Loud and Clear: Effects of Homogenous and Extreme Partisan Media Diets
Douglas M. Allen and Devra C. Moehler
The Annenberg School for Communication University of Pennsylvania
[Please do not cite without permission from the authors]
This version: July 25, 2014
Keywords: Public Opinion; Media Effects (Other); Political Psychology; Participation; Quantitative - Survey
Abstract: Though the explosion of cable television and talk-radio programming allows individuals to select opinionated media from only one side of the political spectrum, most Americans consume a mix of programming, with varying degrees of bias across the ideological spectrum. Research on partisan media typically evaluates the independent effects of likeminded, crosscutting and neutral exposure in isolation, yet the effects of media consumption likely depend on an individual’s overall media diet in aggregate. The ambiguity of theoretical approaches regarding mixed media diets suggests the need for empirical research. One line of reasoning predicts that slanted diets will generate more extreme attitudes and greater participation than evenhanded diets, while a second line of reasoning predicts the opposite. We test the effects of media diet composition on issue attitudes and campaign participation using the 2008 National Annenberg Election Survey (NAES). To do so, we construct of novel measure of the relative balance of each respondent’s media diet based on questions about their consumption of 73 different entertainment and news programs. Within-subjects and matching analyses indicate that slanted media diets increase campaign participation, but not issue polarization. This article provides a theoretical and empirical basis for future research on the political effects of real-world media consumption.
Acknowledgements: We are deeply indebted to the guidance and thoughtful feedback provided by Susanna Dilliplane, Ted Brader, Matt Levendusky, Marc Meredith, Andrew Therriault, Dannie Stockmann, and Rosario Aguilar Pariente. We are especially grateful for the work that Elizabeth Roodhouse put into the project.
Corresponding Author: Douglas M. Allen is a PhD Student at the Annenberg School for Communication Address: 3620 Walnut St., Philadelphia, PA, 19104. E-mail: [email protected]
The recent proliferation of media sources provides individuals with more choice than
ever before. Increased competition for audiences motivates targeted programming (both
entertainment and news) that eschews norms of balanced presentation in favor of opinionated or
satirical commentary by openly partisan hosts (Williams & Delli Carpini 2011; Groseclose &
Milyo 2005; Mullainathan & Shleifer 2005). These changes in the information environment
allow citizens to select a media diet that contains more ideologically homogenous and extreme
programming than was possible when network channels dominated the airwaves (Iyengar &
Hahn 2009; Stroud 2011). However, selective exposure to likeminded programming remains
uneven and incomplete.i
Most current research on partisan media examines the independent effects of likeminded
(favoring one’s own side), crosscutting (favoring the other side), or neutral media exposure.ii
Scholars test the effects of exposure to one type of media while controlling for exposure to
others. We theorize that the effect of media consumption may depend on the relative mix of
likeminded, crosscutting and neutral media consumed, and not just by the amount of each
independently. Although Americans now consume a mix of programming, with varying degrees
of bias and from opposite ends of the ideological spectrum, scholars have not yet examined the
consequences of exposure to one-sided versus diverse perspectives in an individual’s media diet
The effects of unbalanced as compared to balanced media diets are theoretically
ambiguous. We identify one line of reasoning that predicts that exposure to an “echo chamber”
of strident likeminded media voices polarizes and mobilizes while a more balanced exposure to
opposing perspectives (whether in crosscutting partisan media or in neutral media) tempers
political attitudes and action. However, a second line of reasoning predicts the exact opposite:
exposure to conflicting views may be more polarizing and mobilizing than exposure to familiar arguments. Contact with the opposition through the media can increase extremity by inviting counterargument, intensifying in-/out-group sentiments and eliciting negative emotions. A media diet without conflicting arguments may be a less potent force for extremism and action.
Our research is designed to adjudicate between these diametrically opposed predictions about the effects of unified versus mixed media messages. We use public opinion data from the
2008 National Annenberg Election Survey (NAES) to develop a single measure of the relative balance of each respondent’s media diet based on questions about 73 different news and entertainment shows. This novel measure allows us to test the effects of media diet composition on issue attitudes and campaign participation.
Our fixed-effects and matching analyses show that more ideologically slanted media diets are associated with greater increases in participation over the course of the campaign than balanced media diets. People who consume a diet consisting primarily of likeminded news and entertainment programing become more involved in the political process than those who hear mostly muted calls to action from moderate mainstream broadcasts or receive mixed signals from both sides of the spectrum. We find no evidence that imbalanced exposure to strident partisan voices causes polarization of attitudes on prominent issues (the Iraq War, immigration, free trade). Our results suggest that one-sided partisan media consumption may mobilize without polarizing
The next section of this article elaborates on the perspectives outlined above to develop hypotheses about the effects of media diet balance on attitudinal polarization and political participation. We then describe our measurement approach, data and research strategy. We
present the results from fixed-effects regression analyses followed by the results of matching
analyses, and conclude with a discussion of key findings from the research.
Effects of Homogenous versus Heterogeneous Media Diets
The main innovation of this article is to theorize about, and test, the effects of composite
media diets rather than the effects of likeminded, neutral and crosscutting exposure in isolation.
We develop our own theories of the political effects of unified versus diverse consumption given
the lack of scholarship on total media diet composition. To do so, we build off studies of the
independent effects of the three types of mediaiii and analogous research on interpersonal contact, motivated reasoning, and social identity theory. These literatures suggest contradictory
effects of exposure to unbalanced as compared to balanced media diets. The first view predicts
that slanted diets will generate more extreme attitudes and greater participation than evenhanded
diets, while the second predicts the opposite.iv
Effects on issue polarization
The first line of reasoning holds that exposure to an echo chamber of exclusively
likeminded partisan media exacerbates partisan preferences while exposure to a mixture of arguments moderates them (Sunstein 2009; Garrett et al. 2014). According to this perspective,
unbalanced media diets should lead to greater attitude extremity than balanced ones.
Research on heterogeneous social networks and interpersonal discussions highlights how
interactions with people who hold different views can foster mutual understanding, reevaluation
of positions, and moderation (Huckfeldt et al. 2004; Klofstad et al. 2013; Mutz 2006), which
dovetails with democratic theorists who reason that democracy is strengthened when citizens are
exposed to myriad views (Barber 1984; Habermas 1989; Mill [1859] 1999; Gutmann & Thompson
1996). By this logic, exposure to competing policy arguments will encourage informed decision-
making. Individuals with balanced diets are better able to distinguish facts from opinions and more cognizant of bias in likeminded sources. These individuals are persuaded by convincing
arguments from the other side, and less susceptible to familiar arguments from their own side
(Dilliplane 2011). The net effect of exposure to diverse viewpoints, whether through exposure to
neutral media that presents competing arguments within a single program (internal diversity) or to
partisan media from competing parties (external diversity), is to moderate attitudes,.
While heterogeneous exposure is expected to moderate, this perspective suggests that
homogeneous diets will reinforce and strengthen initial preferences. Partisan media outlets frame,
spin, or slant coverage to support a particular perspective, highlighting favorable information while
discrediting and denigrating alternative views, organizations and personalities (Jamieson &
Cappella 2008). Those who are only exposed to attitude-congruent media can amass evidence to
justify their beliefs without hearing arguments to the contrary (Levendusky 2013; Jamieson &
Cappella 2008; Sunstein 2009). The presentation of uncontested arguments may also be more
convincing than these same arguments presented alongside critiques (Garrett et al. 2014).
According to this line of reasoning, consumption of only strident likeminded partisan media
increases attitude extremity, especially relative to a more diversified diet.
Based on these arguments, we expect the following hypothesis to be true:
H1a: Exposure to unbalanced partisan media diets results in more extreme attitudes on key policy issues than exposure to balanced media diets.
The first line of reasoning (described above) assumes that individuals are open to
opposing arguments and can be persuaded to change their minds, or at least temper convictions,
based on new evidence (Conroy-Krutz and Moehler n.d.; Feldman 2011; Levendusky 2013).
However, much of the literature on partisan media assumes that individuals are motivated by
directional goals rather than accuracy goals. If individuals exposed to discordant views react
defensively, then unbalanced exposure should lead to greater attitude extremity than balanced
exposure. Thus, the second line of reasoning (described below) predicts the opposite of the first.
The theory of partisan-motivated reasoning posits that individuals seek to minimize cognitive dissonance by engaging in biased information processing (Taber and Lodge 2006).
When presented with oppositional arguments, partisans counter-argue to discredit discordant perspectives and bolster their views. By mounting arguments in defense of their issue positions, partisans become more convinced of the probity of their own side (Kunda 1990; Taber and
Lodge 2006; Arceneaux and Johnson 2013; Levendusky 2013). Some partisans even seek out crosscutting media to act as a foil for their own beliefs (Garrett 2009). The juxtaposition of competing arguments in internally diverse programming can also strengthen initial preferences by highlighting differences between the two sides.
Furthermore, social identity theory suggests that individuals may envision themselves as members of a community of likeminded media personalities and viewers. Contact with spokespeople from the other party in crosscutting or neutral media may increase the salience of partisan identities, exacerbate in/out-group sentiments, stimulate anxiety, and fuel intolerance.
As a result, individuals with mixed media diets may reject candidates and policies perceived to be associated with the out-group in favor of the in-group (Arceneaux and Johnson 2013;
Levendusky 2013). This effect may be especially pronounced when the out-group is denigrated
and the in-group praised, as is common in partisan media (Jamieson & Cappella 2008).
Theories of partisan motivated reasoning and social identity theory predict that a
balanced media diet will be less polarizing than an imbalanced diet that evoke emotions and
defensive reasoning. When individuals are primarily exposed to their own side (as is the case
with most homogeneous diets) they react less strongly than when exposed to opposing
perspectives in neutral media or a mixture of likeminded and crosscutting media. This reasoning
suggests a hypothesis that directly contradicts the first:
H1b: Exposure to unbalanced partisan media diets results in less extreme attitudes on key policy issues than exposure to balanced media diets.
Effects on campaign participation
There are fewer studies about the effect of partisan media on participation than on
attitudes. Although less extensive, the literature on partisan media and mobilization, and related
bodies of work, lead to competing expectations about which media diets will generate greater
participation.
The first perspective suggests that exposure to unified and outspoken partisan voices in
likeminded media may mobilize citizens to participate in politics, whereas exposure to diverse
perspectives may dampen participation (Conroy-Krutz and Moehler n.d.; DellaVigna & Kaplan
2007; Dilliplane 2011; Jamieson & Cappella 2008; Stroud 2011; van Kempen 2007;). If
heterogeneous media moderate attitudes, and attitude strength drives participation, then we
should expect less participation when media consumption is mixed than when individuals are in
the echo chamber of likeminded media. In addition, favorable contact with out-group members through the media could increase tolerance, enhance trust in opposition politicians, and reduce anxiety about the consequences of losing (Levendusky 2013). Thus, individuals exposed to
spokespeople for the other side may have less reason to actively defend their own side. Finally,
audiences of likeminded media may be exposed to more emotional appeals to get involved on behalf of their candidate or cause, and provided with more information on how to get involved, than audiences of neutral or mixed programming (Jamieson & Cappella 2008).
This first perspective finds support from empirical studies of cross-pressuring through interpersonal communication, social network location, or socio-demographic characteristics.
Studies have found that homogenous social and attitudinal influences are associated with greater
participation and cross-pressuring associated with less participation (Brader et al. 2014;
Huckfeldt et al. 2004; Klofstad et al. 2013; Lazarsfeld et al. 1948; Mutz 2006).v This perspective predicts the following:
H2a: Exposure to unbalanced partisan media diets results in more campaign participation than exposure to balanced media diets.
The second line of reasoning, again, predicts the opposite. If exposure to uncongenial views in heterogeneous media provokes counterargument and strengthens attitudes, it should lead to greater participation. Emotive responses to the other side can also provoke greater action.
Moreover, in-/out-group sentiments can become more salient with exposure to attacks from opposition media and individuals may increase participation to demonstrate that they are accepted members of their in-group of likeminded viewers and hosts.
In contrast, individuals who do not hear from activists from the other party may be more complacent. Absent this exposure, individuals may be unaware of the stakes and less fearful of the other side winning (Scheufele et al. 2004). Finally, when individuals only hear from fellow partisans, they may have a distorted view of overall public opinion, and conclude that their party will win regardless of whether they personally get involved (Daniller et al. n.d.; Tsfati et al.
2014). Thus we might expect the following according to the second perspective:
H2b: Exposure to unbalanced partisan media diets results in less campaign participation than exposure to balanced media diets.
Our empirical analysis is designed to test these competing hypotheses about the effects of media diets on issue attitudes and campaign participation.
Measures and Methods
This study uses data from the Internet Panel of the 2008 National Annenberg Election
Survey (NAES). The NAES panel included five waves of data collected from a nationally representative sample of American adults. The first wave was conducted before the primaries and the last took place soon after the election.vi We draw on a sample of 4,052 respondents who
answered the questions used to create our independent variables.
Independent Variable: Media Partisanship Score
Testing our hypotheses requires a new measure of exposure that incorporates a variety of
politically relevant television and radio programs into a single measure of media diet
composition. Our measure is based on survey questions asking about exposure to 73 news and
entertainment programs.vii Participants were shown lists of approximately 15 shows at a time,
and asked which shows they watched or listened to “regularly” or “at least once a month”.viii
We use these responses to develop the Media Partisanship Score (MPS), a novel measure of the imbalance of each respondent’s media diet—which we have elaborated on and validated
elsewhere (Authors n.d.).ix The basic idea of MPS is to estimate each program’s partisan slant
based on its audience.x The estimates of program slant are then used to determine the extremity
of each respondent’s reported media diet.
Construction of the MPS is a four-step process:
1. Regress preference for McCain in Wave 2 on each of the 73 programs.xi This first step
generates a coefficient estimate of the direction and degree of partisan slant for any given
program based on the candidate preferences of its audience.
2. Use the coefficient estimates generated in Step 1 to calculate the predicted probability of
preferring McCain for each respondent (based on media consumption) while suppressing
the constant term generated in Step 1.xii This second step estimates the degree to which a
person’s media diet favors McCain.
3. Subtract the probability of preferring McCain from 1 to calculate the probability of
preferring Obama.
4. Subtract the probability of preferring Obama from the predicted probability of preferring
McCain and take the absolute value to calculate the degree to which a respondent’s media
diet favors one candidate over the other.xiii
The four-step process results in the MPS, which is bounded between 0 and 1. Individuals
with an MPS close to 1 are those whose media diet strongly favored one candidate, indicating
that they consumed media that was strongly partisan from one side of the ideological spectrum
without any counter-attitudinal consumption. In contrast, people with values close to 0 had
media diets that were balanced, consuming media that was neutral and/or equally represented
both sides of the partisan spectrum. Given how we have constructed the measure, a respondent
might have a low MPS because they watch internally diverse shows (neutral media which
presents both sides of the ideological spectrum on the same program) or externally diverse shows
(partisan media selected from both sides). A low MPS thus indicates that the individual is
exposed to a cross-pressured media diet.
We repeated the four-step process initially used to generate MPS for Wave 2 to generate a new MPS during Wave 4. We make one change in calculating the MPS for Wave 4. In the first step, instead of generating a new set of coefficient estimates for each program using only Wave 4 data, we used the coefficient estimates for each program generated using Wave 2 data. For example, the predicted effect of watching Program X might be b=.80 according to the
coefficients generated with data from the wave 2. In step 2, b=.80, which would then be
multiplied by watching (1)/not watching (0) a program in Wave 4. The only thing that changes
in our calculation of the MPS between Wave 2 and Wave 4 is whether or not a person watches
Program X (i.e., whether it is coded as “1” or “0”) in the relevant wave. Therefore, change in a
respondent’s MPS between the waves results solely from changing media habits over time, rather
than a change in the estimated indicators of the slant in each show.
MPS has several advantages for our purpose in this paper. First, it provides a single
measure of the overall diversity of an individual’s total media diet based on a continuous
measure of partisan slant, rather than a categorical measure of left, neutral, and right. Second,
we can include a large number of programs and incorporate different genres, including
entertainment shows that may promote perspectives or highlight issues favoring one party.
Third, we do not have to discard independents or respondents whose partisanship cannot be
identified. Fourth, the MPS allows comparisons over time (in this case between waves).
Dependent Variables: Issue Polarization and Campaign Participation
Our measure of issue polarization was generated from questions about four issues
relevant to the 2008 election. Respondents were asked their views on the war in Iraq, a path to
citizenship for illegal aliens, the security of border with Mexico, and free trade.xiv For each
question, respondents selected from responses that spanned the conventional views on the issues, and were considered “polarized” on an issue if they chose an extreme response on either side of the spectrum. On average, respondents held more polarized opinions during the convention season (M=1.675; SD=1.118) than during the general election (M=1.626, SD=1.109), meaning
that respondents became less extreme over time.
Campaign participation was measured using five items traditionally used to assess
participation in elections. Respondents reported whether, in the past 12 months, they had (1)
given money to a campaign; (2) worked or volunteered for a campaign; (3) persuaded others in
favor of a candidate; (4) attended a political meeting, speech, or event; or (5) advertised their
support for a candidate via buttons, stickers, or signs. The individual items were summed into a
5-point scale. On average, respondents reported that they had participated in one campaign
activity during the convention season (M=.998; SD=1.214), which increased to an average of
between one and two activities following the general election (M=1.445; SD=1.440).
Research Design
To improve causal inference and reduce the likelihood of spuriousness, we use two different methodologies. Subject fixed-effects analysis compares outcomes over time within an individual, while the matching analysis compares outcomes between similar individuals.
Subject Fixed-Effects Design
First, we used panel survey data to estimate subject fixed-effects models. Due to the difficulty of correctly implementing fixed-effects models using count data (Allison, 2009), we employed a linear model in which
yit = β0 + β1MPS it + β2Total # of News Programs it +αi + uit (1)
where y is the outcome of interest, MPS it provides respondent i’s media diet at Wave t, Total # of News Programsit provides the total number of news programs respondent i watched at Wave
t,xv α is subject fixed effects, and u is an unobserved disturbance term. The benefit of these
fixed-effects models is that they control for unobserved heterogeneity, thus ruling out concerns
related to spuriousness (if the unobserved characteristics are time invariant). This is to say that
some subjects may be more educated, wealthy, etc. than others, and fixed-effects models control
for both observed and unobserved characteristics that remain stable over time. In essence, we
compare subjects at a later time to themselves at an earlier time. Using this approach, β1 provides
the within-subjects effect of the media partisanship score, indicating the effect of changes in media diet over time within individuals.
We face a common challenge of using observational data for causal inference: being unable to establish the direction of influence with certainty. Nonetheless, we designed our analysis such that the dependent variable was measured after the initial measure of the independent variable. Specifically, the independent variable MPS, was measured during Wave 2
(the primaries) and Wave 4 (the general election). The outcome variable issue polarization was
measured during Wave 3 (the convention season) and Wave 4 (the general election), and
campaign participation was measured during Wave 3 (the convention season) and Wave 5 (the
post-election period). This lag design may help somewhat with establishing causal order.
Matching Design
As with any technique, regression models are not perfect. To examine the robustness of
our findings, we also examine the effects of changes in MPS over the course of the survey when
matching subjects on a set of covariates. Matching analyses reduce concerns that determinants of
the subject’s media diet (and thus of their MPS) also influence their political participation and
issue polarization because we only compare subjects that are similar on a large set of observables
(Dehejia & Wahba 1999; Barabas 2004; Kam & Palmer 2008; Ladd & Lenz 2009; Meredith &
Malhotra 2011; Levendusky 2013). Two “as-if” experimental groups were matched on a set of
demographic characteristics, politically relevant variables, and MPS in Wave 2. A description of
the variables on which respondents were matched is included in Appendix 6.
Matching requires the definition of a binary treatment to separate the two groups. For the
analyses described in the body of this paper we compare subjects that had below average MPS in
Wave 5 (the “control”) with subjects that had above average MPS in Wave 5 (the “treatment”),
though results are consistent when we use different criteria for dividing our sample (see
Appendices 8-9). Since respondents are matched based on their MPS at Wave 2, the matching
analysis allows the comparison of otherwise similar respondents whose MPS diverged during the
course of the campaign. As a robustness check, we use two different techniques for matching:
unrestricted Mahalanobis matching and Mahalanobis matching with caliper restriction.xvi
The motivation for matching analysis is to generate control and treatment groups that are
similar in all respects but their treatment status by matching treated to corresponding control
observation. Comparison of the means tests for matched variables indicate that both strategies for
matching successfully reduce differences between the control and treatment groups relative to
the unmatched analysis, and that none of the group-wide differences are significantly different at the 5% level under Mahalanobis matching with a caliper restriction (see Appendix 7).
Once the matched groups are generated, we perform a simple comparison of means to determine whether being in the treatment group is associated with an increase in political participation and ideology relative to those in the ‘control group’.
Results
Subject Fixed-Effects Regression Results
Table 1 below reports the coefficient estimates generated from Equation 1, which
presents the fixed-effects models that control for between-subject differences in media diet. The
first column of Table 1 shows the effects of homogenous and extreme media diets on issue
polarization. The first hypothesis was that unbalanced diets of likeminded media would generate
more extreme issue attitudes than balanced diets (H1a), while the second hypothesized the
reverse effect (H1b). Neither hypothesis is supported by the data. The fixed-effects regression indicates that there is no significant effect of within-subject change in MPS on within-subject change in the extremity of issue attitudes across our two waves (b=-.000, SE=.066). It seems that homogenous and extreme media diets do not contribute to issue polarization.
[Table 1 about here]
However, the results in column 2 of Table 1 do indicate a significant effect of media diet on campaign participation. The results are consistent with the first line of reasoning that homogenous and ideologically extreme media diets result in increased campaign participation
(H2a). The fixed-effects models show that, on average, a person who moved from a more balanced media diet to a more unified and extreme media diet would participate in more campaign activities (b=.220, SE=.069). During the general election, the size of this effect indicates that respondents with MPS (M = 0.557, SD = 0.298) of one standard deviation above the mean will participate in 0.113 additional campaign activities on average.
Matching Analyses Results
Table 2 shows the comparison of means with respect to issue polarization and campaign
participation without matching, matching without a caliper restriction, and matching with a
caliper restriction of 0.25 standard deviations of the Mahalanobis distance. The first row of
Table 2 shows a simple comparison of means between individuals with different media habits,
absent any matching. As might be expected, individuals who choose to watch homogenous and
extreme media, on average, are more extreme and active than those with more diverse or neutral
media diets. However, the differences reflected in these unmatched comparisons do not account
for confounding factors and thus tell us nothing about whether media diet composition affects
attitudes and behaviors.
[Table 2 about here]
The next two rows in Table 2 show the key results of interest. When individuals are
matched, there is no significant difference in the means for issue polarization between those with
high and low MPS. The results are insignificant regardless of whether the matching is done with
or without the caliper. These findings are consistent with those from the fixed-effects analysis;
both indicate that MPS does not significantly affect issue polarization.
Most importantly, Table 2 also supports the findings of the subject fixed-effects analysis
with regards to participation. Over the course of the campaign, increasing MPS from below the
median to above the median results in greater participation regardless of whether a caliper is used. Overall, the results of the matching analyses provide additional support for the hypothesis that an unbalanced media diet energizes political participation (H2a).
We used multiple methods to account for threats to causal inference; however, our study is not without limitations. We cannot fully rule out the possibility of reverse causation without experimental data that would ensure temporal precedence. Although we chose analytical methods that minimize the likelihood of spuriousness—namely, by using fixed effects and matching—there are still potential threats to causal attribution. Specifically, our efforts to match on key variables are only as good as those variables that we have observed, and all fixed-effects models are vulnerable to the criticism that they do not control for spuriousness due to time- varying unobserved variables. Nonetheless, our claim that campaign participation is boosted by an increasingly unbalanced and extreme media diet is strengthened by the consistent evidence generated from multiple analytical methods.
Discussion
Research on partisan media typically evaluates the independent effects of likeminded,
crosscutting and neutral exposure without considering how combination of media types in an
individual’s overall media diet might influence outcomes. We argue that the conventional
approach—testing the effects of one kind of exposure while controlling for other types—is
insufficient for understanding the current media environment where individuals consume a mix
of programming with varying degrees of extremity. Empirical research on the topic is especially
valuable because different scholarly traditions lead to contrasting predictions about the relative
influence of unbalanced as opposed to balanced media diets on polarization and participation.
In this article we use a new measure to evaluate the effects of unbalanced media diets on
issue attitudes and campaign participation. Our subject fixed-effects and matching analyses suggest that ideologically homogenous and extreme media diets increase campaign participation.
We find no empirical support for the claim that slanted media diets affect opinions on key issues.
Why might a diet of stanch partisan media from only one side of the political spectrum draw people into the political process but not alter preferences on key issues? It may be that issue attitudes are fairly stable and unlikely to be affected by media consumption over the course of an election campaign. This may be especially true given that the issues included in this analysis—the Iraq war, citizenship, boarder security, and free trade—are longstanding and often discussed topics. Respondents probably had strong priors on these issues long before the survey
(Levendusky 2014). It may be that a homogeneous diet of likeminded media increases other types of polarization (such as polarization on novel issues or affective polarization) and that more extreme attitudes along these other dimensions motivate action.
It is also possible that when faced with diverse perspectives, some individuals may
polarize, while others may moderate their issue stances. A heterogeneous media diet may both
motivate strong partisans to counter-argue against opposing messages and provide them with
fodder with which to do so. In contrast, exposure to alternative perspectives in crosscutting or
neutral media may persuade other individuals to reconsider and temper initial opinions. The
estimated average effect of polarizing reactions among some individuals, and moderating
reactions among others, would be consistent with our null result.xvii
Finally, unbalanced media diets may increase participation without affecting attitudes by pulling individuals into the political process with partisan appeals to get involved, and targeted information about how to do so. Elite mobilization through partisan media, rather than citizen motivations, may be responsible for the effect of slanted media diets on participation. Additional research is necessary to determine which explanations are most likely.xviii
The analysis in this paper shows that, on average, exposure to loud and clear partisan
media diets as compared to muted and mixed diets have the ability to mobilize without
generating more extreme attitudes about the Iraq war, citizenship, boarder security, and free
trade. These results may help assuage tension between the competing theories of deliberative
and participatory democracy in the new media environment. However, ours is the first study of
its kind, and it is too early to conclude that unbalanced media diets never exacerbate polarization.
This article proves a theoretical and empirical basis for future research on the effects of
homogeneous versus heterogeneous media diets on additional outcomes of interest.
Endnotes
i Audience ratings show that network news programs still attract much larger audiences than even the most popular cable news shows (Arceneaux and Johnson 2013; Webster 2005), and many cable news consumers report that they watch programming that ostensibly conflicts with their political views (Garrett 2014). ii Or alternatively scholars study the independent effects of: media with either a conservative or a liberal slant; a specific channel such as FOX, a single program, or media segment. In each case, the effect of one type of media is studied in isolation of other types. Garrett et. al. (2014) provide a notable exception to the analysis of independent effects. They test the interactive effect of likeminded and crosscutting exposure on affective polarization and social distance in the US and Israel (and find primarily null results). They take an important step in the direction of considering media diet composition writ large by acknowledging possible contingent effects of one type of media exposure on another. However, their interaction term does not reflect use of neutral media, the degree of slant in programs, nor the relative balance of overall media diets. iii It is difficult to derive clear predictions about the effects of media diet composition solely from research on the average effects of crosscutting or likeminded media (which holding constant other types of consumption). Results about the average influence of one type of media may hide heterogeneous effects between subpopulations that consume different mixes of media. While likeminded exposure has been found to have positive (e.g. Levendusky 2013) or null (e.g.
Arceneaux et al. 2013) average effects on polarization, the magnitude of the effects may be contingent on the total composition of one’s media diet. Crosscutting media has been found to have positive (e.g. Arceneaux et al. 2013), neutral (e.g. Levendusky 2013), or negative (e.g.
Feldman 2011) effects on polarization in different studies, which further complicates inference
about mixed media diets.
iv Garrett et al. (2014) come to similar conflicting hypotheses when theorizing about the
interactive effects of likeminded and crosscutting media on attitude polarization. Although they
do not theorize about the effect of neutral media, nor the overall balance of a media diet, their
reasoning with regard to contingent effects parallels our own.
v Although, others found limited or no effects (Leighley 1990; Nir 2005; Scheufele et al. 2006).
vi Data was drawn from the second and fourth waves of the NAES survey because these were the
periods during which respondents were asked detailed questions about their media consumption.
vii See Dilliplane et al. (2013) for a detailed description and validity tests of this approach to
measuring media exposure. Forty of the shows were classified as “news,” with 24 appearing on cable stations (e.g. FNC, MSNBC, and CNN) and 16 appearing on network news (ABC, NBC,
CBS, and PBS). Eighteen programs were classified as non-news content, including dramas such
as CSI and talk shows such as Oprah. Fifteen of the programs were political talk-radio shows.
Programs were included on the survey based on to their popularity according to Nielson ratings prior to the election.
viii Respondents could check off any number of programs that they wanted, with the average
respondent reporting that they consumed between 10 and 11 programs during both the primaries
and the general election (M=1.608; SD=7.064 during Wave 2; M=1.436; SD=6.819 during Wave
4). For a complete list of programs included, see Appendix 1.
ix Our measure of partisan media diets (MPS) is based on a measure of sociodemographic cross-
pressures developed by Brader, Tucker and Therriaut (2014).
x We use audience preferences to measure relative slant, not bias according to some normative standard or benchmark. The percent preferring McCain in our data is strongly correlated with other measures of relative partisan slant, including: Dilliplane’s (2011)’s measure based on perceived bias by survey respondents (r=.83, n=42), LaCour’s (2014) measure from an automated computer-coded content analysis of the similarity of a program’s language (closed- captioning transcripts) and the congressional record (r=.88, n=38); and PEW’s (2008a; b) content analyses of coverage tone (r=.63, n=16) and candidate visibility (-.76, n=19). xi Our goal for this first step is calculate partial correlations between audience preferences and programs without implying causal influence one way or the other. We use logistical analysis because Preference for McCain is a dichotomous variable, which we coded as follows: First, we used vote intention to identify McCain and Obama supporters. If a respondent reported no preference, or that they did not know, we used party membership (e.g. a strong Democrat would be coded as favoring Obama) or the party they leaned towards (e.g. a person who leaned
Republican would be coded as favoring McCain). xii We suppressed the constant term because we are interested in calculating the predicted slant of a given media diet, and not the leaning of a given respondent. In more technical terms, our reasons for including the constant in Step 1 but then suppressing it in Step 2 are as follows: suppressing the constant in Step 2 prevents bias in our initial estimates for the effects of media due to general preference for Obama in the dataset, but including the constant in Step 1 avoids forcing our media variables to soak up the variation caused by this preference. In suppressing the constant term in Step 2, we deviate from the methodology used by Brader, Tucker, and
Therriault (2014).
xiii For example, the estimated coefficients (Step 1) were large in magnitude but opposite in direction for Countdown with Keith Olbermann (β=-.77) and The Rush Limbaugh Show (β=.90), the most extreme programs on the left and right respectively. If we were to calculate MPS for a hypothetical individual who consumed the Rush Limbaugh Show and nothing else, his/her predicted probability of voting for McCain during the primaries was 0.71 (Step 2) creating a predicted probability of voting for Obama of 0.29 (Step 3), and an MPS of 0.42 (Step 4). A hypothetical individual who consumed both shows (but only these shows) would have a predicted probability of voting for McCain of 0.53 (Step 2), a probability of supporting Obama of 0.47 (Step 3) and an MPS of 0.06 (Step 4). xiv The exact question wording can be found in the Supporting Information. xv We controlled for the total number of shows watched to account for changes in the volume of a person’s news consumption as well as the overall tenor of that media diet. xvi Using a caliper drops treatment observations from the dataset if they are unable to be closely matched with a control observation. This reduces the sample size but increases the comparability of matched observations, strengthening the assumption of ‘as-if’ random assignment. xvii Symmetrical polarizing and moderating effects among subpopulationsdo not necessitate symmetrical mobilizing and demobilizing effects among subpopulations. For example, slanted diets may increase attitude extremity and participation among one subsection of the population, and decrease attitude extremity, but not participation, among another subsection. Those whose attitudes are moderated by exposure to alternative arguments may not give up routinized activities such as voting. xviii Of course null results could be due to measurement noise, bias, or range. For example, one potential cause of null results would be a ceiling effect on issue polarization, though that seems
unlikely in this case. While those respondents in the top 1/3 of MPS scores show slightly more polarized attitudes, only 7.2% held extreme positions on all four issues.
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Table 1. Subject Fixed-Effects Analysis
Issue Polarization Campaign Participation
MPS -.000 .220 * (.066) (.069)
Total media .000 -.0030 (.004) (.004)
Constant 1.645 *) 1.138 *** (.054) (.050)
Observations 6866 8104
Groups 3433 4052
R2 overall .008 .070
Note: * p<.05, two-sided tests. Entries are ordered logistical coefficient estimates with standard errors shown below in parentheses for within-subject comparisons.
Table 2. Matching Analysis
Issue Polarization Campaign Participation
Treatment Control Treatment Control Difference Difference Mean Mean Mean Mean
Unmatched 1.721 1.587 .134 (.041) * 1.979 1.147 .832 (.049) * N=1,526 N=1,480 N=1,633 N=1,564
Matched No Caliper 1.721 1.672 .049 (.071) 1.979 1.605 .374 (.089) * N=1,526 N=1,480 N=1,633 N=1,564
Caliper 1.913 1.739 .174 (.114) 1.990 1.594 .396 (.145) * N=276 N=1,480 N=308 N=1,564
Note: * p<.05, two-sided T-test with N-2 df. Standard errors are shown in parentheses next to the differences. Control group includes respondents with below average MPS in wave 5, and treatment includes respondents with above average MPS in wave 5.
Appendix 1. Programs used to estimate the partisan news score Cable news programs Anderson Cooper 360 (CNN) Hardball with Chris Matthews (MSNBC) The Beltway Boys (FNC) Heartland with John Kasich (FNC) The Big Story with John Gibson and Heather Larry King Live (CNN) Nauert (FNC) Late Edition with Wolf Blitzer (CNN) Countdown with Keith Olbermann (MSNBC) Lou Dobbs (CNN) CNN Headline News MSNBC Live Fox & Friends (FNC) Out in the Open (CNN) FOX News (FNC) Reliable Sources (CNN) The Fox Report with Shephard Smith (FNC) Situation Room (CNN) Geraldo at Large (FNC) Special Report with Brit Hume (FNC) Hannity & Colmes (FNC) Studio B with Shepard Smith (FNC) Hannity’s America (FNC) The O’Reilly Factor (FNC) Your World with Neil Cavuto (FNC) Network news programs 20-20 (ABC) Dateline NBC 60 Minutes (CBS) Face the Nation (CBS) Nightline (ABC) Frontline (PBS) ABC World News The McLaughlin Group America This Morning (ABC) Meet the Press (NBC) CBS Evening News NBC Nightly News CBS Morning News News Hour with Jim Lehrer (PBS) CBS Sunday Morning This Week with George Stephanopoulos (ABC) Non-news programs 24 Good Morning America (ABC) Big Love Law and Order Brothers and Sisters The Late Show with David Letterman The Colbert Report Oprah The Daily Show with Jon Stewart Scrubs CSI: Miami The Simpsons (FOX) The Early Show (CBS) The Today Show (NBC) Ellen DeGeneres Show The Tonight Show with Jay Leno Family Guy The View (ABC) Political talk radio Bill Bennett’s Morning in America The Mark Levin Show Bill O’Reilly Radio Factor Michael Savage, “The Savage Nation” The Clark Howard Show The Mike Gallagher Show The Glenn Beck Program The Neal Boortz Show The Jerry Doyle Show NPR’s All Things Considered The Jim Bohannon Show The Rush Limbaugh Show Dr. Laura Schlessinger The Sean Hannity Show Laura Ingraham
Application 2. Predicting Preference for McCain using Media Exposure
Program b (SE) % Preferring McCain in Wave 2 NBC Nightly News .036 .288 (.082) The Simpsons .0010 .248 (.125) CBS Evening News -.0390 .270 (.087) CBS Morning News .0190 .265 (.126) CNN Headline News -.152 .260 (.090) The Tonight Show (Leno) .128 .332 (.090) America This Morning .1010 .262 (.249) The Daily Show with Jon Stewart -.639* .133 (.149) ABC News: Nightline -.289* .249 (.104) ABC World News .042 .289 (.084) Today Show -.002 .289 (.089) News Hour with Jim Lehrer -.339* .176 (.133) FOX News .514* .468 (.079) Good Morning America .070 .295 (.096) Ellen DeGeneres Show -.101 .214 (.106) 60 Minutes -.193* .271 (.084) Hannity & Colmes .360* .686 (.158) Hannity’s America .034 .830 (.256) Hardball with Chris Matthews -.205 .244 (.138) Heartland with John Kasich -.405 .769 (.385) 24 .004 .355 (.107) Late Edition with Wolf Blitzer -.246 .219 (.177) Appendix 2, continued – Program b (SE) % Preferring McCain in Wave 2
The Late Show with David Letterman -.167 .272 (.092) The O’Reilly Factor .485* .618 (.137) The Early Show .056 .279 (.139) Face the Nation .130 .242 (.132) Fox & Friends .012 .606 (.127) Brothers and Sisters -.199 .227 (.112) Frontline -.207 .189 (.127) Oprah -.597* .236 (.091) Meet the Press -.425* .248 (.114) CBS Sunday Morning .128 .250 (.100) The Beltway Boys .283 .726 (.225) The Big Story with John Gibson & .214 .769 Heather Nauert (.283) 20-20 .225* .285 (.091) The Fox Report with Shepard Smith .250 .665 (.163) MSNBC Live .042 .234 (.111) CSI:Miami .131 .341 (.080) Out in the Open .080 .200 (.688) Law & Order -.194* .302 (.076) Situation Room -.193 .190 (.166) Special Report with Brit Hume .477 .659 (.185) Larry King Live -.118 .252 (.119) Lou Dobbs -.340* .250 (.160)
Appendix 2, continued – Program b (SE) % Preferring McCain in Wave 2
Big Love -.037 .226 (.175) Dateline NBC .012 .288 (.087) Family Guy -.298* .249 (.118) Studio B with Shepard Smith -.030 .692 (.214) Reliable Sources .043 .225 (.397) Your World with Neil Cavuto .242 .687 (.185) McLaughlin Group .116 .259 (.134) The Week with George Stephanopolous -.266* .211 (.125) The View -.209 .216 (.118) Scrubs -.107 .269 (.105) The Colbert Report -.270 .167 (.153) Anderson Cooper -.127 .229 (.125) Geraldo at Large -.146 .534 (.190) Countdown with Keith Olbermann -.772* .131 (.171) Bill O’Reilly Radio Factor .311* .656 (.140) The Rush Limbaugh Show .896* .703 (.111) The Jim Bohannon Show -.458 .478 (.331) The Clark Howard Show .490* .583 (.202) The Mark Levin Show .102 .790 (.279) Bill Bennett’s “Morning in America” .278 .634 (.383) The Jerry Doyle Show .013 .622 (.511) NPR’s All Things Considered -.677* .197 .088
Appendix 2, continued – Program b (SE) % Preferring McCain in Wave 2
The Sean Hannity Show .444* .749 (.138) Michael Savage’s “Savage Nation” .377 .638 (.157) The Glenn Beck Program .622 .667 (.149) Dr. Laura Schlessigner .075 .545 (.157) Laura Ingraham .300 .759 (.216) The Neal Boortz Show .321 .693 (.227) The Mike Gallagher Show .023 .753 (.378) Constant -.084 (.068) Observations 5251 Pseudo R2 .255
Appendix 3. Comparing NAES and PEW 2008 Media Consumption Data
To assess the validity of our data, we break down the people who indicated that they watch each show by political ideology and compare to the proportions found for similar media questions in the 2008 Pew Biennial Media Consumption Survey. Because Pew 2008 often asked more general media consumption questions (usually identifying networks rather than specific shows), some comparisons required generation of comparable values from the show- specific answers provided in the NAES. Since the Media Partisanship Score method uses a binary (McCain-Obama) construction to generate MPS values, media variables are compared in terms of the proportion of self-identified (non-leaning) Democrats and Republicans. Pew responses are weighted as recommended in the codebook.
Notes: Asterisks denote shows for which there was not an exact match between the NAES and Pew Datasets. In these instances, NAES show-level responses were re-coded to match the Pew question as closely as possible. Shows / categories that showed significant differences in the Dem/Rep breakdown between the NAES and Pew respondents are in bold. Pearson’s X2 Program Survey % Dems % Reps (p, df=1) National Nightly NAES .590 .410 17.321 Network News* Pew .628 .372 (.000)
Cable News NAES .513 .487 35.323 Networks* Pew .556 .444 (.000)
CBS Evening NAES .611 .389 10.366 News Pew .705 .295 (.001)
ABC World News NAES .606 .394 16.945 Pew .710 .290 (.000)
NBC Nightly NAES .589 .411 8.266 News Pew .660 .340 (.004)
CNN* NAES .625 .375 1.923 Pew .654 .346 (.166)
Fox News Cable NAES .393 .607 47.086 Channel* Pew .541 .459 (.000)
MSNBC* NAES .674 .326 1.575 Pew .644 .356 (.209)
NPR NAES .713 .287 .775 Pew .691 .308 (.379) Appendix 3, Continued: Comparing NAES and PEW 2008 Media Consumption Data Program Survey % Dems % Reps Pearson’s X2
News Magazine NAES .594 .406 8.323 Shows* (Dateline, 20/20) Pew .639 .361 (.004)
The NewsHour NAES .752 .248 2.013 with Jim Lehrer Pew .714 .286 (.156)
Late Night TV NAES .594 .406 1.704 Shows* (Letterman, Leno) Pew .621 .379 (.192)
Morning Talk NAES .585 .415 11.832 Shows* (Today Show, Pew .647 .353 (.001) GMA, The Early Show)
Sunday Morning NAES .628 .372 2.981 Talk Shows (Meet the Press, Pew .661 .334 (.084) This Week, Face the Nation)
Larry King Live NAES .659 .341 .317 (CNN) Pew .675 .325 (.573)
Countdown with NAES .826 .174 .312 Keith Olbermann (MSNBC) Pew .805 .195 (.576)
Hannity and NAES .167 .833 12.423 Colmes (FOX) Pew .265 .735 (.000)
The Daily Show NAES .810 .190 24.735 with Jon Stewart (Comedy Central) Pew .654 .346 (.000)
The Rush NAES .115 .885 22.131 Limbaugh Show (Talk Radio) Pew .243 .757 (.000)
Appendix 3, Continued: Comparing NAES and PEW 2008 Media Consumption Data Program Survey % Dems % Reps Pearson’s X2
Lou Dobbs Tonight NAES .637 .363 .364 (CNN) Pew .662 .338 (.546)
Hardball with Chris NAES .687 .314 .205 Matthews (MSNBC) Pew .672 .328 (.651)
The O’Reilly NAES .218 .782 85.631 Factor (Fox) Pew .458 .542 (.000)
The Colbert Report NAES .753 .247 .418 (Comedy Central) Pew .774 .226 (.518)
Appendix 4. Lagged MPS Differences Predict Future MPS
To address the concern that within-subject differences in MPS represent noise rather than meaningful change, we also validated our measure using the approach employed by Levendusky (2011). Notably, there was significant variation in the MPS within individuals (p<.001 using a paired t-test), with the average person’s media diet becoming more homogenous and extreme during the general election (M=.488; SD=.308 during Wave 2; M=.526; SD=.304 during Wave 4; p<.001 using a paired t-test). To address the concern that these changes were due to measurement error, we estimated the following:
MPS i3 = β0 + β1(MPS i2 – MPS i1) + ui (3)
where the 1, 2, and 3 subscripts denote waves of the panel data1 and all other terms are defined as in Equations (1) and (2). As Levendusky (2011, p. 49) argues, finding a significant coefficient for the effect of change in MPS between Wave 2 and Wave 4 on the level of MPS in Wave 5 helps to rebut the argument that between-period differences are simply random noise stemming from measurement error. If this were true, change in MPS between Wave 2 and Wave 4 should have no predictive power to explain the level of MPS at Wave 5. The statistically significant β1 (p<.001) shown in Appendix 1 indicates that differences in the MPS reflect real change in media effects that persist over time. According to this coefficient estimate, people whose media diets become more (less) polarized and homogenous between the political primaries and general election receive higher (lower) MPS during the post-election period.
Lagged MPS Differences Predict Future MPS
Wave 5 MPS Wave 4 – Wave 2 MPS Differences .198* (.016) Constant .550* (.005) N 3487 R2 .031 * p < .05
______1 The careful reader will note that we are using three waves of panel data in order to validate our measure, whereas we are using two waves of data to test the relationship between media diet and polarization or participation. This is because data regarding both self-reported ideology and participation were only available in two waves subsequent to the media exposure variables. To reiterate, the participation variables were gathered during Wave 3 and Wave 5, and the ideology variables were gathered during Wave 3 and Wave 4.
Appendix 5. Question Wording And Response Details For Polarization Measure
Iraq: “Which of the following plans for the United States Policy in Iraq comes closest to your own position?”
Response options: (1) The US should withdraw all troops from Iraq as soon as possible, regardless of conditions in Iraq. (2) The US should set a deadline for withdrawing its troops if the Iraqi government doesn't show definite progress in training Iraqi forces and controlling violence on its own. (3) The US should keep its troops in Iraq as long as is needed until a stable democratic government is established there.
In wave 2 (convention season), 52.7% of respondents indicated a polarized viewpoint (response 1 or 3). In wave 5 (the general election), 50.3% of respondents indicated a polarized viewpoint.
Path to Immigration: “Please indicate whether you favor or oppose each of the following proposals addressing immigration. Provide a path to citizenship for some illegal aliens who agree to return to their home country for a period of time and pay substantial fines.”
Response options: (1) Strongly favor. (2) Somewhat favor. (3) Somewhat oppose. (4) Strongly oppose.
In wave 2 (convention season), 33.2% of respondents indicated a polarized viewpoint (response 1 or 4). In wave 5 (the general election), 32.2% of respondents indicated a polarized viewpoint.
Border Security: “Please indicate whether you favor or oppose each of the following proposals addressing immigration. Increase border security by building a fence along part of the US border with Mexico.”
Response options: (1) Strongly favor. (2) Somewhat favor. (3) Somewhat oppose. (4) Strongly oppose.
In wave 2 (convention season), 57.0% of respondents indicated a polarized viewpoint (response 1 or 4). In wave 5 (the general election), 55.6% of respondents indicated a polarized viewpoint.
Free trade: “Do you favor or oppose the federal government in Washington negotiating more free trade agreements like NAFTA?”
Response options: (1) Strongly favor. (2) Somewhat favor. (3) Somewhat oppose. (4) Strongly oppose.
In wave 2 (convention season), 24.9% of respondents indicated a polarized viewpoint (response 1 or 4). In wave 5 (the general election), 24.7% of respondents indicated a polarized viewpoint.
Appendix 6. Variable Definitions and Coding Rules Used in Matching Analysis
This section details the coding rules used for the variables in the matching analysis. Variables like age, education, and income were recoded into groups (cohort, educational achievement, income tercile) to reduce the influence of small variations in these variables in the matching process.
Birth Cohort: based on age, respondents are coded into one of 6 cohorts: pre-New Deal (born 1914 or earlier), New Deal (born 1915-1930), post-New Deal (born 1931-1945), baby boomer (born 1946-1961), generation X (born 1962-1982), and generation Y (born 1983 or later).
Income Tercile: Income responses placed respondents into income ranges, which were recoded into relative income tercile for the matching analysis. The first tercile includes respondents indicating an income less than $50,000, the second includes respondents indicating an income greater than $49,999 and less than $85,000, and the third includes respondents indicating an income greater than or equal to $85,000.
Education: Respondents were placed into one of five groups based on their highest level of academic achievement: (1) less than a High School degree, (2) High School Degree (3) some College or Associate’s Degree, (4) Bachelor’s Degree, and (5) Graduate / Professional Degree.
Ideology: Respondents indicated their political ideology on a seven-point scale ranging from Extremely Liberal to Extremely Conservative.
Party Identification: Respondents indicated whether they were Democrats, Independents, or Republicans.
Bush Vote: 1 if the respondent voted for Bush in the 2004 election.
Political Interest: Respondents indicated their level of political interest on a four-point scale.
Media Partisanship Score (Wave 2): In order to examine the effect of changes in MPS over the course of the election, respondents were matched on their calculated MPS during the second wave of interviews.
Black: 1 if the respondent was Black, 0 otherwise.
Hispanic: 1 if the respondent was Hispanic, 0 otherwise.
Gender: 1 if the respondent was Male, 0 otherwise.
Appendix 7. Difference in Means (Standard Deviation) for Matched Variables Between Control and Treatment Groups, Treatment 2
Variable Unmatched Matched, No Caliper Matched, With Caliper Black .017 -.000 .000 (.010) (.010) (.014) Hispanic .009 .000 -.000 (.007) (.007) (.005) Birth Cohort -.158* -.020 -.000 (.027) (.026) (.056) Gender -.001 -.005 .000 (.017) (.018) (.039) Income .058* .012 .000 (.028) (.029) (.070) Education .108* -.007 .000 (.023) (.022) (.046) Ideology -.472* -.012 .000 (.054) (.061) (.131) Party ID -.222* .012 -.000 (.028) (.031) (.071) Bush Vote -.183* -.004 -.000 (.017) (.017) (.039) MPS at Wave 2 .318* .078* .016 (.009) (.009) (.023) Political Interest .439* .012 -.000 (.023) (.020) (.040)
N (Treatment 1 = 1) 1,633 1,633 308 N (Treatment 1 = 0) 1,564 1,564 1,564 Note: In column three, caliper = .25 Standard Deviations of the Mahalanobis Distance. Differences in bold are significant at the 5% level (two-sided), * p<.05. The table above shows the results for the “Political Participation” matching analysis, but results from the “Issue Polarization” matching analysis are functionally the same.
Appendix 8. Comparison of issue polarization means for unmatched, matched without caliper, and matched with caliper data sets under alternative treatment definitions
Issue Polarization Mean for: N = Treatment Control Difference Treated Control Alternative Treatment 1 (treatment group = above lower tercile) .0740 2,036 970 Unmatched 1.679 1.605 (.043) -.015 2,036 970 Matched, No Caliper 1.679 1.694 (.073) .039 230 970 Matched, Caliper 1.713 1.674 (.127)
Alternative Treatment 2 (treatment group = in upper tercile) .133*0 1,043 1,963 Unmatched 1.742 1.609 (.043) .035 1,043 1,963 Matched, No Caliper 1.742 1.708 (.071) .047 295 1,963 Matched, Caliper 1.946 1.898 (.111)
Note: * p<.05, two-sided T-test with N-2 df. Standard errors are shown in parentheses below the differences
Appendix 9. Comparison of campaign participation means for unmatched, matched without caliper, and matched with caliper data sets under alternative treatment definitions
Campaign Participation Mean for: N = Treatment Control Difference Treated Control Alternative Treatment 1 (treatment group = above lower tercile) .752* 2,167 1,030 Unmatched 1.814 1.062 (.053) .376* 2,167 1,030 Matched, No Caliper 1.814 1.438 (.086) .322* 261 1,030 Matched, Caliper 1.704 1.383 (.151)
Alternative Treatment 2 (treatment group = in upper tercile) .880* 1,113 2,084 Unmatched 2.145 1.266 (.052) .288* 1,113 2,084 Matched, No Caliper 2.145 1.858 (.090) .067 314 2,084 Matched, Caliper 2.217 2.150 (.149)
Note: * p<.05, two-sided T-test with N-2 df. Standard errors are shown in parentheses below the differences