How Does Geographical Region Affect The Makeup Of Politcal Parties

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Peer-reviewed

Research Article

The political reference point: How geography shapes political identity

• Matthew Feinberg,
• Alexa Yard. Tullett,
• Zachary Mensch,
• William Hart,
• Sara Gottlieb

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• Published: Feb sixteen, 2017
• https://doi.org/10.1371/journal.pone.0171497

Figures

Abstract

It is commonly assumed that how individuals identify on the political spectrum–whether liberal, conservative, or moderate–has a universal pregnant when it comes to policy stances and voting behavior. Just, does political identity mean the same matter from place to place? Using data collected from across the U.South. we find that fifty-fifty when people share the aforementioned political identity, those in "bluer" locations are more than probable to support left-leaning policies and vote for Democratic candidates than those in "redder" locations. Because the pregnant of political identity is inconsistent across locations, individuals who share the same political identity sometimes espouse opposing policy stances. Meanwhile, those with opposing identities sometimes endorse identical policy stances. Such findings suggest that researchers, campaigners, and pollsters must utilize caution when extrapolating policy preferences and voting behavior from political identity, and that animosity toward the other end of the political spectrum is sometimes misplaced.

Citation: Feinberg M, Tullett AM, Mensch Z, Hart W, Gottlieb South (2017) The political reference point: How geography shapes political identity. PLoS ONE 12(2): e0171497. https://doi.org/10.1371/journal.pone.0171497

Editor: Kimmo Eriksson, Mälardalen University, SWEDEN

Received: October 24, 2016; Accepted: January 20, 2017; Published: February 16, 2017

Copyright: © 2017 Feinberg et al. This is an open admission commodity distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in whatever medium, provided the original author and source are credited.

Data Availability: Information are available at https://osf.io/hby72/.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

When it comes to politics, how do you lot identify? Slightly bourgeois? Extremely liberal? Moderate, middle of the road? Academics, political campaigns, and pollsters commonly use people's political identity as a heuristic for classifying and making judgments about what people believe, who they will vote for, and whether they should exist targeted for political persuasion. The lay public frequently uses political identity to determine whether someone fits into their political ingroup or outgroup and is therefore deserving of respect or derogation [one]. Indeed, research suggests that although explicit prejudice is on the decline in well-nigh domains (e.chiliad., race), it is on the rise when it comes to political identity [two, 3]. A core assumption made in each of these cases is that political identity has universal meaning; what 1 person understands to exist a conservative (liberal) is what another person understands to exist a conservative (liberal). But, practise political identities have consequent meanings across people and places, or might researchers, campaigners, pollsters, and the lay public be judging a moving target (c.f., [four–9])?

Much research suggests that individuals arrive at their political identity through a confluence of bottom-up influences, including genetics [10, 11], physiology [12], personality [13], fundamental needs and motivations [xiv, xv], and moral values [xvi, 17]. Other inquiry highlights the elevation-down influence of political elites and the media [9, 18]. Rarely looked at empirically is the possibility that horizontal influences–those exerted by the people around us–could be integral in how we understand and determine our own political identity. Abstract judgments almost ourselves typically entail social comparing processes and a shared understanding of what it means to be function of a particular group [nineteen–22]. Thus, choosing a political identity does non occur in a vacuum; instead it reflects what one's social surroundings tacitly defines as liberalism and conservatism.

It stands to reason, so, that when people decide where they fall on the political ideology spectrum they will rely on perceptions of those effectually them. If so, where individuals live should have an of import bear on on how they identify because unlike locations diverge in how left or right leaning they tend to be overall, and thus should exert dissimilar social influences. For example, "moderately liberal" might mean something different to someone in a relatively Republican region of the land than it does to someone in a Democratic region. As such, we hypothesize that the political "blueness" or "redness" of people'southward locations will influence where they view themselves on the political spectrum.

This reasoning is consistent with two competing hypotheses. Showtime, it is possible that people feel pressure to conform to the identity that is prevalent in their location [23]. In other words, one might have to concur peculiarly liberal views to resist conformity and identify equally a liberal in a red state, or particularly conservative views to identity as a conservative in a blue state. If this were the example, so location should "pull" people's identities out of alignment with their views; as states become bluer, the same identity would be associated with more right-leaning views, and as states get redder, the same identity would be associated with more left-leaning views. We deem this first hypothesis the "identity conformity hypothesis".

Alternatively, it is possible that people use the identity of those around them equally a "political reference point"–a point with which to compare themselves and adjust accordingly [19]. In red locations, if individuals perceive their political stances to be to the left of most of those effectually them, they may place as strongly liberal even though their positions (nationwide) are not well-nigh as extreme as those individuals identifying as strong liberals who live in bluish locations. In dissimilarity, individuals in blueish locations who perceive their politics to exist to the right of those around them will exist more than probable to identify every bit strongly conservative even though their positions (nationwide) are much more than moderate than those identifying as strongly conservative in cherry locations. In other words, the political reference betoken in blue and red locations differs, and when individuals determine where they stand on the political spectrum, they will compare themselves to their respective reference points.

Both alternatives share an important implication: how a person identifies on the political credo spectrum takes on dissimilar pregnant depending on where people alive. We test this possibility by measuring participants' cocky-reported political identity and their attitudes on various policy issues, examining whether the relationship betwixt political identity and policy attitudes shifts due to the blueness versus redness of one's location. We outset by exploring state level furnishings with data from the American National Ballot Study (ANES). So, we examine whether the effects replicate at the more than nuanced county level using data we nerveless specifically to examination our hypotheses.

With data from the ANES (N _issues = one,809, N _voting = 3,862) we assessed the correspondence between participants' reported political identity (e.yard., "slightly conservative") and their stances on 9 political bug (due east.k., abortion). Nosotros examined whether this correspondence shifts depending on land "blueness" (i.due east., the percentage of voters that voted for Obama vs. Romney in the 2012 presidential election), testing specifically whether individuals–regardless of political identity–hold unlike policy positions depending on their state. We tested whether this effect would replicate in a second sample (Due north _issues = 1,269, Due north _voting = ane,056), assuasive for a more than refined test at the canton level to see whether the effect generalized to a unlike set up of 10 politically relevant issues. In both samples we besides tested whether blueness (this fourth dimension calculated from the previous election's results to avoid redundancy) predicted voting in the upcoming presidential election.

Materials and methods–Report i

For extended methods and results for Study ane and ii, see Supplementary Data available online. All research reported beneath was approved by the University of Toronto Inquiry Ethics Lath (#31102). Participants consented electronically past clicking an "I consent" button, a method approved by the ethics board.

In Report 1 we examined whether specific political identities (eastward.m., "potent conservative") were associated with unlike positions on issues depending on ane's state of residence. To practise and then we used data from the publicly available American National Election Survey (ANES) dataset. Participants indicated their political identity using a 7-point scale (1 = Extremely liberal, 2 = Liberal, iii = Slightly liberal, four = Moderate, middle of the road, 5 = Slightly conservative, half-dozen = Conservative, 7 = Extremely conservative). Responses were reverse coded so that higher scores indicated greater liberalism.

Participants' views on 9 political bug (eastward.g., affirmative activity) were used to create a composite score called "issue position" (α = .79; higher scores indicate more liberal positions). This yielded a sample of N = 1809 participants for analyses involving issues.

From each participant'south state of residence we were able to determine the pct of people in that land that voted Democrat or Republican in the 2012 national election. Values for percent-voting-Democrat and percent-voting-Republican were highly correlated, r(1809) = -.99, p < .001, so we used percentage-voting-Democrat (state blueness) values throughout.

The ANES dataset provided us with the opportunity to assess whether political identities were associated with dissimilar voting behavior depending on ane'south state of residence. Participants indicated the person they voted for (1 = Democrat, 2 = Republican) during the 2012 Presidential ballot. Nosotros recoded responses such that higher values respective to voting Democrat. For analyses involving voting behavior we used state blueness values from the previous ballot (i.e., 2008) to avert redundancy between the land blueness predictor variable and the voting outcome variable. For analyses of voting intentions, nosotros collapsed political identity into three categories (conservative, moderate, and liberal) as we were primarily interested in comparing identity-consequent and identity-inconsistent voting across states (N.B. treating political identity equally a continuous variable does not qualitatively modify the results of either study). Participants were included in analyses if they voted for a Democratic or Republican candidate and responded to the identity item. This yielded a sample of N = 3,862 participants for analyses involving voting.

Results–Study i

The ANES data were consistent with the political reference point hypothesis (Fig 1) and not the identity conformity hypothesis. We ran a stepwise regression predicting issue position from political identity (centered; Pace 1), state blueness (centered; Stride 2) and their interaction (Step 3). This assay revealed that even after accounting for differences in self-reported political identity, the bluer the land individuals lived in, the more their policy positions aligned with a liberal stance, R ² _change = .004, F(ane, 1806) = 12.32, p < .001 (Fig A in S1 File). Exploratory analyses revealed that at that place was likewise an unpredicted interaction between political identity and country blueness, R ² _change = .002, F(1, 1805) = eight.11, p = .004. Simple gradient analyses indicated that state blueness significantly predicted policy positions for both the conservatives and moderates, but not for liberals (Tabular array B in S1 File). In other words, conservatives and moderates in blue states indicated more support for liberal policy positions than conservatives and moderates in scarlet states, and the bluer the country was, the stronger their back up was for liberal positions. This effect also extended to people's actual behavior–people in bluer versus redder states were more likely to vote Democrat in the 2012 Presidential election, even after controlling for political identity, χ²(1) = nine.84, p = .002 (Fig B in S1 File). Thus, people who reported the same political identities differed in their political stances and voting beliefs depending on the redness versus blueness of their land.

We also tested whether these furnishings replicated in 10 additional samples using ANES information from every election twelvemonth going back to 1972 (Due north _problems = viii,336, N _voting = 12,842). Data collected over the past 44 years once more demonstrated that even when accounting for political identity, the bluer the state individuals alive in the more than liberal the positions they will agree, R ² _change = .005, F(1, 8333) = 57.15, p < .001, and the more than likely they will be to vote Democrat, χⁱⁱ(1) = 117.30, p < .001 (Figs C–10 in S1 File; Tables E–BB in S1 File). In this example the interaction between political identity and state blueness was barely significant, R ² _change = .000, F(1, 8332) = 3.99, p = .046, and exhibited a different pattern than that observed in the 2012 data (with the strongest effect of state blueness amongst moderates), suggesting that interactive effects may be inconsistent over fourth dimension.

Materials and methods–Study 2

In Study 2 we assessed the same question as in Written report i with a sample nerveless specifically to test our hypotheses. Hither, we were able to assess "blueness" at the canton level, allowing a more nuanced exam of our master question. We too did targeted recruiting of participants in order to ensure that the extremes of political identity (i.e., potent conservatives and strong liberals) were well-represented. Finally, this report also immune us to test the generalizability of our previous results past testing different political issues.

Because there was little precedent for estimating these furnishings, we decided to collect a relatively large sample. For each political identity position (ane = strong conservative, 2 = conservative, 3 = moderate conservative, 4 = moderate, 5 = moderate liberal, 6 = liberal, 7 = strong liberal) we aimed to recruit 100 mTurk workers from red states and 100 from blueish states from a large pre-screened sample (Due north of approximately 30,000). A total of 1,349 participants began the survey and 1,269 finished all relevant items. This sample of 1,269 people was used in analyses involving result position (M _{historic period} = 39.80, SD _age = 12.94, 672 female; see Table D in S1 File for number of participants in each category).

Participants indicated their position (-5 = strongly oppose, 5 = strongly in favor) on 10 political issues. V issues were traditionally liberal (e.m., social welfare) and five issues were traditionally bourgeois (eastward.thou., a strong military). As predicted, after opposite coding the 5 traditionally conservative issues the 10 items yielded a reliable composite score which nosotros labeled "issue position" (α = .89; college scores bespeak more than liberal positions).

From each participant's naught code listed in the prescreening data, we were able to decide the percentage of people in that canton that voted Democrat or Republican in the 2012 national ballot. Because values for percent-voting-Democrat and percentage-voting-Republican were highly correlated, r(1269) = -.99, p < .001, nosotros decided to apply percentage-voting-Democrat (canton blueness) values throughout.

Participants as well indicated their voting intentions for the upcoming (2016) presidential election (a Democratic candidate, a Republican candidate, or an independent candidate). As in Study 1 nosotros collapsed political identity into iii categories (conservative, moderate, and liberal). Participants who did non specify either a Democratic or Republican candidate were excluded (due north = 213), yielding a sample of 1,056 participants for analyses involving voting intentions.

Results–Written report 2

In the second data set we observed support for the political reference point hypothesis at the county level. Running the same analysis as in Study 1 revealed a significant upshot of county blueness, R ² _change = .005, F(1, 1266) = 15.76, p < .001, such that bluer counties were associated with more than liberal event positions controlling for identity. Exploratory analyses revealed an interaction between political identity and canton blueness, R ² _change = .005, F(i, 1265) = 14.76, p < .001. Uncomplicated slope analyses indicated that county blueness was a pregnant predictor of policy positions for everyone except for liberals and strong liberals (Fig 2; Table F in S1 File). In add-on, people in bluer (vs. redder) counties were more likely to say they intended to vote Democrat in the upcoming 2016 election, χⁱⁱ(i) = 7.xv, p = .007 (Fig 3).

Fig 2. County blueness indicates percentage of participant'due south canton voting democrat in the 2012 presidential ballot.

Lines are linear trendlines calculated separately for each level of political identity. If political ideology had the same meaning regardless of location the trendlines would be flat.

https://doi.org/10.1371/journal.pone.0171497.g002

Fig 3. Voting intentions by county blueness for each political identity category.

County blueness is divided into quartiles for visualization purposes, but was treated every bit a continuous variable in all analyses. If political credo had the same pregnant regardless of location all bars of the same colour would be the same height.

https://doi.org/x.1371/periodical.pone.0171497.g003

General give-and-take

These findings demonstrate that political identity, though typically understood every bit having universal pregnant, is actually dependent on social context; from location to location ideological classifications hateful different things. Although the effects we found were small (R ² _change = .004 in Study one, and R ² _change = .005 in Report 2), we think they are meaningful for at to the lowest degree two reasons. First, any effect is notable given that political identity is often used as a proxy for consequence position, and thus incremental predictive power should be hard to come by. In particular, the fact that this consequence is in the aforementioned management across different samples is telling given that there were plausible reasons to expect no effect, or even the opposite effect.

Second, even small effect sizes can outcome in meaningful differences. For case, the ANES data showed that extremely conservative people in Utah (24.ix% blue) were reluctant to consider legalizing abortion fifty-fifty in cases of rape, whereas extremely conservative people in Hawaii (seventy.60% blue) were willing to consider legalizing abortion in a range of circumstances. In the second data ready, when information technology came to their views on having a potent military, moderates in the bluest counties were finer indistinguishable from strong liberals in the reddest counties (with Gs of .33 and .38, respectively). In the 2016 Presidential election, a moderate in Philadelphia County, PA (85.2% blue) was more than 3 times every bit likely to indicate an intention to vote Democrat compared to a moderate in Brown County, TX (13.vii% blue). Thus, those who identify in the exact same way on the political ideology spectrum may possess markedly different policy stances and preferences; meanwhile, people with unlike identities may hold identical or fifty-fifty ideologically reversed views (Fig 4).

Fig 4. An illustrative instance of the outcome of location on policy position.

SC = strong conservative identity; SL = strong liberal identity. Red Texas symbols correspond people in the 100 reddest counties in America; Blue New York symbols represent people in the 100 bluest counties in America. 1 conservative and one liberal effect were chosen for illustrative purposes. Data are from Study 2.

https://doi.org/10.1371/periodical.pone.0171497.g004

Information technology is important to consider that the event of land blueness in the ANES datasets was more robust for some years than it was for others, peradventure suggesting that people's use of others around them every bit a political reference point might wax and wane depending on other time-sensitive influences. Presumably, the outcome depends on the extent to which individuals utilize people "typical" of their location (in terms of sociopolitical characteristics) to calibrate their own political identity, then the effect may fluctuate to the extent that this behavior fluctuates. That said, the effect appears to exist reliable given that it was nowadays when considering the ANES data equally a whole. Moreover, Study two replicated the effect using a targeted sample that included participants with all levels of political identification (including a big sample of those on the extremes) and with a singled-out fix of policy items.

All in all, we believe our findings highlight how the meaning of political identity is not objective, but rather socially determined–a point rarely considered past researchers or the lay public. Along these lines, our results may also align with theorizing about political ideology having both symbolic and operational components [24, 25], where the first refers to more abstract categories and stereotypes, and the latter refers to the more concrete policy positions. When individuals answer questions about political identification they might rely more than on symbolic ideology, but when answering policy preference items they might rely more on operational ideology [5]. If the social environment impacts one of these ideologies more than the other, information technology may assist business relationship for why a growing number of studies, including the present one, find a disconnect between political identification and policy attitudes (e.g., [8, 26–29]).

On a related note, although the results we institute highlight how one's location affects the relationship betwixt political identity and policy attitudes, we cannot make directly claims almost causality. What exactly does 1's social environment affect–political identity, policy attitudes, both? The answer would seem to depend on which of these 2 is more susceptible to social influence. Existing literature on social identity–i.due east., 1'due south sense of self based on social grouping membership [30, 31]–suggests that it tin be malleable depending on the social context [32, 33], whereas many studies suggest that political stances, especially those grounded in moral convictions, are less flexible and may become fifty-fifty more strongly held when faced with contrasting viewpoints [34–36]. Thus, it may be that where individuals live more strongly affects their reported identity, simply has less of an impact on their actual behavior and stances. Future research that follows individuals who move from strongly blueish to red, or red to blueish, locations would help provide insight into exactly how people's surroundings influence the relationship between political identity and policy stances.

Overall, our results suggest that researchers who rely on political identity equally a proxy for classifying and estimating written report participants' beliefs and voting beliefs may exist using an oversimplified measure. Pollsters who administer specific questions to respondents based on their reported identity may be introducing mistake into their polling past not accounting for county-to-county variation in what those identities hateful. Additionally, pollsters and the media often nowadays polling results grouped into political identity clusters to provide a snapshot of attitudes across the political spectrum. Just, once more, this snapshot may be inaccurate unless the location of each pollee is taken into consideration. In sum, assessing political identity without because location ignores the impact of social context.

Our findings also propose that the animosity and disgust so commonly felt toward those on the other side of the political ideology spectrum may often be misplaced. Indeed, our results advise that if a person feels hatred toward others only based on how they place on the political ideology spectrum, then in some circumstances, that hatred is really aimed at someone with the exact same policy stances. Conversely, the ingroup favoritism unremarkably afforded those with the same political identity (east.g., [37]) may ironically involve giving preferential treatment to individuals who agree the opposing viewpoint on diverse problems. Overall, our findings suggest that it is important to consider that, oft, it is non the policy preferences or the values that differ betwixt people, just but the labels they requite themselves–labels that shift depending on their political reference indicate.

Supporting data

S1 File.

Fig A) Land blueness indicates pct of participant's state voting democrat in the 2012 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from Written report ane. Fig B) Country blueness indicates percentage of participant'southward country voting democrat in the 2008 presidential election. Data are from Study i. Fig C) Land blueness indicates the percentage of each participant's country voting Democrat in the 2008 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig D) Country blueness indicates percentage of participant's state voting democrat in the 2004 presidential ballot. Data are from the ANES. Fig Due east) State blueness indicates the percentage of each participant's state voting Democrat in the 2004 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig F) State blueness indicates percentage of participant'due south state voting democrat in the 2000 presidential ballot. Data are from the ANES. Fig G) State blueness indicates percent of participant's state voting democrat in the 2000 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Information are from the ANES. Fig H) State blueness indicates percentage of participant's state voting democrat in the 1996 presidential election. Data are from the ANES. Fig I) State blueness indicates pct of participant's state voting democrat in the 1996 presidential ballot. Lines are linear trendlines calculated separately for each level of political identity. Information are from the ANES. Fig J) State blueness indicates percent of participant's state voting democrat in the 1992 presidential election. Information are from the ANES. Fig Grand) Land blueness indicates percentage of participant's land voting democrat in the 1992 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig Fifty) State blueness indicates percentage of participant's state voting democrat in the 1988 presidential election. Data are from the ANES. Fig G) State blueness indicates pct of participant'due south state voting democrat in the 1988 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig N) State blueness indicates percentage of participant'south state voting democrat in the 1984 presidential ballot. Data are from the ANES. Fig O) State blueness indicates percentage of participant'southward state voting democrat in the 1984 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig P) State blueness indicates percentage of participant's country voting democrat in the 1980 presidential ballot. Information are from the ANES. Fig Q) State blueness indicates percentage of participant's state voting democrat in the 1980 presidential election. Lines are linear trendlines calculated separately for each level of political identity. Information are from the ANES. Fig R) State blueness indicates per centum of participant's state voting democrat in the 1976 presidential election. Data are from the ANES. Fig S) Land blueness indicates percentage of participant's state voting democrat in the 1976 presidential ballot. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig T) Country blueness indicates per centum of participant's state voting democrat in the 1972 presidential election. Data are from the ANES. Fig U) Land blueness indicates percent of participant's country voting democrat in the 1972 presidential ballot. Lines are linear trendlines calculated separately for each level of political identity. Data are from the ANES. Fig V) State blueness indicates per centum of participant's state voting democrat in the 1968 presidential ballot. Data are from the ANES. Fig West) State blueness indicates the percentage of each participant'due south state voting Democrat in the corresponding year's presidential election. Data include all election years from 1972–2008. Lines are linear trendlines calculated separately for each level of political identity. Information are from the ANES. Fig 10) Land blueness indicates percentage of participant'due south state voting democrat in the previous presidential election. Data include all election years from 1972–2008. Information are from the ANES. Table A) Descriptive statistics for outcome position by political identity. Data are from Study 1. Table B) Unstandardized betas for simple slopes for issue position by state blueness at each level of political identity. Data are from Written report 1. Table C) Key results from stepwise regressions conducted for each upshot individually. The R ^two _change statistic is for Footstep 2 of a stepwise regression in which issue stance is predicted from political orientation (Step one), location (Step two) and their interaction (Step iii). Issues are recoded such that scores are on a scale of 0 to 1, and higher scores indicate more than liberal positions. Information are from Written report ane. Table D) Number of participants by state color and political identity. For political identity, 1 = strong bourgeois, 2 = conservative, three = moderate bourgeois, iv = moderate, v = moderate liberal, half dozen = liberal, 7 = strong liberal. Data are from Study ii. Tabular array E) Descriptive statistics for effect position past political identity. Data are from Study 2. Tabular array F) Unstandardized betas for uncomplicated slopes for result position past state blueness at each level of political identity. Data are from Study 2. Tabular array K) Key results from stepwise regressions conducted for each issue individually. The R ² _alter statistic is for Stride two of a stepwise regression in which event stance is predicted from political orientation (Pace 1), location (Step 2) and their interaction (Stride 3). Issues are recoded such that higher scores indicate more than liberal positions. Data are from Written report 2. Table H) Items included in "upshot position" variable, by year. Data are from the ANES. Table I) Issue position data for the twelvemonth 2008. Unstandardized betas for unproblematic slopes for issue position by state blueness at each level of political identity. Statistically pregnant results are highlighted in bold. Data are from the ANES. Table J) Voting data for the year 2008. Unstandardized betas for simple effects for voting by state blueness at each level of political identity. Statistically significant results are highlighted in assuming. Information are from the ANES. Tabular array K) Outcome position data for the twelvemonth 2004. Unstandardized betas for simple slopes for consequence position by state blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Table L) Voting information for the year 2004. Unstandardized betas for simple effects for voting by country blueness at each level of political identity. Statistically pregnant results are highlighted in bold. Information are from the ANES. Table M) Issue position data for the year 2000. Unstandardized betas for uncomplicated slopes for event position by state blueness at each level of political identity. Statistically significant results are highlighted in assuming. Data are from the ANES. Table N) Voting data for the twelvemonth 2000. Unstandardized betas for simple effects for voting past state blueness at each level of political identity. Statistically pregnant results are highlighted in assuming. Data are from the ANES. Tabular array O) Issue position data for the year 1996. Unstandardized betas for unproblematic slopes for issue position by state blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Table P) Voting data for the yr 1996. Unstandardized betas for simple furnishings for voting by state blueness at each level of political identity. Statistically significant results are highlighted in assuming. Data are from the ANES. Table Q) Result position data for the year 1992. Unstandardized betas for simple slopes for issue position by state blueness at each level of political identity. Statistically meaning results are highlighted in bold. Data are from the ANES. Table R) Voting data for the year 1992. Unstandardized betas for simple furnishings for voting by state blueness at each level of political identity. Statistically pregnant results are highlighted in assuming. Data are from the ANES. Table Southward) Issue position data for the twelvemonth 1988. Unstandardized betas for simple slopes for issue position by state blueness at each level of political identity. Statistically significant results are highlighted in assuming. Data are from the ANES. Tabular array T) Voting data for the year 1988. Unstandardized betas for simple effects for voting by country blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Table U) Outcome position information for the year 1984. Unstandardized betas for simple slopes for issue position by land blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Table V) Voting data for the twelvemonth 1984. Unstandardized betas for simple effects for voting by state blueness at each level of political identity. Statistically pregnant results are highlighted in bold. Data are from the ANES. Table W) Issue position data for the year 1980. Unstandardized betas for simple slopes for upshot position by state blueness at each level of political identity. Statistically pregnant results are highlighted in assuming. Data are from the ANES. Table X) Voting information for the year 1980. Unstandardized betas for uncomplicated effects for voting by state blueness at each level of political identity. Statistically pregnant results are highlighted in bold. Data are from the ANES. Table Y) Event position data for the twelvemonth 1976. Unstandardized betas for elementary slopes for issue position by state blueness at each level of political identity. Statistically pregnant results are highlighted in assuming. Information are from the ANES. Table Z) Voting information for the year 1976. Unstandardized betas for uncomplicated effects for voting past state blueness at each level of political identity. Statistically meaning results are highlighted in bold. Data are from the ANES. Table AA) Outcome position information for the year 1972. Unstandardized betas for uncomplicated slopes for consequence position by land blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Tabular array BB) Voting information for the year 1972. Unstandardized betas for uncomplicated furnishings for voting by state blueness at each level of political identity. Statistically pregnant results are highlighted in bold. Data are from the ANES. Table CC) Issue position data for all election years combined (1972 through 2012). Unstandardized betas for simple slopes for issue position past state blueness at each level of political identity. Statistically pregnant results are highlighted in assuming. Data are from the ANES. Tabular array DD) Voting data for all election years combined (1972 through 2012). Unstandardized betas for simple effects for voting by state blueness at each level of political identity. Statistically significant results are highlighted in bold. Data are from the ANES. Table EE) Summary of regression results for result position. Information are from the ANES. Table FF) Summary of regression results for voting behaviors. Data are from the ANES.

https://doi.org/10.1371/periodical.pone.0171497.s001

(DOCX)

Author Contributions

1. Conceptualization: MF AT.
2. Data curation: MF AT WH ZM SG.
3. Formal analysis: MF AT WH.
4. Investigation: MF AT.
5. Methodology: MF AT.
6. Project administration: MF AT.
7. Resource: MF AT ZM WH SG.
8. Supervision: MF AT.
9. Validation: MF AT ZM WH.
10. Visualization: MF AT.
11. Writing – original typhoon: MF AT.
12. Writing – review & editing: MF AT ZM WH SG.

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How Does Geographical Region Affect The Makeup Of Politcal Parties,

Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171497

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