Happiness and the Propensity to Interact With Other People: Reply to Elmer (2021)

In 2019, we reported the results of a large experiencesampling study in which we sought to reconcile two literatures (Quoidbach et al., 2019). The first showed that happiness was associated with more social interaction, and the second showed that unhappiness was associated with more social interaction. We found that average happiness predicted more social interaction, whereas momentary unhappiness predicted subsequent pleasant (but not unpleasant) categories of social interaction. To distinguish the effect of momentary fluctuations in a person’s happiness from the longer-term trends (e.g., happy people are more socially engaged, happy days are often socially active ones), we argued it was essential to control for participants’ daily happiness levels. In his insightful Commentary, Elmer (2021) pointed out that our decision to control for daily happiness (Hday) was problematic for two reasons. First, for 34% of observations, Hday included information about future states of happiness that should have no bearing on people’s propensity to socialize at a previous point in time. Second, for 49% of observations, Hday was equal to current happiness (Ht+1), that is, happiness recorded at the same time as the social interaction we sought to predict. Given the overrepresentation of Ht+1 in Hday, and informed by an empirical model in which the change in happiness between t and t + 1 was used as a covariate, Elmer suggested our negative association between happiness and subsequent social interaction might be “driven by observations of individuals who were happy before (at t) and were not interacting at the subsequent time point (t + 1) because they had become less happy by the subsequent time point” (p. 958). To avoid these difficulties—while still attempting to disentangle shortversus long-term effects of happiness on social interaction—Elmer used our original data (https://osf.io/bxgn4) to compute several alternative models, including models that controlled for average happiness over the last 24 hr (Hpastday) and average happiness over the past week (Hpastweek). Using these alternative specifications, he found that people were more likely to report interacting with others when they were happy a few hours before. We thank Elmer for pointing out that the effects we reported depend on the specific covariate that is used. To better understand this intriguing dependency, we performed a series of follow-up analyses to pinpoint which component of the Hday covariate is critical to the results and to assess how viable different covariates are from a statistical standpoint. We then attempted to shed light on why controlling for Hday (or Ht+1) instead of Hpastday, Hpastweek, or an unadjusted model leads to radically different inferences. One explanation is Elmer’s reduced-happiness account: When the model includes the Hday (or Ht+1) covariate, happiness at t captures the change in happiness between t and t + 1. A second explanation—also inspired by Elmer’s observation but not foregrounded by him—is a temporal-proximity account. Because several hours typically separated reports at t and t + 1 in our data set, changes in happiness are likely to have occurred between successive assessments. With such noisy data, it is difficult to isolate the short-term signal (i.e., momentary shift) part 1012673 PSSXXX10.1177/09567976211012673Quoidbach et al.Happiness and Social Behavior research-article2021