Category Archives: Wellbeing

Open-SOEP: Personality and Wellbeing Revisited

[corrected 8/6/2019 5.29pm – there was a mistake in the model for worry]

After behaviorism banned emotions as scientific constructs and cognitivism viewed humans as computers, the 1980s witnessed the affective revolution. Finally, psychologists were again allowed to study feelings.

The 1980s also were a time where personality psychologists agreed on the Big Five as a unified model of personality traits. Accordingly, personality can be efficiently summarized by individuals’ standing on five dimensions: Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness.

Not surprisingly, the 1980s also produced a model of personalty, emotions (affect), and well-being that has survived until today. The model was first proposed by Costa and McCrae in 1980 (see Schimmack, 2019, for details). This model assumed that extraversion is a disposition to experience more positive affect, neuroticism is a disposition to experience more negative affect, and the balance of positive and negative affect is a major determinant of life-satisfaction. As extraversion and neuroticism are independent dimensions, the model also assumed that positive affect and negative affect are independent, which led to the creation of the widely used Positive Affect and Negative Affect Schedule (Watson et al., 1988) as a measure of well-being.

The model also assumed that general affective dispositions account for most of the stability in well-being over time, while environmental factors produce only momentary and short-lived fluctuations around dispositional levels of well-being (Diener, 1984; Lykken & Tellgen, 1996). This model dominated well-being research in psychology for 20 years (see Diener, Suh, Lucas, & Smith, 1999, for a review).

However, when Positive Psychology emerged at the beginning of the new millenium, psychologists focus shifted from the influence of stable dispositions to factors that could be changed with interventions to boost individuals’ wellbeing (Seligman & Csikszentmihalyi, 2000) and some articles even questioned the influence of dispositions on well-being (Diener, Lucas, & Scollon, 1996). As a result, the past 20 years have seen very little new research on dispositional influences on well-being. The last major article is a meta-analysis that showed positive correlations of extraversion and neuroticism with several well-being indicators (Steel, Schmidt, & Shultz, 2008).

Revisiting the Evidence

There is robust evidence for the influence of neuroticism on wellbeing. Most important, this relationship has been demonstrated in multi-method studies that control for shared method variance when self-ratings of personality are correlated with self-ratings of well-being (McCrae & Costa, 1991; Schimmack, Oishi, Funder, & Furr, 2004). However, the relationship between extraversion and well-being is not as strong or consistent as one would expect based on Costa and McCrae’s (1980) model. For example, McCrae and Costa failed to find evidence for this relationship in a multi-method study, and other studies that controlled for response styles also failed to find the predicted effect (Schimmack, Schupp, & Wagner, 2008).

Taking a closer look at Costa and McCrae’s (1980) article, we see that they did not include life-satisfaction measures in their study. The key empirical finding supporting their model is that extraversion facets like sociabilty measured at time 1 predict positive affect and hedonic balance (positive affect minus negative affect) concurrently and longitudinally and that these correlations remain fairly stable over time. This suggests that personality is stable and contributes to the stable variance in the affect measures. However, the effect size is small (r = .22 to .24). This suggests that extraversion accounts for about 5% of the variance in affect. This finding hardly supports the claim that extraversion accounts for half of the stable variance in well-being.

It is symptomatic of psychology that subsequent articles run with the story while ignoring gaps in the actual empirical evidence. As longitudinal studies in psychology are rare, there have been few attempts to replicate Costa and McCrae’s findings.

Headey and Wearing (1989) replicated and extended Costa and McCrae’s study by including life-satisfaction measures as an indicator of wellbeing. They replicated the key findings and showed that personality also predicts future life-satisfaction. However, the effect size for extraversion was again fairly small; as was the effect of neuroticism, suggesting that most of the stable variance in life-satisfaction is not explained by extraversion and neuroticism.

A key limitation of both studies is that they do not take shared method variance into account. Although method variance may be transient, it is also possible that it is stable over time (Anusic et al., 2009). Thus, even the already modest effect sizes may still be inflated by shared method variance.

New Evidence

Data and Model

Fortunately, better data are now available to revisit the longitudinal relationships between personality and life-satisfaction. I used the data from the German Socio-Economic Panel (SOEP). The SOEP measured the Big Five personality traits on four occasions (waves) spanning a period of 12 years (2005, 2009, 2013, 2017). Personality was measured with the 15-item BFI-S. I created a measurement model for the BFI-S that shows measurement invariance across the four occasions (Schimmack, 2019a). I also related personality to the single-item life-satisfaction rating in the SOEP (Schimmack, 2019b). Here, I extend this analysis by taking advantage of the fourth measurement of personality in 2017, which makes it possible to separate trait and state variance in personality and well-being.

The SOEP measures life-satisfaction in two ways. First, it includes several domain-satisfaction items (health, finances, recreation, housing). Second, it includes a global life-satisfaction item. In a different post (Schimmack, 2019c), I examined the relationship between these items and found that global items are influenced by a general disposition factor and satisfaction with finances and health, while the other two domains are relatively unimportant. Based on this finding and related evidence (Zou, Schimmack, & Gere, 2013), I averaged the domain satisfaction judgments and used it as an indicator of life-satisfaction. This makes it possible to remove random measurement error from the measurement of life-satisfaction on a single occasion. I then fitted latent-trait-state (LST) models to the personality factors and the well-being factor. These models separate the longitudinal correlations into two components. A stable trait component and a changing state component. A third parameter estimates how stable state variance is over time.

There are several ways to relate personality to life-satisfaction in this model. I chose to predict life-satisfaction variance on each occasion to the personality variances on the same occasion. The model indirect function can then be used to examine how much of the variance is due to stable personality traits or due to personality states.

The availability of four waves of data also makes it possible to model stability of the residual variances in personality items. Typically, these residuals are allowed to correlate to allow for item-specific stability, but the use of correlated residuals makes it impossible to relate this variance to other constructs. With four waves, it is possible to fit an LST model to item-residuals. Exploration of the data showed that the neuroticism item “worry” showed consistent relationships with well-being. Thus, I fitted an LST model to this item and allowed for an influence of worry on life-satisfaction.

The synatax and the complete results are posted on OSF (SOEP.4W.B5.DSX.LS).

Results

Overall model fit was acceptable, CFI = .967, RMSEA = .019, SRMR = .030.

Trait Variance and Stability of State Variance

Table 1 shows the amount of trait variance and the stability of state variance in the personality predictor variables. A more detailed discussion of the implications of these results for personality research can be found elsewhere (Schimmack, 2019a). The results for the Big Five serve as a comparison for the trait variance in life-satisfaction.

TraitStability1Y-Stability
Neuroticism0.690.380.790.56
Extraversion0.740.380.780.51
Openness0.710.340.760.53
Agreeableness0.680.200.670.57
Conscientiousness0.600.290.730.64
Halo0.600.360.780.63
Acquiescence0.340.110.580.81
Worry0.640.520.850.60

Table 2 shows how life-satisfaction at each time point is related to personality predictors. For model identification purposes, it is necessary to fix one relationship to zero. I used openness because meta-analysis show that it is the weakest predictor of life-satisfaction (Steel et. al., 2008). I did not impose constraints across the four waves.

LS-T1LS-T2LS-T3LS-T4
Neuroticism-0.29-0.27-0.27-0.26
Extraversion0.080.070.080.09
Openness
Agreeableness0.080.050.040.03
Conscientiousness0.040.040.040.04
Halo0.190.280.240.26
Acquiescence0.180.080.120.16
Worry-0.35-0.34-0.35-0.33

The results show that out of the Big Five, neuroticism is the only notable predictor of life-satisfaction with a moderate effect size (r = -.26 to -.29). A notable finding is that extraversion is a weak predictor of life-satisfaction (r = .07 to .09). This finding is inconsistent with Costa and McCrae’s (1980) model. The results for agreeableness and conscientiousness are also weak. This finding is inconsistent with meta-analysis and with McCrae and Costa’s (1991) suggestion that high agreeableness and conscientiousness are also instrumental for higher life-satisfaction. Both halo and acquiescence bias are stronger predictors of life-satisfaction judgments than extraversion, agreeableness, and conscientiousness. Another notable finding is that the worry-facet of neuroticism is the strongest personality predictor; even stronger than the neuroticism factor (rs = -.33 to -.35). This finding is consistent with previous studies that facets of neuroticism and extraversion are better predictors of life-satisfaction than the global factors (Schimmack, Oishi, Funder, & Furr, 2004).

Table 3 shows how much of the variance in life-satisfaction is explained by trait factors that remain stable over time.

LS-T1LS-T2LS-T3LS-T4
Neuroticism0.050.050.050.05
Extraversion0.000.000.000.01
Openness
Agreeableness0.000.000.000.00
Conscientiousness0.000.000.000.00
Halo0.020.040.030.04
Acquiescence0.010.000.000.01
Worry0.080.080.080.07
Unexplained0.380.380.380.38
Total0.550.560.560.55

Given the weak effects of extraversion, agreeableness, and conscientiousness, it is not surprising that these Big Five traits explain less than 1% of the variance in life-satisfaction judgments. The only notable predictor is neuroticism, which explains 5-6% of the variance. In addition, the worry facet of neuroticism is an even stronger predictor of trait variance in life-satisfaction. This finding shows that more specific traits below the Big Five add to the prediction of life-satisfaction (Schimmack, Oishi, Furr, & Funder, 2004). Halo adds only 2% and acquiescence only 1%. By far the largest portion of the trait variance was unexplained with 41% of the variance. Combined this implies that approximately half of the variance in life-satisfaction is trait variance. This finding is consistent with estimates in a meta-analysis and other analyses of the SOEP data (Anusic & Schimmack, 2016; Schimmack, Krupp, Wagner & Schupp, 2010). The estimate of 55% trait variance is also smaller than the estimate of 70% trait variance in the Big Five personality traits. This finding is also consistent with meta-analytic comparison of personality and well-being measures (Anusic & Schimmack, 2016).

Table 4 shows the results for the state-predictors of life-satisfaction. Once more extraversion, agreeableness, and conscientiousness predict less than 1% of the variance. This time, neuroticism and worry are also relatively weak predictors because most of the relationship for this traits stems from the stable component. However, the results suggest that some changes in neuroticism and worry are related to changes in life-satisfaction. However, most of the state variance in life-satisfaction is not explained by the personality predictors (33% out of 44%).

LS-T1LS-T2LS-T3LS-T4
Neuroticism0.020.020.020.02
Extraversion0.000.000.000.00
Openness
Agreeableness0.000.000.000.00
Conscientiousness0.000.000.000.00
Halo0.010.030.020.03
Acquiescence0.020.000.010.02
Worry0.040.040.040.04
Unexplained0.330.340.340.34
Total0.440.440.440.45

Conclusion

These results challenge Costa and McCrae’s (1980) model of personality and well-being in several ways. First, extraversion is not a strong predictor of the stable variance in life-satisfaction. Second, even the influence of neuroticism accounts for only 10% of the stable trait variance in life-satisfaction. Adding other Big Five predictors also does not help because they have negligible relationships with life-satisfaction. Thus, most of the trait variance in life-satisfaction remains unexplained. It is either explained by more specific personality traits than the Big Five (facets) or by stable environmental factors (e.g., income). The SOEP data provide ample opportunity to look for additional predictors of trait variance. Also, researchers should conduct studies with broader personality questionnaires to find additional predictors of life-satisfaction. Searching for these predictors is an important area of research in an area that has stagnated over the past two decades.

Costa and McCrae’s model also underestimated the importance of state-factors. State factors are highly stable over fairly long periods of times and account for 50% of the reliable variance in life-satisfaction. As the Big Five mostly reflect stable traits, they cannot account for this important variance in life-satisfaction. Schimmack and Lucas (2010) argued that these factors are environmental factors because changes in life-satisfaction are shared between spouses. Thus, changes in actual life-circumstances may contribute to state variance in life-satisfaction. Consistent with this model, spouses were more similar in domains that are shared (housing, income) than in domains that are less shared (health, recreation).

Evidently, the conclusions are based on a single German sample. As impressive as these data are, it is important to compare results across samples from different populations. At least regarding the influence of extraversion, the present results are consistent with other studies that suggest the influence of extraversion on life-satisfaction (Kim, Schimmack, & Tsutsui, 2019). The idea that extraverts are happier has been exaggerated by Costa and McCrae’s model, while their own empirical results did not warrant this claim. The reason is that psychologists often ignore effect sizes.

Implications

The present results also have implications for developmental theories of personality. The idea of development is a process with an ideal outcome. For humans, the outcome is an adult human being with optimal capabilities. A collective of personality psychologists suggested that optimal personality development results in a personality type with optimal personality characteristics. I criticized this idea and argued that there is no such thing as an optimal personality. Just like there is no optimal height as the end-goal of human growth, there is no optimal level of extraversion or conscientiousness. In clinical psychology, the key criterion of mental health is that an intervention is beneficial for a patients’ well-being. Thus, we could argue that an optimal personality is a personality that maximizes individuals’ well-being. Meta-analyses suggests that extraveted, agreeable, and conscientious people have higher well-being. Thus, it might be beneficial for individuals to become more extraverted, agreeable, and conscientious. However, the present results challenge this view. After removing the evaluative aspect of personality from the Big Five only neuroticism remains a notable predictor of well-being. Thus, the key personality trait for self-improvement is neuroticism. Not surprisingly, this is also the key aspect that is targeted in self-help books and well-being programs. Until we have a better understanding of the relationship between personality and well-being, it seems premature to propose interventions that are aimed at changing individuals’ personality. Just like personality psychologists no longer endorse conversion therapy for sexual orientation, I urge for caution in submitting individuals who are carefree and impulsive to a conscientiousness conversion program. You never know when acting on the spur of a moment is the best course of action.

Measuring Well-Being in the SOEP

Psychology has a measurement problem. Big claims about personality, self-esteem, or well-being are based on sum-scores of self-ratings; or sometimes a single rating. This would be a minor problem if thorough validation research had demonstrated that sum-scores of self-ratings are valid measures of the constructs they are intended to represent, but such validation research is often missing. As a result, the validity of widely used measures in psychology and claims based on these measures is unknown.

The well-being literature is an interesting example of the measurement crisis because two opposing views about the validity of well-being measures co-exist. On the one hand, experimental social psychologists argue that life-satisfaction ratings are invalid and useless (Schwarz & Strack, 1999); a view that has been popularized by Noble Laureate Daniel Kahneman in his book “Thinking: Fast and Slow” (cf. Schimmack, 2018). On the other hand, well-being scientists often assume that life-satisfaction ratings are near perfect indicators of individuals’ well-being.

An editor of JPSP, which presumably means he or she is an expert, has no problem to mention both positions in the same paragraph without noting the contradiction.

There is a huge literature on well-being. Since Schwarz and Strack (1999), to take that arbitrary year as a starting point, there have been more than 11,000 empirical articles with “wellbeing” (or well-being or well being) in the title, according to PsychInfo. The vast majority of them, I submit, take the subjective evaluation of one’s own life as a perfectly valid and perhaps the best way to assess one’s own evaluation of one’s life. “

So, since Schwarz and Strack concluded that life-satisfaction judgments are practically useless, 11,000 articles have used life-satisfaction judgments as perfectly valid measures of life-satisfaction and nobody thinks this is a problem. No wonder, natural scientists don’t consider psychology a science.

The Validity of Well-Being Measures

Any attempt at validating well-being measures requires a definition of well-being that leads to testable predictions about correlations of well-being measures with other measures. Testing these predictions is called construct validation (Cronbach & Meehl, 1955; Schimmack, 2019).

The theory underlying the use of life-satisfaction judgments as measures of well-being assumes that well-being is subjective and that (healthy, adult) individuals are able to compare their actual lives to their ideal lives and to report the outcome of these comparison processes (Andrews & Whithey, 1973; Diener, Lucas, Schimmack, & Helliwell, 2009).

One prediction that follows from this model is that global life-satisfaction judgments should be correlated with judgments of satisfaction in important life domains, but not in unimportant life domains. The reason is that satisfaction with life as a whole should be related to satisfaction with (important) parts. It would make little sense for somebody to say that they are extremely satisfied with their life as a whole, but not satisfied with their family life, work, health, or anything else that matters to them. The whole point of asking a global question is the assumption that people will consider all important aspects of their lives and integrate this information into a global judgment (Andrews & Whithey, 1973). The main criticism of Schwarz and Strack (1999) was that this assumption does not describe the actual judgment process and that actual life-satisfaction judgments are based on transient and irrelevant information (e.g., current mood, Schwarz & Clore, 1983).

Top-Down vs. Bottom-Up Theories of Global and Domain Satisfaction

To muddy the waters, Diener (1984) proposed on the one hand that life-satisfaction judgments are, at least somewhat, valid indicators of life-satisfaction, while also proposing that correlations between satisfaction with life as a whole and satisfaction with domains might reflect a top-down effect.

A top-down effect implies that global life-satisfaction influences domain satisfaction. That is, health satisfaction is not a cause of life-satisfaction because good health is an important part of a good life. Instead, life-satisfaction is a content-free feeling of satisfaction that creates a halo in evaluations of specific life aspects independent of the specific evaluations of a life domain.

Diener overlooked that top-down processes invalidate life-satisfaction judgments as valid measures of wellbeing because a top-down model implies that global life-satisfaction judgments reflect only a general disposition to be satisfied without information about the actual satisfaction in important life domains. In the context of a measurement model, we can see that the top-down model implies that life-satisfaction judgments only capture the shared variance among specific life-satisfaction judgments, but fail to represent the part of satisfaction that reflects unique variance in satisfaction with specific life domains. In other words, top-down models imply that well-being does not encompass evaluations of the parts that make up an individuals entire life.

The problem that measurement models in psychology often consider unique or residual variances error variances that are often omitted from figures does not help. In the figure, the residual variances are shown and represent variation in life-aspects that are not shared across domains.

Some influential articles that examined top-down and bottom-up processes have argued in favor of top-down processes without noticing that this invalidates the use of life-satisfaction judgments as indicators of well-being or at least requires a radically different conception of well-being (well-being is being satisfied independent of how things are actually going in your life) (Heller, Watson, & Ilies, 2004).

An Integrative Top-Down vs. Bottom-Up Model

Brief et al. (1993) proposed an integrative model of top-down and bottom-up processes in life-satisfaction judgments. The main improvement of this model was to distinguish between a global disposition to be more satisfied and a global judgment of important aspects of life. As life-satisfaction judgments are meant to represent the latter, life-satisfaction judgments are the ultimate outcome of interest, not a measure of the global disposition. Brief et al. (1993) used neuroticism as an indicator for the global disposition to be less satisfied, but there are probably other factors that can contribute to a general disposition to be satisfied. The integrative model assumes that any influence of the general disposition is mediated by satisfaction with important life domains (e.g., health).

FIGURE 1. DisSat = Dispositional Satisfaction, DS1 = Domain Satisfaction 1 (e.g., health), DS2 = Domain Satisfaction 2, DS3 = Domain Satisfaction 3, LS = Life-Satisfaction.

It is important to realize that the mediation model separates two variances in domain satisfaction judgments, namely the variance that is explained by dispositional satisfaction and the variance that is not explained by dispositional satisfaction (residual variance). Both variances contribute to life-satisfaction. Thus, objective aspects of health that contribute to health satisfaction can also influence life-satisfaction. This makes the model an integrative model that allows for top-down and bottom-up effects.

One limitation of Brief et al.’s (1993) model was the use of neuroticism as sole indicator of dispositional satisfaction. While it is plausible that neuroticism is linked to more negative perceptions of all kinds of life-aspects, it may not be the only trait that matters.

Another limitation was the use of a health satisfaction as a single life domain. If people also care about other life domains, other domain satisfactions should also contribute to life-satisfaction and they could be additional mediators of the influence of neuroticism on life-satisfaction. For example, neurotic individuals might also worry more about money and financial satisfaction could influence life-satisfaction, making financial satisfaction another mediator of the influence of neuroticism on life-satisfaction.

One advantage of structural equation modeling is the ability to study constructs that do not have a direct indicator. This makes it possible to examine top-down effects without “direct” indicators of dispositional satisfaction. The reason is that dispositional satisfaction should influence satisfaction with various life domains. Thus, dispositional satisfaction is reflected in the shared variance among different domain satisfaction judgments and domain satisfaction judgments serve as indicators that can be used to measure dispositional satisfaction (see Figure 2).

Domain Satisfactions in the SOEP

It is fortunate that the creators of the Socio-Economic Panel in the 1980s included domain satisfaction measures and that these measures have been included in every wave from 1984 to 2017. This makes it possible to test the integrative top-down bottom-up model with the SOEP data.

The five domains that have been included in all surveys are health, household income, recreation, housing, and job satisfaction. However, job satisfaction is only available for those participants who are employed. To maximize the number of domains, I used all five domains and limited the analysis to working participants. The model can be used to build a model with four domains for all participants.

One limitation of the SOEP is the use of single-item indicators. This makes sense for expensive panel studies, but creates some psychometric problems. Fortunately, it is possible to estimate the reliability of single-item indicators in panel data by using Heise’s (1969) model which estimates reliability based on the pattern of retest correlations for three waves of data.

REL = r12 * r23 / r13

More data would be better and are available, but the goal was to combine the well-being model with a model of personality ratings that are available for only three waves (2005, 2009, & 2013). Thus, the same three waves for used to create an integrative top-down bottom-up model that also examined how domain satisfaction is related to global life-satisfaction across time.

The data set consisted of 3 repeated measures of 5 domain satisfaction judgments and a single life-satisfaction judgments for a total of 18 variables. The data were analyzed with MPLUS (see OSF for syntax and detailed results https://osf.io/vpcfd/ ).

Results

Overall model fit was acceptable, CFI = .988, RMSEA = .023, SRMR = .029.

The first results are the reliability and stability estimates of the five domain satisfactions and global life satisfaction (Table 1). For comparison purposes, the last column shows the estimates based on a panel analyses with annual retests (Schimmack, Krause, Wagner, & Schupp, 2010). The results show fairly consistent stability across domains with the exception of job satisfaction. Job satisfaction is less stable than other domains. The four-year stability is high, but not as high as for personality traits (Schimmack, 2019). A comparison with the panel data shows higher stability, which indicates that some of the error variance in 4-year retest studies is reliable variance that fluctuates over the four-year retest period. However, the key finding is that there is high stability in domain satisfaction judgments and life-satisfaction judgments. which makes it theoretically interesting to examine the relationship between the stable variances in domain satisfaction and life-satisfaction.

ReliabilityStability1Y-StabilityPanel
Job Satisfaction0.620.620.89
Health Satisfaction0.670.790.940.93
Financial Satisfaction0.740.810.950.91
Housing Satisfaction0.660.810.950.89
Leisure Satisfaction0.670.800.950.92
Life Satisfaction0.660.780.940.89

Table 2 examines the influence of top-down processes on domain satisfaction. Results show the factor loadings of domain satisfaction on a common factor that reflects dispositional satisfaction; that is, a general disposition to report higher levels of satisfaction. The results show that somewhere between 30% and 50% of the reliable variance in life-satisfaction judgments is explained by a general disposition factor. While this leaves ample room for domain-specific factors to influence domain satisfaction judgments, the results show a strong top-down influence.

T1T2T3
Job Satisfaction0.690.680.68
Health Satisfaction0.680.660.65
Financial Satisfaction0.600.610.63
Housing Satisfaction0.720.740.76
Leisure Satisfaction0.610.610.61

Table 3 shows the unique contribution of the disposition and the five domains to life-satisfaction concurrently and longitudinally.

DS1-LS1DS1-LS2DS1-LS3DS2-LS2DS2-LS3DS3-LS3
Disposition0.560.590.570.610.590.60
Job 0.140.100.050.170.080.12
Health0.230.220.210.280.270.33
Finances0.340.200.140.240.180.22
Housing0.040.030.030.040.040.06
Leisure0.060.100.060.130.070.09

The first notable finding is that the disposition factor accounts for the lion share of the explained variance in life-satisfaction judgments. The second important finding is that the relationship is very stable over time. The disposition measured at time 1 is an equally good predictor of life-satisfaction at time 1 (r = .56), time 2 (r = .59), and at time 3 (r = .57). This suggests that about one-third of the reliable variance in life-satisfaction judgments reflects a stable disposition to report higher or lower levels of satisfaction.

Regarding domain satisfaction, health is the strongest predictor with correlations between .21 and .33. Finances is the second strongest predictor with correlations between .14 and .34. For health satisfaction there is high stability over time. That is, time 1 health satisfaction predicts time 1 life-satisfaction nearly as well (r = .23) as time 3 life-satisfaction (r = .21). In contrast, financial satisfactions shows a bit more change over time with concurrent correlations at time 1 of r = .34 and a drop to r = .14 for life-satisfaction at time 3. This suggests that changes in financial satisfaction produces changes in life-satisfaction.

Job satisfaction has a weak influence on life-satisfaction with correlations ranging from r = .14 to .05. Like financial satisfaction, there is some evidence that changes in job satisfaction predict changes in life-satisfaction.

Housing and leisure have hardly any influence on life-satisfaction judgments with most relationships being less than .10. There is also no evidence that changes in these domain produce changes in life-satisfaction judgments.

These results show that most of the reliable variance in global life-satisfaction judgments remains unexplained and that a stable disposition accounts for most of the explained variance in life-satisfaction judgments.

Implications for the Validity of Life-Satisfaction Judgments

There are two ways to interpret the results. One interpretation is that is common in the well-being literature and hundreds of studies with the SOEP data is that life-satisfaction judgments are valid measures of well-being. Accordingly, well-being in Germany is determined mostly by a stable disposition to be satisfied. Accordingly, changing actual life-circumstances will have negligible effects on well-being. For example, Nakazato et al. (2011) used the SOEP data to examine the influence of moving on well-being. They found that decreasing housing satisfaction triggered a decision to move and that moving produces lasting increases in housing satisfaction. However, moving had no effect on life-satisfaction. This is not surprising given the present results that housing satisfaction has a negligible influence on life-satisfaction judgments. Thus, we would conclude that people are irrational by investing money in a better house, if we assume that life-satisfaction judgments are a perfectly valid measure of well-being.

The alternative interpretation is that life-satisfaction judgments are not as good as well-being researchers think they are. Rather than reflecting a weighted summary of all important aspects of life, they are based on accessible information that does not include all relevant information. The difference to Schwarz and Strack’s (1999) criticism is that bias is not due to temporarily accessible information (e.g., mood) that makes life-satisfaction judgments unreliable. As demonstrated here and elsewhere, a large portion of the variance in life-satisfaction judgments is stable. The problem is that the stable factors may be biases in life-satisfaction ratings rather than real determinants of well-being.

It is unfortunate that psychologist and other social sciences have neglected proper validation research of a measure that has been used to make major empirical claims about the determinants of well-being, and that this research has been used to make policy recommendation (Diener, Lucas, Schimmack, & Helliwell, 2009). The present results suggest that any policy recommendations based on life-satisfaction ratings alone are premature. It is time to take measurement more seriously and to improve the validity of measuring well-being.