The quality of subjective well-being measures

Quality is crucial to any statistical measure. Unless data captures the concept being measured with a sufficient degree of accuracy to draw reasonable inferences from it, there is little point in collecting it. This is particularly true for official statistics, which are expected to be of the highest quality. As the United Nations Fundamental Principles of Official Statistics states, “official statistics provide an indispensible element in the information system of a society, serving the government, the economy and the public with data about the economic, demographic, social and environmental situation” (OECD, 2008). It is therefore essential that decisions about the measurement of subjective well-being through official statistics are solidly grounded in a clear understanding of the reliability and validity of such measures.

The Quality Framework and Guidelines for OECD Statistical Activities (OECD, 2008) sets out the OECD’s approach to dealing with issues of statistical quality. Under the Framework, quality is defined as “fitness for use” in terms of user-needs. The ultimate benchmark as to the quality of statistics is essentially whether they meet the needs of the user in terms of providing useful information. Because users must often make decisions about a course of action whether or not statistical information is available, a focus on “fitness for purpose” may involve accepting the use of data that is less than perfectly accurate provided that the data is of sufficient quality that it improves rather than detracts from the quality of decision-making.

Evaluating a concept as broad as “fitness for purpose” is challenging. For this reason, the Framework identifies seven dimensions of statistical quality. These seven dimensions define the characteristics of high-quality data and provide a structured way of assessing the quality of a particular set of statistics. The seven dimensions of quality are:

  • Relevance, i.e. the degree to which data serves to address the purposes for which they are sought by users.
  • Accuracy, i.e. the degree to which data correctly estimate or describe the quantities or characteristics they are designed to measure.
  • Credibility, i.e. the confidence that users place in statistics based on their image of the data producer.
  • Timeliness, i.e. the length of time between the availability of data and the phenomenon or event that the data describe.
  • Accessibility, i.e. how readily data can be located and retrieved by users.
  • Interpretability, i.e. the ease with which the user can understand and properly use and analyse the data.
  • Coherence, i.e. the degree to which the data is mutually consistent with other similar measures and logically integrated into a system of statistics.

These seven criteria, along with the more general principle of cost effectiveness in producing/collecting such data, provide the OECD’s overall framework for assessing statistical quality. However, most of these criteria relate to how statistics are measured and collected rather than what is collected. For the purposes of these guidelines, the concern is more narrowly focused on what should be collected rather than the more general principles of how an official statistical agency should operate. Thus, the main focus for assessing the quality of measures of subjective well-being will be the principles of relevance, accuracy and, to a lesser degree, coherence.

 
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