Article summary with Non-traditional measures of subjective well-being and their validity: A review by Scollon

What is this article about?

This article is a review of a variety of methods for assessing subjective well-being beyond traditional global self-reports, which are the traditional measures of subjective well-being. The costs and benefits of non-traditional measures of SWB will be discussed. No measure is perfect, but with this knowledge researchers will be able to make better informed decisions in their research designs and projects.

How valid are brain activity measures of SWB?

Techniques such as functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have revolutionized the field of psychology. But can fMRI and EEG be used to measure SWB? There are studies that have tried to do this.

There is important information we can gather with fMRI and EEG, but it is often difficult to separate activation due to the emotion itself and activation due to other cognitive processes invoked during the emotion induction procedure. Neuroscience has yet to converge on any clear brain markers of happiness or any other emotion. Part of what makes it difficult to identify which brain structures are associated with happiness is that most structures are involved in multiple emotions and serve multiple functions. But as we learn more about the brain, brain activity measures will become more and more useful for measuring SWB.

How valid are smiling measures of SWB?

There have been promising studies of the connection between a smile and a person's happiness. Whether people smile genuinely in pictures, can reveal a lot about a person's SWB.

However, there are researchers that address the need for caution when making inferences about smiling. Not everyone smiles because they are happy. Some people smile because they want to become happy or portray a happy image. Smiling appears to be a valid indicator of SWB, but we need to study it with caution.

How valid are experience sampling method measures of SWB?

Experience sampling methodology (ESM) involves participants answering questions about their affect and activities in real-time several times a day over several days. The advantage is that there are less memory biases than for retrospective reports of well-being.

There is also a danger to ESM. Depressed people that were asked about how they felt a lot, actually became less happy over time (Conner & Reid, 2012). By contrast, happy people can become happier by ESM. ESM measures make people more aware of how they feel. This is important to keep in mind for researchers in order to go about their research in an ethical way.

There is also the problem of ESM that it is intensive and can be seen as invasive and irritating by participants, and that it depends on compliance from participants.

How valid are day reconstruction method measures of SWB?

The day reconstruction method (DRM) guides respondents through a detailed moment-by-moment reconstruction of the previous day's activities. Participants record the location, duration and affect experiences during each episode. DRM is designed to minimize memory biases.

DRM is less intensive than ESM, but still takes about one hour to complete, which makes it impractical for large scale studies. Another limitation of DRM is the common practice of weighting affect by duration of the episode. But one of the advantages of DRM over ESM is that DRM can provide nearly as much information as ESM without the intensity. But this is also changing because of smart phone apps that are making ESM less invasive.

How valid are memory measures of SWB?

A memory task can be a subtle, yet powerful way to assess SWB. Happy people tend to remember more positive things and unhappy people more negative things. If you make people recall all positive and negative things that have happened, you can make an assessment of how people are.

This type of measuring is practical and low in costs. But to avoid priming effects, researchers should place memory tasks before other items that may influence accessibility of positive or negative information. Evidence shows memory task measures are valid, but they are still underused in literature.

How valid are cognitive accessibility measures of SWB?

Studies have suggested that people with higher SWB organize positive emotions differently from less happy people. Activation of happiness-related concepts may automatically lead to activation of other parts of an associative network in happy individuals.

There have not yet been studies to the validity of cognitive accessibility measures, but this research seems promising.

How valid are informant reports measures of SWB?

Informant reports measures are measures where the individual does not judge their own happiness, but other people in their live judge it.

There is some prove that this could work, but there is also the problem that the degree to which SWB is visible may vary by person and culture. So, it might not work for anyone, as some people don't show how they feel or present themselves in certain ways that don't reflect how they feel.

How valid are implicit measures of SWB?

The implicit association test (IAT) was first developed to measure implicit or unconscious racial bias, but is now also being used to measure SWB. The IAT for measuring SWB is called the implicit life satisfaction (ILS) and uses reaction times to measure the strength of positive and negative associations towards one's own life.

Respondents can slow down their responses and with that suppress their implicit life satisfaction, but ILS is for the most part not susceptible to socially desirable responding. This makes it a valuable tool for measuring SWB.

How valid are big data measures of SWB?

One of the most exciting new areas to provide new insight in SWB is through big data from social networking sites such as Facebook and Twitter and search engines such as Google.

Big data have high volume, velocity, and variety, making them rich sources of information about human behavior. But these new forms of data are raising some potentially serious ethical concerns for scientists regarding privacy, consent, and data ownership. There is also the problem of possible inequities in accessibility to big data and proprietary algorithms for public data that may be causes of over- or underrepresentation.