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 probability of a Republican being very happy is 13 percent greater than the probability of a Democrat being very happy, once all other variables in this model are held equal.
• The probability of those at the highest annual income levels ($150,000 and above) being very happy is 16 percent greater than the probability of those at the lowest income levels (less than $10,000) being very happy, if all else is held equal.
• The probability of married respondents being very happy is 12 percent greater than the probability of unmarried respondents being very happy, if all else is held equal.
• The probability of someone age s 64 and above being very happy is 10 percent greater than the probability of all othert adults being very happy, if all else is held equal.
• The probability of someone ages 29 and below being very happy is 7 percent greater than the probability of all othert adults being very happy, if all else is held equal.
• The probability of someone who has completed college being very happy is 8 percent greater than the probability of someone who has not completed high school being very happy, if all else is held equal.
• Gender, race and ethnicity have no independent impact on the odds that someone is very happy, once all the other variables in this equation have been controlled.

The Model Two analysis found:

• The probability of those who report they are in excellent health being very happy is 36 percent greater than the probability of those in poor health being very happy, if all else is held equal. (There was a small difference in the 2008 and 2005 surveys in the number of responses categories to the health question. To make the measure more comaprable across the years, the 2008 sample combined those who said “poor” and “only fair” into one category for this analyis. In the 2005 sample, the lowest health rating was poor.
• The probability of those of who attend religious services more than once a week being very happy is 18 percent greater than the probability of those who never attend religious services being very happy, if all else is held equal.
• In this model, the estimated impact of party identification, marriage, income and age under 30 all decline slightly from their values in Model One, but still stand as predictors of happiness. The value of age over 64 increases slightly from it value in Model One. No other demographic traits have an independent impact on predicted happiness in this model.

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.