The effect of the judge’s condition on the judgment of others’ well-being

ABSTRACT

We study the effect of perceivers’ health conditions on their judgments of the well-being of target people (their judgments of the targets’ day-to-day physical difficulties) based on information about the targets’ health conditions. We develop a model which suggests that this effect depends on the similarity between perceivers’ and targets’ health: The perceiver’s well-being is used as an anchor and the judgment of the target’s well-being is either assimilated toward or contrasted away from this anchor, depending on the similarity between the subject’s and target’s health. Based on this model we derive and test the correlation-trend hypothesis which states that the higher the similarity between perceivers’ and targets’ conditions, the more positive the correlation between perceivers’ conditions and their judgments of the targets well-being.

KEYWORDS:

• Social judgments
• health judgments
• well-being
• assimilation and contrast
• judgmental anchors

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data described in this article are openly available in the Open Science Framework at https://www.share-project.org/home0.html

Open scholarship

This article has earned the Center for Open Science badges for Open Data and Open Materials through Open Practices Disclosure. The data and materials are openly accessible at https://www.share-project.org/home0.html.

Ethical statement

The data are archival data collected in about 30 countries by the countries’ bureaus of labor statistics. Informed consent was obtained from participants

Notes

1. By low and high we mean targets whose conditions are bad and good, respectively.

2. In our data the normal targets tend to be in the lower range of the subjects’ population. However, our analysis above applies to these targets as well.

3. We chose all the SHARE’s domains for which objective measures of subjects’ conditions were available.

4. With this sample size our analysis had 80% power to detect an effect size of r = .04.

5. In this paper we report the results in terms of Pearson correlations. However, our results are robust to the type of correlation used – whether it is Pearson correlation or Spearman rank order correlation. In addition, our results are robust to basic controls such as sex age and country dummies.

6. All tests for differences between correlations are tests for differences between dependent correlations. The n’s for these tests are given in the tables.

7. The significance tests of the correlations are t-tests with n-2 degrees of freedom. The n’s are given in the tables.

8. The literature on the psychometric properties of these scales suggest that they enjoy suitable reliabilities and validities (e.g., Cheung & Lucas, 2014; Cunny & Perri, 1991; McDowell, 2010).).

Additional information

Funding

Funding was supplied by the Coller Institute of Venture and by the Henry Crown Institute.

Notes on contributors

Yoav Ganzach

Yoav Ganzach received his B.A. from Tel Aviv University, attended graduate studies at the Hebrew University of Jerusalem, and received his Ph.D. from Columbia University. He was a faculty member at the Hebrew University, Tel Aviv University and Ariel University and he is now the dean of the school of management and economics at the Academic College of Tel Aviv Yaffo. His interests are in the areas of social and organizational psychology and behavioral economics.