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Share your thoughts with other customers. Therefore, we retained this fairly complicated curve for the final model. Estimated parameters are presented in Table 1 and predicted satisfaction scores are presented as a line with open triangles in Fig. Even without adjusting for any potentially confounding effects, the estimated trajectory from the multilevel model converges quite well with both the cross-sectional data and the aggregated results, at least until very late in life.
In all three lines, life satisfaction starts out around 5. However, the drop in the predicted scores from the multilevel model is markedly steeper than the drop in the aggregated data, which in turn, is slightly steeper than the drop in the cross-sectional data. To investigate this issue further, we used the strategies employed in Study 1. First, we investigated potential cohort effects.
Because the three refreshment samples entered the study relatively soon after the first wave in which life satisfaction was included and thus, the length of time between assessments is not large , we did not use the strategy of comparing different sub-samples though we do address this issue below. Instead, we used the logic of the cohort sequential design to compare different cohorts at the same age. Specifically, we created 5-year birth cohorts e.
To simplify this figure and to focus more precisely on the parts of the lifespan that seemed to have the least convergence across methods, we plotted from age 60 to age To increase the reliability of each data point and to smooth the graph, we aggregated into 2-year age groups. In addition, we eliminated any data point with fewer than 20 participants. As can be seen in Fig. Among all cohorts examined, the age differences appear to be consistent with the results found using the first-wave cross-sectional data.
Life satisfaction appears to increase slightly from age 60 to the mid 70s and then decline slightly until late life. Thus, cohort effects do not seem to be responsible for the lack of convergence across the analyses presented in Fig.
Next, we assessed whether there was evidence of instrumentation effects using the same multilevel modeling technique that was employed in Study 1. Specifically, we tested a model predicting within-person changes in life satisfaction from a within-person wave variable that indicated the current wave of the study.
Both the intercept and slope were predicted from 38 level-2 dummy variables representing the different 2-year age groups from age 16 to We then concatenated the results in a single figure. If there is convergence across age groups and with the cross-sectional results , then this would suggest that the within-person trends reflect developmental change.
If, on the other hand, there is no convergence and change is similar across different age groups , then this would suggest the existence of instrumentation effects as seen in the GSOEP.
Results from this analysis are presented in Fig. The first-wave cross-sectional effects from Fig.
A number of features of this analysis are noteworthy. First, for much of the life-span, there is remarkable convergence both across adjacent age groups and with the overall cross-sectional effect. This suggests that at least until the 60s, there is no instrumentation effect and the longitudinal change for each age group maps onto what would be expected from analyses based on only the first wave of participation. Figure 6 also shows, however, that there is less convergence across adjacent age groups from the 60s onward.
Starting with the group of individuals who entered the study at age 58, the within-person slopes become negative, even though the cross-sectional results show life satisfaction increasing until the early 70s and even though our previous cohort analyses showed no cohort effects. Comparing the different age groups from age 60 onward it is evident that the starting point for all groups is quite similar; however, the slopes are negative and much steeper than would be expected based on the cross-sectional results and the intercepts for adjacent groups.
This exception withstanding, the various techniques for estimating age effects converge quite well for most of the lifespan. For that reason, it is not necessary to control for instrumentation effects when estimating the final multilevel model that takes into account both cross-sectional and longitudinal effects. As a final test of the robustness of these effects, we estimated a multilevel model using sample membership as a moderator. This analysis can determine whether the general pattern of age-related change in the BHPS replicates across the four different samples.
Predicted satisfaction scores are presented in Fig. As these lines show, very similar curves emerged in all four samples. There is a larger late-life increase among the Northern Ireland sample than in the other three samples; but otherwise the effects are quite consistent. In the British data, life satisfaction declines early in adulthood, increases from mid to late adulthood, and then declines again at the very end of life.
With the exception of the estimated trajectories for the oldest participants, results from the various analytic techniques converged. For these oldest groups, however, within-person modeling of longitudinal change suggested steeper declines in life satisfaction than did cross-sectional results.
There are at least two explanations for these effects. First, it is possible that there is an instrumentation effect similar to that found in the GSOEP, but that this effect only emerges among older adults.
Although this explanation is certainly plausible, it is unclear why such an interaction between age and wave of assessment would occur. We believe that a more likely explanation is that there is a selection effect whereby the initially selected sample becomes less and less representative of typical people among older and older age groups. For instance, an individual who entered the study at age 81 is somewhat unusual in terms of longevity as of , women born in Britain are expected to live It is reasonable to assume that someone who is above average in life expectancy would also be above average in other characteristics including health and perhaps even emotional well-being see Hofer and Sliwinski Thus, cross-sectional analyses may show stable or even increasing levels of life satisfaction among the oldest age groups because these oldest individuals are select cases.
In fact, those who agree to participate in very old age may not even be representative of those who are still alive at that age. Because health problems and other age-related changes may present barriers to participation, only the healthiest older adults may choose to participate in an on-going study like the BHPS. However, once contact has been made and participation has begun, respondents may feel obligated to continue participating even if they would not have agreed to begin participation at that time. Thus, an year-old who began the study at age 70 may be less healthy than an year-old who began the study that same year.
Although we cannot determine using the available data whether this explanation is correct, this type of effect could explain the pattern of results shown in Fig. This, of course, would have implications for the interpretation of age-related changes that are typically found using cross-sectional designs.
In this sample, cross-sectional analyses of the oldest old show a much more optimistic picture of age-related changes in life satisfaction than do longitudinal analyses. The goal of the current studies was to examine age-related trends in life satisfaction. We examined these in the context of three perspectives: We accomplished these goals using two very large, nationally representative panel studies with refreshment samples with larger age ranges than are typically examined. Although there were some consistent findings across the two studies, there were also some substantial differences.
We first discuss the similarities and then point out the differences that emerged. In terms of similarities, both studies showed that for much of the lifespan, there is little evidence for a trend of declining life satisfaction. The GSOEP showed no declines until late life; the BHPS showed moderate declines early in adulthood, but these declines were reversed from middle-age until the mid 70s also see Blanchflower and Oswald These results provide a relatively optimistic picture of life satisfaction from early to late adulthood.
The picture is less rosy, however, for those in very late life. Both studies suggest that life satisfaction levels begin to decline after the mid 70s, and these declines can be quite substantial. These declines may be related to increasing health problems, the loss of social support, or even impending death. For instance, Gerstorf et al. This robust evidence for a late-life decline provides some support for the classical model of subjective well-being and refutes a strong reading of socioemotional selectivity theory.
At the time of life when health, income, and social support are declining Baltes and Mayer and individuals are approaching the end of life, life satisfaction drops quite steeply. It is possible that the processes described by socioemotional selectivity theory do counteract some declines in objective circumstances, and the combined effects of these countervailing forces result in stable or even increasing levels of life satisfaction among the middle-aged and older adults in these samples.
But even if these effects contribute to stability among the young old, they are not strong enough to protect the oldest participants from a substantial drop in happiness at the end of life once objective circumstances become particularly negative and particularly salient. As we noted above, this pattern replicates across the various subsamples of the BHPS, and it shows up in both the cross-sectional and longitudinal analyses.
This pattern could be due to the processes described in socioemotional selectivity theory though the increase in satisfaction begins decades before people reach their life expectancy , or it could be due to changes in some objective circumstances that begin in middle age and continue until late life. For instance, research shows that relationship satisfaction among couples often drops after the birth of a child and this decline lasts through the child's teenage years for a review, see Myers The rebound that occurs during the empty nest years may contribute to an overall increase in life satisfaction among middle-aged and older adults.
In any case, it is unclear why this U -shaped trajectory emerges in one of our studies but not the other, but subtle cultural differences across nations may be responsible. In support of this idea, Deaton found that age-related differences in life satisfaction varied a great deal across nations. In nations from Eastern Europe and the former Soviet Union, life satisfaction declined steadily with age, whereas in wealthy English-speaking countries, life satisfaction was S -shaped across the lifespan.
Deaton argued that some of this cross-nation variation was due to differences in national income. Results such as this suggest that there may be no simple answer to the question of how life satisfaction changes with age. The diverse results reviewed in the introduction to this paper may not simply represent the effects of sampling error or differences in methods. Instead, this may reflect the true state of this association—the links between age and life satisfaction may vary depending on important contextual factors Deaton Both of the studies included in our analyses used extremely large representative samples collected using rigorous procedures and analyzed with a variety of complex analytic techniques; yet the conclusions from these two studies differ in important ways.
It is clear that future research will examine the potential contextual factors [some of which have been identified by Deaton and Pinquart ] that might explain these cross-study differences. Although average levels of life satisfaction were stable across most of the lifespan in the GSOEP, they declined substantially at the very end of life. Because personality change is limited at this stage of the lifespan Roberts et al. To be sure, our results do not rule out the possibility of a moderate to strong influence of personality factors on subjective well-being. In fact, there is strong evidence that personality processes lead to considerable rank-order stability of life satisfaction even over very long periods of time see Lucas and Donnellan , for evidence regarding the stability of life satisfaction in the GSOEP.
However, these personality influences are not so strong as to prevent mean-level age-related changes from occurring.
This point underscores the difference between differential and absolute stability see Roberts et al. These studies also have important methodological implications for future work that examines the association between age and subjective well-being, and for work that uses the GSOEP and BHPS data. Most notably, these studies confirm that neither cross-sectional nor longitudinal designs on their own can provide an unambiguous picture of the changes that occur across the lifespan. In the GSOEP, instrumentation effects appear to lead to an overestimation of the decline that occurs over the course of the lifespan when unadjusted longitudinal modeling techniques are used.
In this case, the cross-sectional data probably provide a more accurate picture of the changes that occur over the course of the lifespan. In the BHPS, the estimates from the longitudinal data converged well with the cross-sectional trajectories, at least until late life. Among the oldest adults, convergence was less clear, as life satisfaction levels dropped more quickly than would be suggested by the cross-sectional data or by the starting level of satisfaction among older groups. We believe that the best explanation for this late-life effect is that there is a selection bias in which the oldest adults who agree to participate in an intensive study like the BHPS may be less representative than younger participants who agree to take part.
If so, then the cross-sectional data may overestimate the life satisfaction of these oldest adults. Ultimately, it is necessary to have data from sufficiently complex designs to rule out possible confounds on a study-by-study basis. Our recommendation is therefore necessarily broad—researchers should continue to heed classic advice about the virtues of combining cross-sectional and longitudinal designs to best understand developmental change.
Recently, a growing number of studies have used longitudinal analyses of data from the GSOEP to examine how life satisfaction changes following the experience of life events see Lucas b , for a review. However, the results of these longitudinal analyses could be distorted if there are instrumentation effects that lead to decreasing life satisfaction as respondents participate in additional waves.
Although one could correct for this by including wave dummy variables in the life event analyses themselves, the estimates for the instrumentation effects from such an analysis might not be optimal because they would be based on smaller, select samples and because they do not specifically take into account unique information provided by the refreshment samples. Our final analyses suggest that the instrumentation effect accounts for about a. Correcting for this decline might provide more precise estimates when examining other questions that rely on longitudinal analyses of GSOEP data.
The strengths of these studies are clear—as far as we know, these are the largest nationally representative studies that include multiple cohorts, many waves of data, and refreshment samples to be used in research on the link between age and subjective well-being. But like any study, these studies also have some limitations. Foremost, we only focused on one aspect of subjective well-being, namely life satisfaction, because neither the BHPS nor the GSOEP included clear and repeated measures of positive and negative affect. The BHPS does include a measure of psychological distress the General Health Questionnaire; Goldberg and Williams , but the links between this measure and the commonly studied components of subjective well-being are unclear.
Thus, support for the various models of well-being might have differed if these other components had been measured and our results are limited to changes in life satisfaction that may occur with age. A second potential limitation concerns the use of a single-item measure of life satisfaction. For instance, one main concern about single-item scales is the potential for low reliability. In addition, the fact that we were able to identify robust age-related changes suggests that there is enough valid variance to capture reliable trends across the lifespan. An additional concern about single-item scales relates to their breadth of coverage.
However, life satisfaction is a relatively narrow and straightforward concept that should easily be captured with a single statement. In fact, multiple-item scales often include just slightly different wordings of the same basic idea e. Finally, single-item measures have been shown to be valid. For instance, these measures have been shown to be reactive to changing life circumstances, and the size of the effects obtained with single-item measures are often very similar in size to those obtained using multi-item measures e.
Thus, evidence suggests that concerns about the quality of the single-item measures used in the GSOEP and BHPS should not limit the conclusions that can be drawn from these data. Answers to seemingly simple questions regarding age-related changes in subjective well-being turn out to be deceptively complex. In our research with two nationally representative studies, the most robust finding was that life satisfaction declines at the end of the lifespan, a time during which many objective circumstances are likely to be worsening for individuals who are fortunate enough to reach the average life expectancy.
This result seems to support the bottom-up perspective of subjective well-being in that the appraisal of life satisfaction is based, at least partially, on objective conditions. Neither the original collectors of the data nor the archive bear any responsibility for the analyses or interpretations presented here. For instance, Diener et al. Positive and negative affect are emotional dimensions that capture the affective feelings that people experience as they live their lives.
Life satisfaction is a cognitive judgment that taps an individual's reflective judgment about the conditions in his or her life. In the current paper, we use these terms positive affect, negative affect, and life satisfaction when referring to research that has assessed one of these more specific variables.
We should note, however, that the empirical research we present in these studies focuses specifically on life satisfaction, rather than on the affective dimensions. However, the confidence intervals for each data point are quite large among the oldest groups, and this is the only age group that did not follow the general trend of lower life satisfaction among the very old. This contrasts with the instrumentation effect analyses from the previous model where there was no overall intercept.
In this earlier model there were 38 dummy variables for the 38 age groups because we estimated separate intercepts and slopes for each group. Centered age was divided by 10 before calculating the higher-order powers. To ensure that this variable had a similar meaning in all sub-samples, we decided upon the latter. However, results are the same regardless of the coding that is used. More importantly, the estimated age trajectory did not change once these effects were controlled. National Center for Biotechnology Information , U.
Author manuscript; available in PMC Nov 1. Baird , Richard E. Lucas , and M. Correspondence concerning this article should be addressed to Brendan M. See other articles in PMC that cite the published article.
Abstract Two large-scale, nationally representative panel studies the German Socio Economic Panel Study and the British Household Panel Study were used to assess changes in life satisfaction over the lifespan. Subjective well-being, Life satisfaction, Aging, Sequential design, Instrumentation effects. Open in a separate window. Table 1 Estimated parameters for age in Studies 1 and 2. The British Household Panel Study 6. Results from the cross-sectional, aggregated, and multilevel modeling analyses BHPS.
Estimated life satisfaction trajectory for the four subsamples of the BHPS. Contributor Information Brendan M. Longitudinal and cross-sectional sequences in the study of age and generation effects. The Berlin aging study: Aging from 70 to Cambridge University Press; An accelerated longitudinal approach. Is well-being U-shaped over the life cycle? Social Science and Medicine. The quality of American life: Perceptions, evaluations, and satisfactions.
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However, the confidence intervals for each data point are quite large among the oldest groups, and this is the only age group that did not follow the general trend of lower life satisfaction among the very old. This point underscores the difference between differential and absolute stability see Roberts et al. Unfortunately, however, additional studies contradict this positive picture of well-being in later life. Amazon Rapids Fun stories for kids on the go. There are at least two explanations for these effects. Like the GSOEP, additional sub-samples were added at various points in the study, including samples from Scotland and Wales in , and a sample from Northern Ireland in Unlike the data from the GSOEP, this aggregated analysis converges quite well with the simple cross-sectional trajectory, and the lines overlap almost perfectly.
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