Choice of questions

The choice of which questions to use is of critical importance for measuring subjective well-being. Different questions capture different dimensions of subjective well-being and, as discussed in Chapter 2, the precise question wording can have a non-trivial impact on results. In selecting questions to incorporate into existing survey vehicles, statistical agencies face trade-offs between the time taken to ask any new questions, the potential impact of new questions on responses to existing questions, and the added information gained from the new questions. These trade-offs will come under particularly severe scrutiny if the survey in question refers to an important and well-established concept (e.g. household income or unemployment).

In selecting subjective well-being questions themselves, there is also a trade-off to manage between using existing questions from the literature that will enable reasonable comparability with previous work, and modifying questions or response formats in light of what has been learned about good practice - including the evidence described in Chapter 2. The approach adopted in this chapter is to recommend tried-and-tested questions from the literature first and foremost. Where a variety of approaches have been used in the past, the rationale for selecting between these is explained. Finally, where there is a case for making small alterations to the question wording based on the evidence in Chapter 2, some modifications are proposed.

For statistical agencies already using subjective well-being measures in their surveys, a crucial question will be whether the potential benefit of using improved measures, and/or more internationally comparable measures, outweighs the potential cost of disrupting an established time series. This is a choice for individual statistical agencies, and will depend on a number of factors, including what the current and future intended use of the data set is, how drastic the change may be, and how long the time series has been established for. It is recommended that any changes to existing questions are phased in using parallel samples, so that the impact of the change can be fully documented and examined. This will enable insights into the systematic impact of changes in methodology and provide agencies with a potential method for adjusting previous data sets (e.g. Deaton, 2011).

In recognition of the different user needs and resources available to statistics producers, this chapter does not present a single approach to gathering information on subjective wellbeing. Instead, six question modules are attached to the guidelines as Annex B (A to F). Each question module focuses on a distinct aspect of subjective well-being. Question Module A contains the core measures for which international comparability is the highest priority. These are measures for which the evidence on their validity and relevance is greatest, the results are best understood, and the policy uses are the most developed. Of all the six question modules, Module A is unique in that it contains both life evaluation and affect measures, and because all national statistical agencies are encouraged to implement it in its entirety. When this is not possible, the primary measure outlined in the module should be used at the minimum. Modules B through to E are focused on specific aspects of subjective well-being. These modules are not intended to be used in their entirety or unaltered, but provide a resource for national statistical agencies that are developing their own questionnaires.

The six modules are listed below, and those which it is recommended that national statistical offices implement are highlighted as recommended in order to distinguish them from those modules intended as a resource for data producers of all types that are developing more detailed questionnaires.

Recommended:

A. Core measures.

Resource:

  • • B. Life evaluation.
  • • C. Affect.
  • • D. Eudaimonic well-being.
  • • E. Domain evaluation.

Recommended for time-use surveys:

• F. Experienced well-being.

 
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