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The sample drawn for this study reflects the city of Philadelphia's population, though levels of education and income appear to be slightly high. For an overview of the city's demographics, visit
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The sample drawn for this study reflects the city of Philadelphia's population, though levels of education and income appear to be slightly high. For an overview of the city's demographics, visit http:/ /quick facts.census.gov/qfd/states/42/42101.html.
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85036738578
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note
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In line with other survey research, 17% of respondents either did not know their annual household income or refused to answer the question. After a single-item imputation was used, an income response was available for 96% of respondents. An income for respondents that did not volunteer one was tabulated by substituting the mean income level of other respondents in the same zip code who did provide an income. Four percent of survey respondents provided neither an income nor a zip code and, consequently, are dropped from the multivariate analyses.
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54
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85036746395
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note
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National and local political interest are included in both regression models presented in this paper for two reasons. First, including both makes the two models identical aside from the dependent variables. This allows for clearer comparison. Second, including both interest measures is a way to check the construct validity of the dependent variable knowledge indices. Theoretically, local political interest should predict local political knowledge but not national political knowledge. Similarly, national political interest should predict national political knowledge but not local political knowledge. In the models presented in this paper, this is the case and some modicum of inferential confidence can be derived from this fact.
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61
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85036742271
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There is a strong positive correlation between Internet access and income (R = .40, p ≤ 01) and education (R = .37, p ≤ .01). Income and education, as depicted in Table 1, are n turn strongly correlated with local political knowledge (income: R = .31, p ≤ .01; education: R = .30, p ≤ .01
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There is a strong positive correlation between Internet access and income (R = .40, p ≤ 01) and education (R = .37, p ≤ .01). Income and education, as depicted in Table 1, are n turn strongly correlated with local political knowledge (income: R = .31, p ≤ .01; education: R = .30, p ≤ .01).
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