October 23, 2008

Republicans: Still Happy Campers

II. Explaining the Regression Analyses

A regression analysis is a statistical technique designed to show the relative importance of each of a number of independent variables in predicting a phenomenon of interest– in this case, the likelihood that a respondent is very happy.

For the purpose of this analysis, we constructed two regression models,. The first considerd party identification along with a series of demogrpahic traits– including age, gender, race, ethnicity, income, educational acheviement and marital status. A second model considered all those factors, as well as church attendance and health status, which have long been shown to be correlated with happiness. Predicted probabilities have been computed by varying a given independent variable from its minimum to its maximum value, while holding all other variables in the equation constant (at their mean or modal value). Both regression analyses were performed using a combined data base from two different Pew surveys– one conducted in July, 2008 among 2,250 adults and the other conducted in October, 2005 among 3,014 adults.

The Model One analysis found:

The Model Two analysis found:

We also ran the regression equation with using ideological self-identification (conservative versus liberal) rather than partisan self-identification (Republican versus Democrat) as one of the variables. We found that the impact of being conservative as a predictor of happiness is about the same as the effect of being a Republican. In addition, when we did another analysis that combined both party and ideology on a continuum from liberal Democrat to conservative Republican, we found that the effect of these combined variables on predicting happiness is slightly greater than the effect of either variable on its own.