The framework presented above emerges from a top-down, deductive, approach. Based on the latest theories of well-being, which in turn are based on empirical evidence, we posited a structure for what well-being looks like.
Another, very different, approach is the bottom-up, inductive one. Such an approach avoids reference to theory and attempts to allow the data to determine a structure. The most popular methodology for doing this is factor analysis.1 Having assumed a multi-dimensional structure of well-being, we might hope that such analyses would identify distinct factors within the ESS data. Questions on social life should intercorrelate more with each other than they do with questions on, for example, feelings of autonomy. Do exercise 3 now to see whether that’s the case.
-  Factor analysis groups together questions whose responses intercorrelate highly. For example, imagine you presented respondents with a quiz about Europe. Half the questions are about European geography (e.g. “what is the longest river in Europe?”, “which country has the southernmost point in mainland Europe?” etc.). The other half are more about European history (e.g. “who led the Reformation of the church in the 16th century?”, “in which century did Serbia become independent from Ottoman rule?”, etc.). The chances are that people who are good at geography will get more geography questions right generally, and people who are good at history will get more history questions right generally. In other words, someone who knows what is the highest mountain in Europe is more likely to know which is the most southerly country in Europe. This impact is likely to be weaker between the two sets of questions. As a result there are two factors that determine responses to the questions - geography knowledge and history knowledge.