The Sad Truth about Happiness Scales

By Timothy N. Bond, Kevin Lang. Journal of Political Economy, 2019

295 citations recorded by the source corpus.

Happiness is reported in ordered intervals (e.g., very, pretty, not too happy). We review and apply standard statistical results to determine when such data permit identification of two groups’ relative average happiness. The necessary conditions for nonparametric identification are strong and unlikely to ever be satisfied. Standard parametric approaches cannot identify this ranking unless the variances are exactly equal. If not, ordered probit findings can be reversed by lognormal transformations. For nine prominent happiness research areas, conditions for nonparametric identification are rejected and standard parametric results are reversed using plausible transformations. Tests for a common reporting function consistently reject.

Subfields: Experimental and Cognitive Psychology, Social Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b39d83c776c6d34814bda72d06c62459f9cf16e6

How researchers see this paper

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In Experimental and Cognitive Psychology

1. Alex Bryson

In Social Psychology

1. L. Stokes
2. J. Forth
3. Alex Bryson

In Sociology and Political Science

1. Johannes Wohlfart
2. Christopher Roth
3. Ingar K. Haaland
4. Alex Bryson
5. B. Oc
6. Michael R. Bashshur

Citations in the corpus

• Designing Information Provision Experiments (2020, background): “…that they are not easily interpersonally comparable, which can result 3See Delavande et al. (2011) and Delavande (2014) for excellent overviews on the measurement of subjective beliefs with a particular focus on developing country settings. in severe identification challenges (Bond and Lang, 2019).”
• Does employees’ subjective well-being affect workplace performance? (2017, background): “1 Focusing on the tails in this way can help to avoid some of the assumptions that are needed about the underlying distribution of SWB when constructing mean SWB (Bond and Lang, 2014).”
• Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (2022, background): “rating-scale data may lead to a biased estimator [148].”