Bivariate Regression Vote 08 Vote 04.

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Presentation transcript:

Bivariate Regression Vote 08 Vote 04

Bivariate Regression Liberalism Vote 08

Trivariate Regression Liberalism Vote 08 Vote 04

Crosstab: Liberalism and Presidential Vote 1.00 Extremely conservative 2.00 Conservative 3.00 Slightly conservative 4.00 Moderate 5.00 Slightly liberal 6.00 Liberal 7.00 Extremely liberal 1. John McCain 95.4% 74.1% 63.0% 41.2% 17.2% 15.0% 1.2% 2. Barack Obama 4.6% 25.9% 37.0% 58.8% 82.8% 85.0% 98.8% Total 100.0%

Correlation: Liberalism and Presidential Vote

Bivariate Regression: Liberalism and Presidential Vote

Crosstab: Presidential Vote (2004) and Presidential Vote (2008) Bush Kerry 1. John McCain 80.9% 7.5% 2. Barack Obama 19.1% 92.5% Total 100.0%

Correlation: Presidential Vote (2004) and Presidential Vote (2008)

Bivariate Regression: Vote 04 and Presidential Vote

Crosstab: Presidential Vote (2004) and Presidential Vote (2008), controlling for liberalism Bush Kerry Conservative 1. John McCain 89.4% 16.2% 2. Barack Obama 10.6% 83.8% Total 100.0% Moderate 59.3% 15.6% 40.7% 84.4% Liberal 51.7% 3.6% 48.3% 96.4%

Correlation: Presidential Vote (2004) and Presidential Vote (2008), controlling for liberalism

Crosstab: Presidential Vote (2008) and Liberalism, controlling for Presidential Vote (2004) 1.00 Conservative 2.00 Moderate 3.00 Liberal 1. John McCain 89.4% 59.3% 51.7% 2. Barack Obama 10.6% 40.7% 48.3% Total 100.0%   16.2% 15.6% 3.6% 83.8% 84.4% 96.4% 100.0% 

Correlation: Presidential Vote (2008) and Liberalism, controlling for Presidential Vote (2004)

Correlations

Explaining Venn Diagrams Variance Vote 08

Explaining Venn Diagrams Variance Vote 04

Explaining Venn Diagrams Variance Liberalism

Plot showing positive covariance Mean urban % Mean female literacy

Plot showing negative covariance Mean calorie intake Mean infant mortality

Plot showing no covariance Mean GDP Mean crop production

Pearson r = .62 Pearson r = .73 Pearson r = .61 Vote 04 Liberalism

Vote 08 Vote 04 Liberalism

Multivariate Regression: Presidential Vote (2008), controlling for Liberalism and Presidential Vote (2004)

Appendix: Syntax The following set of commands changes a nominal variable into an ordinal variable. Prespref_2004 had six categories for their presidential preference, including Nader and others. I needed a variable that just included Bush and Kerry. if prespref_2004 = 1 presvote_2004 = 1. if prespref_2004 = 2 presvote_2004 = 2. execute. I wanted a variable that went in the same direction as presvote_2004 and presvote so I recoded ideology to make liberalism coded high. recode ideology (7=1) (6=2) (5=3) (4=4) (3=5) (2=6) (1=7) into liberalism. Here I am adding value labels for liberalism. add value labels liberalism 1 'Extremely conservative' 2 'Conservative' 3 'Slightly conservative' 4 'Moderate' 5 'Slightly liberal' 6 'Liberal' 7 'Extremely liberal' . Here I am creating a three category variable for libealism so the crosstab is easier to understand. recode liberalism (1=1) (2=1) (3=1) (4=2) (5=3) (6=3) (7=3) into liberalismc. add value labels liberalismc 1 'Conservative' 2 'Moderate' 3 'Liberal' . Here I am running a crosstab with presidential vote as the dependent variable and liberalism as the independent variable. crosstabs tables = presvote by liberalism/cells = col /stats = corr. Here I am running a crosstab with presidential vote as the dependent variable and 2004 vote as the independent variable. crosstabs tables = presvote by presvote_2004/cells = col count/stats = corr. Here I am running a correlation between the three variables. corr presvote presvote_2004 liberalism. Here I am running a crosstab with presidential vote as the dependent variable and 2004 vote as the independent variable, controlling for liberalism. crosstabs tables = presvote by presvote_2004 by liberalismc/cells = col /stats = corr. Here I am running a crosstab with presidential vote as the dependent variable and liberalism as the independent variable, controlling for 2004 vote. crosstabs tables = presvote by liberalismc by presvote_2004/cells = col /stats = corr. Here I am running a regression with presidential vote as the dependent variable and liberalism and vote 04 as the independent variables. regr vars = presvote presvote_2004 liberalism / depe = presvote/enter.