Abstract:
This investigation explored the salient variables that predict general happiness, as well as those
variables’ relative importance, as indicated by the General Social Survey (GSS). The study
applied the Least Absolute Shrinkage and Selection Operator (LASSO) regression method to
explicitly select the most important variables and remove the others. Additionally, a Random
Forest (RF) was applied to the variables to create a large number of models with random subsets
of the variables included in each. All coefficients were reduced to zero within the LASSO
regression, and the relative importance of each variable were diminutive. Due to unexpected
results, follow-up correlations and a forward regression were conducted. All predictor variables
were significantly, positively, and, at best, moderately correlated with the outcome variable.
Within the forward regression, the second model, fit the data the best, accounting for
approximately 39.8% of the variance with only two variables. This study concludes that an
immediate sense of happiness and satisfaction in and with life best determines general
happiness within United States’ citizens.
