relative to negative pictures in the right amygdala was associated with scores on an extraversion measures. According to the authors, this finding was the first demonstration that the amygdala—which is usually associated with the process-ing of negative information—is also involved in the processprocess-ing of positive stim-uli.
Other theories focus on general systems that may underlie broad, affective and motivational traits (e.g., Cloninger, 2004; Gray, 1970). For instance, within the context of affiliative behavior, Depue and his colleagues (Depue & Collins, 1999; Depue & Morrone-Strupinsky, 2005) have linked a variety of psy-chophysiological processes with emotions. Specifically, Depue and Morrone-Strupinsky suggested that affiliative behavior could be broken down into two distinct phases of activity, both of which are intimately involved with psycho-physiologically based emotional processes. In an appetitive phase of affiliative behavior, dopamine systems affect the feelings of excitement as an organism approaches a potential reward. In a consummatory phase, opiate systems affect feelings of pleasure, gratification, and liking that result from the attainment of a social reward. Although Depue and Morrone-Strupinsky link individual differ-ences in these systems to the personality trait of affiliation, the processes involved have clear relevance for well-being. Differential functioning in these systems may result in individual differences in the emotions that ultimately promote affiliative behavior.
It is tempting to conclude from this research that differences in these psychophysiological processes provide the link from genes to the observed indi-vidual differences in well-being. It is also tempting to infer that because these underlying differences are biologically based, they are difficult, if not impossible, to change. However, Davidson (2004) is careful to point out that such a conclu-sion would be premature. For instance, animal studies have shown that early environment can directly affect the biological systems that govern emotional response to stressors (e.g., Francis & Meaney, 1999). Furthermore, Davidson and colleagues have shown that some of the physiological indicators that have been linked with subjective well-being can change over time. For instance, an 8-week mindfulness meditation training program led to changes in asymmetry that mir-ror the individual differences described above (Davidson et al., 2003). Thus, it is important not to infer from these biological differences that subjective well-being cannot change.
stabil-ity estimates, Roberts and Del Vecchio (2000) showed that personalstabil-ity traits were stable even over long periods of time. On average, 6- to 7-year stability coefficients for personality traits ranged from .54 during the college years, to .64 at age 30, to .74 between the ages of 50 and 70. Other researchers have shown that personality characteristics are stable across situations (e.g., Epstein, 1979), though, of course, there has been debate about this issue throughout the history of the field. Nevertheless, if personality plays an important role in subjective well-being, we should also expect happiness and other related variables to be sta-ble over time and across situations.
Numerous studies show that there are consistent and enduring patterns in people’s cognitive and emotional evaluations of their lives. When people are asked to evaluate different aspects of their lives (e.g., their relationships, income, health, and environment), there are moderate to strong correlations between the various ratings, even across domains that would not be expected to correlate very strongly. Diener (1984) labeled this a “top-down” effect, and many researchers assume that it is due to the strong and pervasive impact of personality factors on people’s evaluation of their lives (Heller, Watson, & Ilies, 2004). Presumably, those individuals with a temperament-based tendency to be happy also tend see the world through rose-colored glasses.
Recently, I investigated the strength of this top-down effect using domain sat-isfaction ratings from a long-running panel study (Lucas, 2004). Participants were asked to rate their satisfaction on a variety of domains each year for many years. By using hierarchical latent state–trait models, I could separate stable trait variance in domain satisfaction ratings from transient state variance. I was then able to deter-mine how much of the stable variance was shared across domains. If domain satis-faction ratings are primarily determined by top-down effects, then once measure-ment error is removed, most of the stable variance should be shared across domains.
Results showed that, at most, 53% of the stable variance in any of the latent domain satisfaction traits was shared across domains. The remaining variance (up to 72% of the total variance) was unique, stable variance that was not shared across domains.
Furthermore, measures of objective variables (e.g., the number of doctors visits, a person’s actual income) were related both to the unique and shared components.
This finding means that even the shared top-down variance may be sensitive to external circumstances. Together, these results suggest that there are reliable and moderately strong top-down effects on domain satisfaction ratings, but in most cases, these effects do not overwhelm the specific domain variance. In addition, at least some of what appears to be a top-down effect may, in fact, be due to the actual covariation among conditions in people’s lives.
Research on top-down and bottom-up processes shows that people evaluate diverse domains in similar ways. But do they actually report similar levels of hap-piness when they are in diverse situations? To address this question, Diener and Larsen (1984) assessed momentary affect multiple times in a variety of situations.
Cross-situational consistency coefficients were generally quite high. Positive affect at work correlated .70 with positive affect reported during recreation expe-riences; negative affect at work correlated .74 with negative affect in recreation situations. Similar correlations were found when comparing affect in social tions to affect reported when alone, and when comparing affect in novel situa-tions to affect in typical situasitua-tions. Even higher correlasitua-tions were obtained for life satisfaction. Thus, there appear to be stable individual differences in the level of positive and negative affect that people experience, and these individual differ-ences are apparent even in relatively diverse situations.
It is also possible to show that people are able to recognize and report on these stable affective and cognitive reactions, even as their emotions fluctuate on a day-to-day basis. Eid and Diener (2004) used multistate–multitrait–
multiconstruct models to estimate how much stable trait variance there was in multiple reports of well-being over time. They asked participants to report on their mood and their global well-being three times over a 2-month period.
Using structural equation modeling techniques, Eid and Diener were able to show that most of the variance in subjective well-being measures is stable trait variance. For instance, between 74 and 84% of the variance in the Satisfaction with Life Scale was stable over time. Only a small percentage was occasion-specific state variance, and this state variance tended to be only weakly related to state variance in moods. Importantly, although mood levels did fluctuate consid-erably over time (i.e., there was less trait variance and more occasion variance in each assessment when compared to global measures), the trait component was strongly correlated with global well-being. Thus, people can recognize and report on stable levels of well-being, and these measures are not strongly influ-enced by transient affective states.
Of course, showing that happiness is stable over the course of a few weeks or months is only the first step in showing that stable individual differences in well-being exist. It is possible that constructs could be very stable over the short run but still change dramatically over many years or after the experience of major life events. A number of studies have examined the long-term stability of subjec-tive well-being measures, and most show that well-being measures exhibit a moderate degree of stability. Magnus and Diener (1991) reported that the 4-year stability of life satisfaction was .58 and that this correlation only dropped to .52 when self-reports were used to predict informant reports 4 years later. Lucas, Diener, and Suh (1996) reported stability coefficients that ranged from .56 to .61 for positive affect, negative affect, and life satisfaction over a 3-year period. Costa and McCrae (1988) found similarly high correlations between self and spouse rat-ings over a 6-year period. Watson and Walker (1996) examined the 3-year stabil-ity of affect ratings and found correlations that ranged from .36 to .46.
Recently, psychologists have been able to take advantage of existing data sets to examine the stability of well-being measures over even longer periods of
time. Fujita and Diener (2005) used data from the German Socio-Economic Panel (GSOEP) study to assess the stability of a single-item life satisfaction mea-sure over a 17-year period. As might be expected from the results reviewed above, year-to-year stabilities were moderately high, in the range of .50–.60. In addition, these stabilities dropped off over time. However, even over the full 17 years of the study, stability coefficients for this single-item measure were approxi-mately .30.
Lucas and Donnellan (2006) used latent state–trait models to determine the extent to which stable trait variance contributed to measures of life satisfaction.
Using 9 years of data from the British Household Panel Study, a long-running, nationally representative panel study of households in Britain, we were able to isolate variance due to (1) a stable trait, (2) an autoregressive trait, and (3) occasion-specific variance and measurement error. Consistent with Fujita and Diener’s (2005) analysis, about 37% of the variance in life satisfaction in any sin-gle year was stable trait variance. This finding suggests that long-term stabilities should bottom-out around .30–.40. We were also able to show that an additional 30% of the variance was accounted for by an autoregressive trait, which accounts for the relatively high stability over shorter periods of time. It is also likely that at least some of the remaining variance within a single year is reliable variance that is unique to the specific year. This suggestion is supported by separate analyses on a multi-item measure of psychological distress. The use of multiple items allowed us to separate measurement error from reliable situation-specific variance, and similar estimates for stable trait, autoregressive trait, and occasion-specific state variance were obtained.
These studies demonstrate that there is moderate stability in subjective well-being over long periods of time. The question then arises as to whether subjec-tive well-being itself should be considered a trait, or whether it is less stable than other trait measures. Fujita and Diener (2005) compared the stability of life satis-faction in the GSOEP to the stability of personality as estimated by Roberts and Del Vecchio (2000). Although 1-year stabilities were similar in size (at least among young samples), the stability of life satisfaction dropped much more quickly (suggesting more of an autoregressive structure). Vaidya, Gray, Haig, and Watson (2002) compared the 2.5-year stability of personality and affect in the same sample. They showed that personality traits were significantly more stable than affect. For instance, stability coefficients for the Big Five personality traits ranged from .59 to .72, whereas the stability of negative affect was .49, and the stability of positive affect was .51. Depending on the autoregressive nature of these effects, these relatively small short-term differences could translate into major differences over long periods of time.
Although the stability of subjective well-being suggests that internal factors may play a role, it is possible that stability results from unchanging external cir-cumstances. To address this question, it is necessary to examine stability among
individuals who undergo major life events, and to determine whether these life events have an impact on well-being. Early research suggested that life events do not affect stability and that people inevitably adapt back to their temperament-based well-being setpoints. For instance, Costa, McCrae and Zonderman (1987) examined the stability of well-being among individuals who had major changes in life circumstances (e.g., divorce, widowhood, or job loss) and individuals who had very few changes in life circumstances. Stability estimates were only slightly lower in the high-change group. Similarly, researchers who have investigated the impact of life events have often emphasized people’s somewhat remarkable abil-ity to adapt (e.g., Brickman, Coates, & Janoff-Bulman, 1978). These results sup-ported the prevailing view that people inevitably return to temperament-based setpoints following major life events (see Diener et al., 2006, for a review).
However, more recent evidence challenges the idea that life events have lit-tle to no effect on well-being. As Watson (2004) has pointed out, small differ-ences in stability can be important over the long term, and recent evidence con-firms that life events do seem to affect the stability of well-being measures. In fact, these effects appear to be stronger than the effects on other personality traits.
For instance, Vaidya et al. (2002) showed that life events during a 2-year period had a bigger effect on mean levels and stability coefficients for affect variables as compared to personality variables.
More importantly, recent longitudinal studies examining the effect of major life events show that such events can have strong and lasting impact on people’s happiness. For instance, Lucas, Clark, Georgellis, and Diener (2003) showed that although adaptation to marriage is relatively quick (occurring within a couple of years), adaptation to widowhood is much slower (taking about 8 years). Perhaps more importantly, adaptation effects varied considerably across individuals. Some people reported large drops in satisfaction and very little recovery over time.
Others reported only minor changes and/or a complete return to baseline. Thus, for some individuals, widowhood (and even marriage) was associated with lasting changes in happiness.
Furthermore, research on other life events shows that even when average trajectories are examined, adaptation is often not complete. Lucas, Clark, Georgellis, and Diener (2004) showed that unemployment has lasting effects on happiness, and Lucas (2005) showed that divorce also appears to have permanent effects. Most recently, Lucas (2007) used two nationally representative panel studies to show that the onset of a long-term disability has relatively large and lasting effects on measures of life satisfaction. For instance, people who experi-enced severe disabilities dropped over a full standard deviation from their own baseline levels, and these levels did not show any trends back to baseline, even though participants were followed for an average of 8 years after onset. These results are consistent with most cross-sectional studies comparing individuals with disabilities to population norms (e.g., Dijkers, 1997). In fact, Lucas (2007)
and Diener et al. (2006) showed that much of the research on the effects of dis-ability have been misinterpreted and that these conditions often have lasting effects on subjective well-being.
Together, these results have a number of important implications for research on subjective well-being. First, there is some degree of stability even over long periods of time. It appears as though this stability is not due entirely to stable life circumstances. Even those individuals who undergo major life changes report moderate stability over time. Thus, this research provides evidence for the influ-ence of personality on well-being measures. However, stability estimates are not so high as to suggest that happiness cannot change. Even among samples who are not selected based on their experience of major life events, stabilities drop over time, bottoming out at around .30–.40. These results suggest that approximately one-third of the variance in well-being measures is stable variance that changes only slightly over time. Furthermore, these stability estimates tend to be lower (often considerably lower in the long term) than the stability of other personality traits such as extraversion, neuroticism, or conscientiousness. Finally, studies of life events show that such events affect both the mean levels and the stability of life satisfaction. Although no positive events have been found that reliably increase well-being, many negative events, including widowhood, divorce, unemployment, and the onset of disability, appear to have lasting or even perma-nent effects on people’s happiness. These findings are consistent with the idea that personality contributes to do, but does not completely set, long-term levels of well-being.