Understanding Well-being Data

chapter 4 Discovering ‘the new science of happiness’ and subjective well-being

Experience measures

Experience measures aim to capture a person’s feelings at a given, specific time which can be thought of as ‘the amount of affect felt in any moment’1. Measures are constructed with the Benthamite view that certain aspects of life are good or bad, based on their qualities of ‘pleasurableness’ or painfulness2. How happy, sad or anxious any person is at a particular time is re-conceived as well-being by taking the average balance of pleasure (or enjoyment) over pain, measured over the relevant period. As already pointed out directly above, there is some evidence that positive and negative affect do not directly predict each other and should therefore be measured separately. Heeding Huppert and Whittington’s concerns (2003), positive psychology has more recently begun to conceive of well-being as a continuum3, rather than something which can be assessed by taking the average of positive and negative measures. The experience approach relied on in surveys will tend to specify a period of time for you to remember how you felt. In the ONS4, this is the only account with two questions, one for happy yesterday and one for anxious yesterday (see also Table 4.1). As well as specifying the exact moment you want someone to recall, other methods capture people’s emotions at multiple points in a day or week, and for that reason, they are not really included in national-level surveys, which would be difficult to administer. However, they are suitable for mobile apps, as we shall discover.

The Day Reconstruction Method (DRM) ((Kahneman et al. 2004)) is perhaps the most renowned of numerous measures which attempt to capture experienced well-being over time which is called the experience sampling method (ESM). The DRM is a diary-based technique, through which participants reflect on the main episodes that affected them on the previous day and recall the type and intensity of feelings. In other words, it literally takes a sample of feelings from specific days and weeks. Affect is an aspect of subjective well-being that is particularly sensitive to immediate surroundings and activities4. This is why it is considered suitable for understanding the relationship between what we do and how we feel, as well as situational aspects of life that affect us.

For example, short-term affect data can be collected through DRM approaches to include information about both activities and locations, as well as the affective states accompanying them5. Such an approach has the potential to capture data on how people spend their time and the ‘experienced utility’5 of such activities. For example, 132 teachers in the Netherlands completed a daily diary on three consecutive work days as well as a background questionnaire6. The researchers found that despite a lack of work-life balance, working hard was not necessarily detrimental to the teachers’ happiness scores. If you take these scores at face value, then if the teachers were ambitious, then striving towards their goals was satisfying, but this motivation was not necessarily constant.

The Ecological Momentary Assessment (EMA) ((Stone et al. 1999)) is based on self-reports of well-being at specific, but often randomly chosen points in time. Reports explicitly include self-assessments of behaviours and physiological measures, but also the recording of events. In Chap. 5, we discuss how an app alerts its users to record how happy they feel at random moments, allowing the user (and whoever is capturing their data) to track their mood over time and establish what is good for their mood. The researcher who developed ‘mappiness’ has used these data to measure a number of aspects of happiness: that we are most miserable commuting, on the one hand, and that ‘happiness is greater in natural environments’, for example7. These data have also been used8 to compare how happy people feel doing different kinds of activities from birdwatching, to making love; and more specifically, between artforms, such as watching the performing arts or reading alone.

An exploration of the determinants of, and changes to, affect and time-use may offer understandings of how people’s ‘experiences of utility’ vary. Returning to the example of the local, subsidised concert in Chap. 3, again, the questions we asked there can help us understand how people’s responses to the cost, amount of time and effort vary, and how that changed their declaration of how they felt. This may be at odds with the ‘utility’ assumed by ‘the provider’, whether that is the local council, a theatre company or another funder.

However, it is important to remember that people who attended our hypothetical park concert, self-selected to do so. This is one of the key issues with valuing how people experience social and cultural activities: it makes it difficult to say how a particular experience might affect others in the future9. Also, people are liable to ‘mind wanderings’, which can mean they are not thinking of what you think they are when you ask them how they are feeling (ibid.: 8). Furthermore, what makes sense, or represents the experience of one person may not manifest in the average of a sample.

These approaches ostensibly measure at different points during the day and they relate to experiences associated with specific activities and time points. However, because in a national-level survey, large population samples are questioned at certain points during the year, it is not feasible to repeatedly survey respondents during a particular day. As an alternative, the rationale with the ONS4 experience measures is to ‘replicate’ or ‘proxy’ ESM approaches by asking respondents for their experiences and feelings relating to a whole day (yesterday).

While there is potential for the measurement of change in affect and time-use longitudinally, questions remain as to whether existing national-level survey data can capture the sensation and emotion of ‘situated experience’ (how it felt, to be there, in that moment) in a meaningful way, and to do so over time. In cultural policy studies, there is often a call for longitudinal measurement of the relationship between cultural participation and aspects of well-being. It is thought that this will solve some of the proclaimed issues with the evidence base (around data and causation, discussed in the latter chapters of the book). However, while longitudinal analysis can help address issues of causal direction in the evidence, they will not address issues related to capturing the duration of the impact of an experience, and this also is not always clearly understood10.

  1. Dolan et al. 2011a, 7 []
  2. Crisp 2006 []
  3. ONS n.d., 3; Diener et al. 2009 []
  4. Smith and Exton 2013, 230 []
  5. Kahneman and Sugden 2005 [] []
  6. Tadić et al. 2013 []
  7. MacKerron and Mourato 2013; Krekel and MacKerron 2020 []
  8. Fujiwara and MacKerron 2015 []
  9. Dolan et al. 2011b, 12 []
  10. Oman 2017b []