4 Modelling approaches
The preceding section presented some preliminary results from the survey. They were intended to capture the broad patterns; for example, the difference in wealth between the working and retired groups. The limitation of this type of analysis is that other factors might lie behind the observed differences and these are not being held constant. In this example it might be that the real reason for the differences lies in the health status of the two groups and is not directly related to their employment status. Regression models, a form of multi-variate analysis, can be used to test the effects of particular variables while holding constant the influence of other variables. In some cases there may be interaction effects between explanatory variables and these can be readily incorporated.
In this study we use two forms of regression: ordinary least squares (OLS) where the dependent variable is a continuous variable (eg, wealth or income) and logistical regressions where the dependent variable of interest is a binary variable (eg, working as distinct from retired; or self-rated good health as distinct from poor health). The next section provides a relatively non-technical discussion aimed at providing the reader with a basic understanding of the models employed.[14]
Notes
- [14]For more detailed and technical discussions there is a wide range of texts available; a recommended starting point would be Wooldridge (2006).
