In the AES between 2001 and 2013, seven questions were consistently asked concerning immigration or immigrants in addition to the occasional questions about asylum seekers. Noting that these questions all relate to persons coming to Australia, I was concerned that the question relating to whether asylum boats should be turned back may be somehow related to the immigration questions. Therefore, I undertook a factor analysis to determine if the questions on asylum seekers and immigrants belong to one latent variable. I also wanted to test whether other issues that the AES addresses, such as economic concerns and the treatment of minorities, might belong to a latent variable that also included variables concerning attitudes towards
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to my research questions, attitudes towards asylum seekers and immigrants, and that were consistently measured over time. Factor analysis is a useful technique as it can confirm or reject those apparent relationships and allow reduction of a larger number of variables into factors — which may be interpreted to reveal underlying concepts (De Vaus, 2002; Graetz & McAllister, 1994). It was also hoped that if underlying concepts, or factors, were identified, these could be used in multivariate analysis either as dependent or independent variables as necessary.
In order to prepare to identify latent variables, twenty-one variables were subjected to factor analysis using the combined dataset for the AES 2001–2013. A table presented the results is available in Appendix E, Table 8.9, p. 236. In summary, the following variables were included in the analysis:
• seven variables concerning immigration • the measure concerning asylum seekers
• two variables concerning attitudes towards Aboriginal people • four economic measures
• two measures concerning attitudes towards crime and punishment • three measures of party identification
• one measure of attitudes towards international engagement.
In the analysis of the variables, several conceptual factors (or latent variables) were identified. The theories discussed at length in Chapter 2 led me to suspect that
factors such as positive/negative attitudes towards immigration, economic insecurity, and attitudes towards a minority would be identified by the principal axis factoring method (Fabrigar et al., 1999). This was not strictly the case. Six latent variables were identified: attitudes towards immigrants, attitudes towards punitiveness, attitudes towards Aboriginal people, perceptions of past economic performance, future economic well-being, and political party affiliation.
The seven measures on immigration from the AES were identified as belonging to a latent variable and the factor analysis supports the combination of
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these variables into one latent variable, which could be combined into an additive scale. However, as the questions relate to several conceptually different aspects of attitudes towards immigration, a single latent variable was not constructed. Instead, the seven measures were divided into conceptual groupings as described below. It is notable, however, that the variable concerning attitudes towards asylum seekers was not identified in the analysis as belonging to the latent variable concerning attitudes towards immigration
Analysis of the dataset identified a punitiveness factor, which has a factor loading of 0.513. This factor included variables concerning a desire to see stiffer sentences handed down, the reintroduction of the death penalty, an increase in defence spending, and turning back asylum boats. The identification of this factor reveals that responses to the question concerning attitudes towards turning back boats carrying asylum seekers has stronger relationships with concerns about justice — punishment and defence — than immigrants more generally; the variable
concerning asylum seekers was not in the factor concerning immigration, and instead shares a relationship with variables concerning justice, measured by increasing the severity of judicial punishments, and the defence of Australia (measured by a desire to see defence spending increased). Moreover, the emergence of this factor in the analysis aligns perceptions concerning turning back boats carrying asylum seekers with perceptions of how to deal with other perceived threats to Australian society. Future research may consider the extent of the relationship between different kinds of perceived threats and asylum seekers.
The seven measures on immigration from the AES address support for various dimensions of immigration among the Australian population. These dimensions include support for immigrants, but also address government
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immigration policy, and the perceived effects of immigration on the Australian community. As shown below, the variables can be divided into three groups – all of which have high alpha coefficients as shown below. The variables, shown as they appear in the AES below, may be divided in this way:
Support for immigrants themselves (one measure)
• Equal opportunities for migrants.
Government immigration policy (Cronbach’s alpha coefficient, α = 0.819, n =
11,221)
• The number of immigrants allowed into Australia nowadays should be reduced or increased
• The number of migrants allowed into Australia at the present time has gone too far, or not far enough.
Effects of immigration (Cronbach’s alpha coefficient, α = 0.782, n = 11,273)
• Immigrants increase the crime rate
• Immigrants are generally good for Australia’s economy
• Immigrants take jobs away from people who are born in Australia
• Immigrants make Australia more open to new ideas and cultures.
Grouping the measures, and constructing additive scales for the effects of immigration and attitudes towards government immigration policy in this way is supported by existing research into immigration in Australia: Pedersen’s et al. (2005) analysis of attitudes towards asylum seekers uses an additive scale as the dependent variable that was derived from several questions relating to perceptions of asylum seekers (Pedersen et al., 2005); Rustenbach (2010) also used an additive scale, using three variables with a high alpha coefficient concerning immigrants (see Rustenbach, 2010, p. 62) to arrive at a reliable measure of attitudes. Further, grouping the
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measures together in this way results in readily understandable concepts that can be used as dependent variables in further analysis. In the Chapter 5, I will present each of the seven original measures from the AES concerning immigration in graphical form, to visually consider trends over time, in the context of additive scales that have been constructed for attitudes towards immigration policy, and the effects of
immigration on society.