0.026 0.309 0.932 R.’s Contact +0.094 0.072 0.191 Does R. Have Network? -0.003 0.200 0.987 _cons +3.349 0.710 <0.001

N=1357, LogLikelihood = -545.00613, LRX2: 37.79, Sig-X2: 0.0006, Pseudo-R2: 0.0335 Table 9: Ordered Logistic Model of Total Opinions Stated on Groups

Variable Coeff. Std. Err. p-value

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R.’s Age -0.023 0.004 <0.001 Is R. Male? +0.571 0.126 <0.001 R.’s Education +0.121 0.026 <0.001 R.’s Income +0.008 0.025 0.754 Is R. White? +0.154 0.147 0.295 R.’s Political Interest -0.014 0.048 0.764 R.’s Partisanship +0.023 0.059 0.693 R.’s Media Consumption +0.127 0.064 0.047 R.’s # of Votes +0.348 0.089 <0.001 Is R. In Battleground? +0.006 0.126 0.962 Is R. Employed? -0.039 0.150 0.796 Is R. a Homemaker? -0.021 0.212 0.920 R.’s Contact -0.067 0.052 0.200 Does R. Have Network? +0.314 0.138 0.023 /cut1 -0.353 0.494 /cut2 +0.687 0.493 /cut3 +1.375 0.494 N=1357, LogLikelihood: -1338.2374, LRX2: 182.07, Sig-X2: <0.0001, Psuedo-R2: 0.0637, Source: 2000 NES

Figure 1: Graph of Mean Percentage of Opinions Held Among All Four Types of Opinionation

Source: 2000 NES

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Figure 2: Resources of Respondent by Whether or Not the Respondent Has a Network

Source: 2000 NES, All variables are displayed as a percent of maximum

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