How to build a Monster Model of Well-being: Part 3

This is the third part in a mini-series of building a monster-model of well-being. The first part (Part1) introduced the measurement of well-being and the relationship between affect and well-being. The second part added measures of satisfaction with life-domains (Part 2). Part 2 ended with the finding that most of the variance in global life-satisfaction judgments is based on evaluations of important life domains. Satisfaction in important life domains also influences the amount of happiness and sadness individuals experience, but affect had relatively small unique effects on global life-satisfaction judgments. In fact, happiness made a trivial, non-significant unique contribution.

The effects of the various life domains on happiness, sadness, and the weighted average of domain satisfactions is shown in the table below. Regarding happy affective experiences, the results showed that friendships and recreations are important for high levels of positive affect (experiencing happiness), but health or money are relatively unimportant.

In part 3, I am examining how we can add the personality trait extraversion to the model. Evidence that extraverts have higher well-being was first reviewed by Wilson (1967). An influential article by Costa and McCrae (1980) showed that this relationship is stable over a period of 10 years, suggesting that stable dispositions contribute to this relationship. Since then, meta-analyses have repeatedly reaffirmed that extraversion is related to well-being (DeNeve & Cooper, 1998; Heller et al., 2004; Horwood, Smillie, Marrero, Wood, 2020).

Here, I am examining the question how extraversion influences well-being. One criticism of structural equation modeling of correlational, cross-sectional data is that causal arrows are arbitrary and that the results do not provide evidence of causality. This is nonsense. Whether a causal model is plausible or not depends on what we know about the constructs and measures that are being used in a study. Not every study can test all assumptions, but we can build models that make plausible assumptions given well-established findings in the literature. Fortunately, personality psychology has established some robust findings about extraversion and well-being.

First, personality traits and well-being measures show evidence of heritability in twin studies. If well-being showed no evidence of heritability, we could not postulate that a heritable trait like extraversion influences well-being because genetic variance in a cause would produce genetic variance in an outcome.

Second, both personality and well-being have a highly stable variance component. However, the stable variance in extraversion is larger than the stable variance in well-being (Anusic & Schimmack, 2016). This implies that extraversion causes well-being rather than the other way-around because causality goes from the more stable variable to the less stable variable (Conley, 1984). The reasoning is that a variable that changes quickly and influences another variable would produce changes, which contradicts the finding that the outcome is stable. For example, if height were correlated with mood, we would know that height causes variation in mood rather than the other way around because mood changes daily, but height does not. We also have direct evidence that life events that influence well-being such as unemployment can change well-being without changing extraversion (Schimmack, Wagner, & Schupp, 2008). This implies that well-being does not cause extraversion because the changes in well-being due to unemployment would then produce changes in extraversion, which is contradicted by evidence. In short, even though the cross-sectional data used here cannot test the assumption that extraversion causes well-being, the broader literature makes it very likely that causality runs from extraversion to well-being rather than the other way around.

Despite 50-years of research, it is still unknown how extraversion influences well-being. “It is widely appreciated that extraversion is associated with greater subjective well-being. What is not yet clear is what processes relate the two” ((Harris, English, Harms, Gross, & Jackson, 2017, p. 170). Costa and McCrae (1980) proposed that extraversion is a disposition to experience more pleasant affective experiences independent of actual stimuli or life circumstances. That is, extraverts are disposed to be happier than introverts. A key problem with this affect-level model is that it is difficult to test. One way of doing so is to falsify alternative models. One alternative model is the affective reactivity model. Accordingly, extraverts are only happier in situations with rewarding stimuli. This model implies personality x situation interactions that can be tested. So far, however, the affective reactivity model has received very little support in several attempts (Lucas & Baird, 2004). Another model assumes that extraversion is related to situation selection. Extraverts may spend more time in situations that elicit pleasure. Accordingly, both introverts and extraverts enjoy socializing, but extraverts actually spend more time socializing than introverts. This model implies person-situation correlations that can be tested.

Nearly 20 yeas ago, I proposed a mediation model that assumes extraversion has a direct influence on affective experiences and the amount of affective experiences is used to evaluate life-satisfaction (Schimmack, Diener, & Oishi, 2002). Although cited relatively frequently, none of these citations are replication studies. The findings above cast doubt on this model because there is no direct influence of positive affect (happiness) on life-satisfaction judgments.

The following analyses examine how extraversion is related to well-being in the Mississauga Family Study dataset.

1. A multi-method study of extraversion and well-being

I start with a very simple model that predicts well-being from extraversion, CFI = .989, RMSEA = .027. The correlated residuals show some rater-specific correlations between ratings of extraversion and life-satisfaction. Most important, the correlation between the extraversion and well-being factors is only r = .11, 95%CI = .03 to .19.

The effect size is noteworthy because extraversion is often considered to be a very powerful predictor of well-being. For example, Kesebir and Diener (2008) write “Other than extraversion and neuroticism, personality traits such as extraversion … have been found to be strong predictors of happiness” (p. 123)

There are several explanations for the week relationship in this model. First, many studies did not control for shared method variance. Even McCrae and Costa (1991) found a weak relationship when they used informant ratings of extraversion to predict self-ratings of well-being, but they ignored the effect size estimate.

Another possible explanation is that Mississauga is a highly diverse community and that the influence of extraversion on well-being can be weaker in non-Western samples (r ~ .2, Kim et al. , 2017.

I next added the two affect factors (happiness and sadness) to the model to test the mediation model. This model had good fit, CFI = .986, RMSEA = .026. The moderate to strong relationships from extraversion to happy feelings and happy feelings to life-satisfaction were highly significant, z > 5. Thus, without taking domain satisfaction into account, the results appear to replicate Schimmack et al.’s (2002) findings.

However, including domain satisfaction changes the results, CFI = .988, RMSEA = .015.

Although extraversion is a direct predictor of happy feelings, b = .25, z = 6.5, the non-significant path from happy feelings to life-satisfaction implies that extraversion does not influence life-satisfaction via this path, indirect effect b = .00, z = 0.2. Thus, the total effect of b = .14, z = 3.7, is fully mediated by the domain satisfactions.

A broad affective disposition model would predict that extraversion enhances positive affect across all domains, including work. However, the path coefficients show that extraversion is a stronger predictor of satisfaction with some domains than others. The strongest coefficients are obtained for satisfaction with friendships and recreation. In contrast, extraversion has only very small relationships with financial satisfaction, health satisfaction, or housing satisfaction that are not statistically significant. Inspection of the indirect effects shows that friendship (b = .026), leisure (.022), romance (.026), and work (.024) account for most of the total effect. However, power is too low to test significance of individual path coefficients.

Conclusion

The results replicate previous work. First, extraversion is a statistically significant predictor of life-satisfaction, even when method variance is controlled, but the effect size is small. Second, extraversion is a stronger predictor of happy feelings than life-satisfaction and unrelated to sad feelings. However, the inclusion of domain satisfaction judgments shows that happy feelings do not mediate the influence of extraversion on life-satisfaction. Rather, extraversion predicts higher satisfaction with some life domains. It may seem surprising that this is a new finding in 2021, 40-years after Costa and McCrae (1980) emphasized the importance of extraversion for well-being. The reason is that few psychological studies of well-being include measures of domain satisfaction and few sociological studies of well-being include personality measures (Schimmack, Schupp, & Wagner, 2008). The present results show that it would be fruitful to examine how extraversion is related to satisfaction with friendships, romantic relationships, and recreation. This is an important avenue for future research. However, for the monster model of well-being the next step will be to include neuroticism in the model. Stay tuned.

4 thoughts on “How to build a Monster Model of Well-being: Part 3

  1. “This implies that extraversion causes well-being rather than the other way-around because causality goes from the more stable variable to the less stable variable. The reasoning is that a variable that changes quickly and influences another variable would produce changes, which contradicts the finding that the outcome is stable.”

    This method of reasoning doesn’t always work:
    1. If the more stable variable is affected by multiple other variables, some of them can be less stable, as long as there are others that are more stable to introduce the required stability.
    1a. Furthermore, if the unstable variance in the source variables is negatively correlated, it may cancel out, and so *all* the source variables can be less stable. This is probably particularly likely to happen with people, as people have a quality of agency, and therefore choose tradeoffs according to some systematic criterion.
    2. If the more stable variable is a rolling average of the less stable variable, then obviously the less stable variable affects it, even though it is more stable due to the rolling average effect.

    Not that I necessarily disagree with you about it in this specific scenario. But playing around with different models I’ve found that there is an endless number of ways in which variables can be causally related, and it’s very difficult to find any easy valid criterion like this for inferring the causality.

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    1. Thank you for commenting.
      Ideally we would measure stable traits with repeated measures and extract the stable variance from them. When we do this, we see that extraversion has more stable variance than life-satisfaction or affect measures (Anusic & Schimmack, 2016). So, at least in this case, I think it is a reasonable argument to justify causality. Of course, demonstrating causality with non-experimental data is hard and I am not saying the evidence is conclusive.

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      1. One thing to beware of is that the stable variance may not be the full true variance.

        Consider for instance a simplified model where people have some resources, which they vary in. (E.g. a basic example would be income, though you could expand it by considering other types of resources, like friends or whatever.) Then imagine that people repeatedly encounter different types of events, where they can “spend” some resources to participate in these events; but there’s a limit to how much they can spend, depending on how many resources they have available. Then finally imagine that the recent events aggregate into life satisfaction, which is a rolling average over these events.

        This situation combines both 1a and 2, and it seems somewhat realistic, though not immediately applicable to the extraversion situation. But a similar situation could plausibly come up in other contexts that you are working with.

        So in this case, we have that the events you’ve partaken in affect well-being. But plausibly those events are less stable than the well-being itself, due to 1a and 2. And in this case, if you extract the stable variance in well-being, then yes this stable variance is going to affect the participation in events, but not because well-being affects events. Rather, it is because the stable variance in well-being (in this toy model) ends up being a measure of how many resources one has, and how many resources one has ends up affecting the events one participates in.

        One issue with personality, I think, is that we don’t really know how personality works? I mean you mentioned some other findings which indicates that extraversion in this specific case doesn’t work like that, so this toy model is not applicable here. But I just thought it might be worth pointing out the general issue; that if one extracts the stable variance, it might change a lot in meaning.

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      2. I guess my example here also contains a third effect:

        3. If there is an underlying stable factor behind the causes, but they also contain some unstable variance themselves.

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