Major Essay

ASSIGNMENT COVER SHEET[School]College of Arts and Social SciencesAustralian National UniversityCanberra ACT 0200 Australiawww.anu.edu.au +61 2 6125 [email protected]

Socy 2038

Assignment details (assignment number, title, etc)Assignment 3

LecturerJoanna Sikora

TutorJoanna Sikora

Tutorial (day and time)Thursday 1400

Word count2595Due date10/062014

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University IDU5179320Phone contact0447 007 754

(I tick the above boxes)Age, Internet Use, and Party IdentificationIntroductionOver the course of the 21st century, one of the more popular topics within political science has been the role the internet has to play in democracy. For instance, online news source such as blogs are having an increased impact upon the media cycle (Farrell and Drezner, 2007). This essay will question whether this theory is applicable to the Australian political scene. Using data from the 2010 Australian Electoral Survey (AES) (McAllister, 2011?), this essay will seek to assess whether the extent of internet use an individual conducts is an indicator of said individuals allegiance to a political party. Theoretical FrameworkIdeally, this essay would look at internet use in relation to positioning on the left/right political axis. The issue with placing oneself on the political spectrum are too numerous to count. While the basic concept of left/right on the political axis are vaguely understood by people, there is a variety of identifying features between these two points which make it difficult to define what each of these specifically mean (Nosek & Haidt, 2012). Most people measure it through placing parties in relation to one another, which is an issue when one gets into comparing perspectives of political partisans (Nosek & Haidt, 2012). This suggest an almost Foucaultian power-relationship model where nothing is concrete, they are instead defined by their relationships with others (Veyne, 2010, 33). From this point it would arguably make more sense to measure political identity by party, rather than by a left/right axis model. Within the Australian context, this can easily be summarised as the Greens sit to the left of the major political parties, with Labor and Liberal occupying the centre-left and centre-right spots respectively. This conforms to the Downs-Hoteling Model, which suggests the major two parties will converge to the centre politically in order to maximise votes. (Cho, & Duggan, 2009, 852) This also accounts for the Greens, who are taking advantage of a political space a third party in the more logical position, where they can attract more votes rather than vying for control in the centre of the political spectrum. The Nationals are in a different position however, as while they are seen as being close on the spectrum to the liberals they have a distinct regional focus. This to a certain extent contrasts with accepted notions of party competition, through the formation of the coalition between these two centre-right parties. This is a power sharing arrangement with the Liberals, which for our purposes will focus on one area; they have agreed to not compete within single member districts in order to not undermine their voter base (Costar, 2011, 31). We can clearly see that while we can vaguely locate political parties on the spectrum, it is far more effective to define them in relation to other parties than to a flawed conceptual axis. A recent trend within politics is an increase in online tools for campaigning, and politics in general (Cantijoch & Gallego, 2009, 862). The recent (2014) Indian election highlighted Narendra Modi, the successful candidate for President, was actively using a Twitter account, where his main rival did not (Economic and Political Weekly, 2014). President Obama was similarly successful in pursuing an online strategy, which was able to galvanise younger and more internet savvy voters to support him over not voting at all. (Mazmanian, A. 2012,). Logically, more frequent internet users in the age of online politics would have an increased amount of exposure to political messages, conceivably impacting their political identification.

Hypotheses1. Difference in levels of internet use between left wing party supporters and right wing party supportersWith more progressive online groups being able to drive political debate in a way which favours their agendas, there must logically have been a support base online who were supportive of them. Similarly, Indy-media groups have flourished in the online environment who have been extremely successful in being able to use the medium to spread their message, when compared with more conservative online presence (Garcelon, 2006). Therefore, there should be a connection between internet use and a more left leaning political identity. 2. Does an increase in party allegiance change the level of internet useWithin groupings of more avid supporters of movements, there is a trend towards being more active within that groups activities (Garcelon, 2006). Given the internet age has seen a lot of these groups becoming active online (Garcelon, 2006), it stands to reason that the more active supporters of such groups are, the more likely they are to participate in that group. Therefore hen such groups have an online presence, the more avid supporters should exhibit a greater participation in that site.MeasurementThe Australian Electoral Survey (AES) conducts a poll just after every Australian Federal Election (roughly every three years) in order to find out a series of details which may have influenced the outcome of the election. In addition to this, they garner a variety of measurements of a variety of activities/possible indicators. The survey is designed to gather information of peoples political positions and other factors which theoretically influence it. In order to test the above hypotheses, this essay uses a series of questions from the AES. These include:H10: In general, how often do you use the internet? Several times a day 38.7% (7 points) About once a day 17.0% (6 points) Three to five days a week 6.2% (5 points) One to two days a week 6.0% (4 points) Every few weeks 3.5% (3 points) Less often 2.6% (2 points) Do not use the internet 26.1% (1 point)From these numbers above, there is a distinct pattern in internet use, where there is a high frequency of use amidst internet users. This said, there is a notable exception, in that just over a quarter of respondents to the survey did not use the internet. For the purpose of this conducting an OLS analysis, each of the values had their value flipped (to what is listed above) in order to give a greater score to those with greater internet use. B1: Generally speaking, do you think of yourself as Liberal, Labor, National or what? Liberal 37.3% Labor 39.5% National Party 3.4% Greens 4.6% Other party 3.6% No party 11.7%As the measurement of someones political identity, B1 is important to this study. However, it is also comprised of nominal values, which cannot be analysed effectively as a group. Instead, for the purposes of looking at the effects of internet usage on party identification, each of these will be looked at as a dummy variable. B2: Would you call yourself a very strong, fairly strong, or not very strong supporter of that party? Very strong supporter 19.7% Fairly strong supporter 52.9% Not very strong supporter 27.4%Extending the party identification by ascertaining the extent of support for a party theoretically shows that someone is more committed to that party. These measurements will be looked at primarily in percentages, and from there within party groups. This is to offset the numerical dominance of respondents who identified as Liberal and Labor party supporters.ResultsFig. 1: Levels of Internet use by Party Identification

The interesting points made in Figure 1, is that there are a series of pairings. First, the level of internet use by the Greens runs close to the internet use of individuals who do not identify with a party, with a far larger percentage of those groups have high levels of internet use. This is particularly true for the more frequent users, less so when looking at those who did not use internet at all when surveyed. Labor, liberal and minor party supporters are also are almost on par in terms of internet use, while the Greens have a heavy saturation of frequent internet users. Given this, it is surprising that there was only a relationship in level of support for a party and level of internet use amidst Labor and the Nationals. One would have assumed that the Greens would have exhibited an equally strong relationship, given 2010 was a period where they were no longer seen as the choice for the protest vote (Blount, 1998). The nationals break the pattern established by the other parties. Where most parties have a plurality of supporters who frequently use the internet on a daily basis, the nationals have more supporters who do not use the internet. This arguably can be expected since the Nationals do have a focus on rural areas. Table 1. Regression analysis of Party identification and weighted internet useUnstandardized CoefficientsStandardized Coefficients

BStd. ErrorBetatSig.

(Constant)5.1150.15333.4380

Greens supporters0.7670.290.0652.6440.008

Labor supporters-0.6190.175-0.121-3.5340

Liberal supporters-0.4850.176-0.094-2.7480.006

National supporters-0.8610.324-0.063-2.6560.008

Minor Party Supporter-0.1810.324-0.013-0.5590.576

a. Dependent Variable: Weighted use of internet

These results are reiterated through a regression analysis. Upon the original weighting of the weighted internet use variable, there is a high level of internet use with supporters of the Greens having an above average response, where the other parties fall show. Upon standardisation, this is still apparent, although variation in a comparative sense is minimal.

Figure three shows some interesting points. While as a group as whole, the null hypothesis is correct, this does not apply when you look at specific groups of party supporters. Given our data comes from ordinal sources, we only have to look at the Linear-by-Linear Associations, which show that there are a few parties for which the null hypothesis can be rejected. These include the National party and the Labor party. It is interesting that these two are the only two where there is any statistical significance through having scores less than 0.05.

Table 2. Collated Chi-Square tests for correlations between weighted internet use and weighted party supportValuedfAsymp. Sig. (2-sided)

Valid respondentsPearson Chi-Square10.631a120.561

Likelihood Ratio10.49120.573

Linear-by-Linear Association2.66510.103

N of Valid Cases1866

Liberal supportersPearson Chi-Square10.883120.539



N of Valid Cases789

National supportersPearson Chi-Square23.743a120.022



N of Valid Cases75

Greens supportersPearson Chi-Square6.949b120.861



N of Valid Cases100

Labor supportersPearson Chi-Square17.89120.119



N of Valid Cases838

Minor Party supportersPearson Chi-Square8.728c100.558



N of Valid Cases61

a 15 cells (71.4%) have expected count less than 5. The minimum expected count is .27.

b 16 cells (76.2%) have expected count less than 5. The minimum expected count is .17.

c 14 cells (77.8%) have expected count less than 5. The minimum expected count is .64.

With that in mind, looking at the correlations between internet use and strength of support in the Labor party and the National party (In fig. 4) some interesting trends become apparent. For those who identify with the nationals, individuals who did not have strong support for the party they identified with were far more likely to not use the internet (45.5%), whereas if they were a strong supporter they were far less likely to not use the internet. Looking at the top two grouping of internet users, there is just over 30% who are not strong supporters of the nationals. For those who would consider themselves strong supporters, there are two peaks, with a trend towards being more frequent internet users. This manifests itself in the top two brackets with a little over 50% considering themselves. When looking at very strong supporters of the nationals, the pattern presented by not very strong supporters was reversed. 70% consider themselves to be more frequent internet users. This suggests that in the case of the nationals there was a clear connection between internet use and a stronger support for the party. This implies the nationals had far more devoted followers on the internet.Table 3. National and Labour supporters strength of support and internet useDoesn't Use Internet23456Frequent Internet UserTotal

National supportersNot strong support45.50%9.10%9.10%4.50%4.50%27.30%100.00%

Strong support30.20%7.00%9.30%32.60%20.90%100.00%

Very strong support10.00%20.00%40.00%30.00%100.00%

Total32.00%2.70%2.70%6.70%6.70%25.30%24.00%100.00%

Labor supportersNot strong support23.60%3.50%5.20%9.20%6.10%14.40%38.00%100.00%

Strong support26.90%2.60%3.20%7.70%6.20%15.70%37.60%100.00%

Very strong support36.80%6.30%2.80%5.60%7.60%10.40%30.60%100.00%

Total27.70%3.50%3.70%7.80%6.40%14.40%36.50%100.00%

The correlations presented about the supporters of the Labor party present a different picture. Not so strong supporters of the party had a higher chance of being more frequent internet users when compared with very strong Labor supporters (38.00% down to 30.60%). There is a similar trend when there is a reduction in usage of the internet, until you get to the bottom two groupings of internet use; those who dont use the internet and those who are quite infrequent with their internet use. The pattern for support of the Labor party reverses, where there is an increased likelihood of stronger support for the Labor party when the individual either does not use the internet or does so on an infrequent basis. When these two correlations are compared, an interesting trend occurs. Popular logic would suggest that the party which was traditionally presented as being centre-left (Labor) would have had a greater amount of support amidst its online user groups, given the more progressive nature the internet is typically given politically. Therefore it is interesting to see that greater support for this party existed amidst individuals who had little to no internet use. In contrast, the nationals had the reverse situation. Given the rural nature of the party, this is surprising given the level of internet in rural areas is far lower than in urban environments (Ward, Singleton & Martyn, 1998, 117). Said Urban environments have a stronger tendency to be affiliated with voting towards Labor, so this dichotomy in support in online avenues is interesting. These two points are intriguing when we look at percentage of internet use within each party. As a whole, frequent internet users are far more common within the Labor party, whereas the Nationals have more supporters who do not use the internet than those who use the internet on a regular basis.

SummaryWhile there was certainly a trend after the 2010 elections within the most left leaning major party, the Greens, towards having the largest percentage of frequent internet users, there was not a similar correlation within this left leaning group amidst the level of support for the party.The idea that left leaning parties had a much higher level of internet use was definitely supported when you compare the levels of internet use National Party supporters with that of the Greens. What was an interesting trend was that the Nationals had a significant trend amidst its internet users of a relationship between an increase in internet use and The Labor Party and the Liberal Party had very similar levels of internet use amidst its supporters, which works against the assumption of supporters of left leaning parties having greater internet usage compared to the supporters of more right wing parties.

ReferencesAnduiza, E., Cantijoch, M. & Gallego, A. 2009, "POLITICAL PARTICIPATION AND THE INTERNET", Information, Communication & Society, vol. 12, no. 6, pp. 860-878.Blount, S. 1998, "Post materialism and the Vote for the Senate in Australia", Australian Journal of Political Science, vol. 33, no. 3, pp. 441-449.Cho, S. & Duggan, J. 2009, "Bargaining foundations of the median voter theorem", Journal of Economic Theory, vol. 144, no. 2, pp. 851-868.Costar, B. 2011, "Australias curious coalition", Political Science, vol. 63, no. 1, pp. 29-44.Mazmanian, A. 2012, Obama's 50-State Twitter Campaign, Atlantic Media, Inc., Washington."Modi's Modus Operandi in the 2014 Elections", 2014, Economic & Political WeeklyGarcelon, M. 2006, "The "Indymedia" experiment: The Internet as movement facilitator against institutional control", Convergence: The Journal of Research into New Media Technologies, vol. 12, no. 1, pp. 55-82.Graham, J., Nosek, B.A. & Haidt, J. 2012, "The Moral Stereotypes of Liberals and Conservatives: Exaggeration of Differences across the Political Spectrum: e50092", PLoS One, vol. 7, no. 12.Veyne, P. & Lloyd, J. (. 2010, Foucault: his thought, his character, Polity, Malden, MA; Cambridge, UK.Ward, Jeff Singleton Paul Martyn Ian 1998, "Did the 1996 Federal Election See a Blue-collar Revolt against Labor? A Queensland Case-study", Australian Journal of Political Science, vol. 33, no. 1, pp. 117-130.Appendix I: Syntax

{AES_2010.sav'.}

DATASET NAME DataSet1 WINDOW=FRONT.RECODE b1 (1=1) (ELSE=0) INTO B1_Lib.VARIABLE LABELS B1_Lib 'Liberal supporters'.EXECUTE.RECODE b1 (2=1) (ELSE=0) INTO B1_Lab.VARIABLE LABELS B1_Lab 'Labor supporters'.EXECUTE.RECODE b1 (3=1) (ELSE=0) INTO B1_Nat.VARIABLE LABELS B1_Nat 'National supporters'.EXECUTE.RECODE b1 (4=1) (ELSE=0) INTO B1_Grn.VARIABLE LABELS B1_Grn 'Greens supporters'.EXECUTE.RECODE b2 (1=3) (3=1) (2=2) INTO b2_weighted.VARIABLE LABELS b2_weighted 'weighted party support'.EXECUTE.RECODE h10 (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO h10_Weighted.VARIABLE LABELS h10_Weighted 'Weighted internet use'.EXECUTE.RECODE b1 (5=1) (ELSE=0) INTO b1_Min.VARIABLE LABELS b1_Min 'Other Parties'.EXECUTE.

* Chart Builder.GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=h10 COUNT()[name="COUNT"] b1 MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE.BEGIN GPL SOURCE: s=userSource(id("graphdataset")) DATA: h10=col(source(s), name("h10"), unit.category()) DATA: COUNT=col(source(s), name("COUNT")) DATA: b1=col(source(s), name("b1"), unit.category()) GUIDE: axis(dim(1), label("H10. How often use internet")) GUIDE: axis(dim(2), label("Percent")) GUIDE: legend(aesthetic(aesthetic.color.interior), label("B1. Party identification")) SCALE: cat(dim(1), include("7", "6", "5", "4", "3", "2", "1"), sort.values("7", "6", "5", "4", "3", "2", "1")) SCALE: linear(dim(2), include(0)) SCALE: cat(aesthetic(aesthetic.color.interior), include("4", "2", "1", "3", "5", "6"), sort.values("4", "2", "1", "3", "5", "6")) ELEMENT: line(position(summary.percent(h10*COUNT, base.aesthetic(aesthetic(aesthetic.color.interior)))), color.interior(b1), missing.wings())END GPL.

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT h10_Weighted /METHOD=ENTER B1_Grn B1_Lab B1_Lib B1_Nat b1_Min.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted BY B1_Lib /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted BY b1_Min /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted BY B1_Grn /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted BY B1_Nat /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

CROSSTABS /TABLES=b2_weighted BY h10_Weighted BY B1_Lab /FORMAT=AVALUE TABLES /STATISTICS=CHISQ /CELLS=COUNT /COUNT ROUND CELL.

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