During the last few decades, life satisfaction has been investigated by an impressive number of studies around the world. Research to date has focused on several different areas: firstly, the association between personality and (overall) life satisfaction has been investigated, although findings have been inconsistent [1
]; secondly, researchers have also explored the link between overall life satisfaction and various life satisfaction variables such as health, job, income, housing, leisure, and family [9
]. The associations between various life satisfaction variables and overall life satisfaction are complex [9
] as well as inconsistent. Other studies have investigated potential associations between physical variables and life satisfaction such as fitness activities [14
] or one’s own personal health situation [17
]. Moreover, the presence of a disease [20
] and its impact on life satisfaction have been explored. Generally, fitness activities and good health have a positive influence on life satisfaction, whereas individuals suffering from a disease usually show lower levels of life satisfaction. Furthermore, several studies have investigated cross-cultural differences in the context of life satisfaction [23
] along with the influence of demographic variables such as age and gender [26
]. These latter studies have provided some (preliminary) evidence that cross-cultural differences as well as age and gender could have an influence on life satisfaction, but more research is needed to establish clear patterns. This brief overview of studies concerning life satisfaction highlights both the worldwide interest in this research topic as well as the need for further research to determine the key influential factors.
In the literature, the terms life satisfaction, happiness, and subjective well-being are often used interchangeably. Although this might not be entirely correct, it is understandable given that these terms overlap to a certain degree [28
]. The construct of happiness plays a key role worldwide as impressively evidenced by ongoing projects such as “The World Happiness Report” (http://worldhappiness.report/
). This project demonstrates that happiness is pursued by most individuals [28
]. Although being happy seems to be of immense importance for most humans, this construct is scientifically still difficult to frame [29
]. While happiness is the overarching term used, well-being could be interpreted as a more distinct part of it [30
] and hence can be better framed and analyzed. According to Diener et al. [31
] subjective well-being (SWB) can be further subdivided into affective and cognitive components. Here positive and negative affect are related to emotional aspects of subjective well-being, whereas life satisfaction represents the cognitive part of subjective well-being [32
]. This is how we will refer to life satisfaction in the present study. Understanding variables impacting upon life satisfaction could clearly lead to a more profound understanding of the broader terms of well-being and happiness [33
Typically, life satisfaction is measured with the help of short questionnaires such as the Satisfaction with Life Scale (SWLS) [31
] or so-called “single items measures” as used in the German Socio-Economic Panel (SOEP) [34
]. An example of such a single item measure would be “How satisfied are you with your life overall?” or, in the case of specific life satisfaction variables, “How satisfied are you with your health?” The results of both types of measurement (short questionnaires and single item measures) have been shown to be very similar [35
]. In the present research, we used single-item measures as used in the SOEP. Therefore, our study design also allows us to take a closer look at the relationship between specific life satisfaction variables and overall life satisfaction.
In addition to the research described above, two approaches/theories in life satisfaction research have been discussed intensely in recent years: bottom-up versus top-down theories [11
]. Bottom-up theories consider overall life satisfaction as a function of various areas of life satisfaction [33
]. From this perspective, many different areas with a potential influence on overall life satisfaction [11
] can be identified in the literature, such as satisfaction with health, job, income, housing, leisure time, and family [11
]. In contrast, researchers have also proposed a top-down approach where life satisfaction is determined by personality disposition (which manifests in rather stable cognitive and emotional traits resulting in an individual displaying stable behavior—see Montag & Panksepp, 2017 for an overview).
The bottom-up perspective of life satisfaction implies that overall life satisfaction is a complex function of various life satisfaction variables which are usually not simply additive [9
]. When individuals are asked about their overall life satisfaction the outcome on this measure depends, amongst others, on factors such as individuals giving greater importance to a certain area of life satisfaction compared to others, or also on personal life preferences [33
]. For example, a person with a high attachment motivation will most probably appraise satisfaction with family and work differently compared to an individual with a preference for achievement (this example clearly also hints at the top-down approach, since personality traits are closely linked to individual differences in motivational aspects). The complex relationship between areas of life satisfaction and overall life satisfaction can be further explained with mechanisms termed compensation, spillover, and segmentation effects [9
]. A compensation effect implies a negative, whereas a spillover effect assumes a positive association between areas of life satisfaction variables and/or overall life satisfaction [33
]. In the first case, a change in one area, for example one’s job, would lead to an inverse change in another area, for example one’s family (being more satisfied with the job could reflect less time for family issues). In the latter case, a change in one area would cause an equal change of life satisfaction in another area (e.g., being healthy also results in higher overall life satisfaction). The term segmentation indicates that changes in one area have no effect on other areas and/or overall life satisfaction [33
]. Further detailed information concerning the nature of the relations between the areas of life satisfaction and overall life satisfaction can be found in Rojas et al. [9
The top-down perspective considers the level of overall life satisfaction or areas of life satisfaction as a function of personality and other stable traits [11
]. In this context, dispositional factors would determine the extent to which a person feels satisfied, allowing an inference on the degree of satisfaction by analyzing their personality structure. For example, the investigation of extraversion or neuroticism could give researchers an idea how satisfied a person should be: Here, mainly positive associations between extraversion and life satisfaction have been observed, whereas the link between neuroticism and life satisfaction is usually negative [30
]. These kinds of relations have also been confirmed by several meta-analyses [2
]. On the other hand, factors beyond personality traits are also considered of importance. For example, situational factors such as (critical) life events or other environmental influences have been shown to be of relevance to assessing the level of life satisfaction. A recent meta-analysis demonstrated an effect of life events especially on cognitive well-being [38
]. Taken together, these findings suggest that neither a bottom-up nor top-down perspective alone can sufficiently explain well-being. Instead, an integrated view combining both perspectives might be most successful. Although both approaches are often described as competing theories [11
], there are also several proposals to integrate both the bottom-up and top-down perspectives in one integrated model [13
]. Erdogan et al. (2012) stated that top-down effects (e.g., personality) could influence the perception of different areas of life satisfaction, and in doing so also affect overall life satisfaction. He also suggested considering personality as a distal predictor in models of life satisfaction and not as a control variable. Nevertheless, there are variables that should be controlled for in models of life satisfaction such as age, gender, and education [30
]. Several findings indicate that these variables could modify the association between personality and/or specific life satisfaction variables with overall life satisfaction [26
], although findings are not always consistent.
Cross-cultural differences between collectivistic and individualistic cultures, concerning life satisfaction, have also been observed [23
]. Notably, it can be assumed that life satisfaction is interpreted differently in different cultures: In an individualistic culture, individuals are rather focused on their own goals, interests, and feelings and not so much on the well-being of a group (such as friends or family). In contrast, in more collectivistic cultures, harmonious relationships with other people are of higher value than personal goals. This could, for example, result in a higher appraisal for family satisfaction in collectivistic compared to individualistic cultures [24
]. In addition, differences concerning the level of life satisfaction across nations have also been found: People in individualistic cultures general report higher levels of life satisfaction than ones in collectivistic cultures. One explanation for this finding is that the personal, goal-oriented view in individualistic cultures contributes to more self-referred attribution of failure and success, possibly leading to greater overall life satisfaction compared to individuals in collectivistic cultures [11
The aim of the present study was to (re-)investigate if and how strong factors, such as personality, different areas of life satisfaction, demographic variables, and cross-cultural effects, are associated with overall life satisfaction to help contribute towards a basic working model of life satisfaction. In line with this, a combination of both bottom-up and top-down approaches was chosen for the first survey. The potential influence of age and gender on overall life satisfaction was also examined (Survey 1). This first survey is of particular interest, since we used a smartphone application to assess life satisfaction and personality variables. As such, if our results were found to be similar to the existing literature, future researchers might be encouraged to use this approach more often in life satisfaction research to assess life satisfaction on a large-scale level. To replicate and confirm our results in Survey 1, we conducted a second survey with a very similar setup and the same research question (Survey 2). To further rule out any doubt concerning the validity of the very short questionnaire used to measure personality (Big Five Inventory 10 (BFI-10); please refer to the method section for further details), we repeated the analyses with a third sample this time using the NEO-FFI [44
] to gather information on personality (Survey 3). This approach helped to demonstrate that personality associations with life satisfaction are not influenced by the chosen questionnaire. Finally, to be able to explore cross-cultural differences in life satisfaction, a last survey was conducted in China (the first three studies were conducted in Europe). For this final survey, we once more also assessed the psychometric quality of BFI-10 by administering/comparing both the BFI-10 and NEO-FFI in the context of life satisfaction (Survey 4).
The aim of the present study was to extend research on the association between life satisfaction and several factors with proposed influence on life satisfaction and by combining two different approaches (bottom-up and top-down). To achieve this goal and find robust results, we used four different samples, which allowed us to first answer questions concerning the influence of the bottom-up and top-down approaches on life satisfaction [11
] and gave us the chance to replicate our findings in multiple samples. The replication of the same findings in our different samples supports their robustness and provides proof against the replication dilemma in psychological research. Additionally, we obtained further insights concerning the quality of the BFI-10 and the presence of cross-cultural effects [23
] concerning life satisfaction.
A main finding of the present study was that the association between overall life satisfaction and personality variables [4
] was not as strong as we expected. A stepwise multiple regression model considering demographic variables, personality variables, and life satisfaction variables as predictors of overall life satisfaction showed that only 0.1–1.8% of the variance of overall life satisfaction could be explained by demographic and personality variables. This finding could be replicated in all four surveys. When personality variables and life satisfaction variables were entered in a hierarchical regression model in separate blocks, the explained variance (R2
) of all personality variables did increase to a maximum of R2
= 0.098, but this was still much lower than the highest R2
(0.533) of all life satisfaction variables. Since all variables were at least weakly correlated, different values in R2
when using a stepwise versus a hierarchical regression model can be explained with the shared variance between those variables. From this perspective, life satisfaction variables (bottom-up) have a much higher impact on the overall life satisfaction score compared to all the other predictor variables. However, this does not mean that the association of personality variables and overall life satisfaction should be ignored, since there were still moderate and significant correlations between personality and overall life satisfaction in our samples. This view is also supported by the meta-analysis of deNeve et al. [2
]. For our findings, concerning the rather weak association between personality variables and overall life satisfaction we would like to suggest the following explanation. Diener et al. [58
] has emphasized the necessity of investigating not only the direct link between personality and overall life satisfaction, but also interactional and indirect effects (such as the presence or absence of various life circumstances). Additionally, some authors have reported moderating or mediating effects of specific variables influencing the link between personality and overall life satisfaction. For example, Gutierez et al. [41
] pointed out the need for considering demographic variables, and Magee et al. [26
] discussed the influence of cultural background on personality and life satisfaction. Thus, the association between personality and overall life satisfaction seems to be a complex network with both direct and indirect pathways. Additionally, several further factors might have the potential to influence the association between personality and overall life satisfaction. These include, for example, the individual’s personal life circumstances, health situation, level of physical fitness, and the presence of diseases. If all these factors have a share in the determination of overall life satisfaction, a single factor would probably contribute only to a low extent to the overall life satisfaction score. Hence, the higher the number of predictors to determine a criterion, the more difficult it should be to find robust results. This is possibly one reason why the association between personality and overall life satisfaction was rather weak in the present study. In addition to this, we would like to emphasize that the substantial number of participants collected in Survey 1 could be achieved via a large-scale smartphone study. The fact that the same findings were obtained in our other surveys using more traditional approaches (in line with previous studies contributing to the literature) demonstrates the feasibility of assessing life-satisfaction using smartphone devices. Future studies might even use this smartphone approach to carry out longitudinal studies with respect to life satisfaction.
Another finding in the present study relates to the association between life satisfaction variables and overall life satisfaction. To obtain a better understanding of the parameters contributing to overall life satisfaction, it seemed appropriate to also analyze the impact of various life satisfaction variables. The variable with the highest impact on overall life satisfaction (Surveys 1–3 in Germany) was leisure. Similar findings were found in previous studies where high satisfaction in leisure had a positive effect on overall satisfaction (12,58). Indeed, there seems to be a link between leisure as a key life satisfaction area and overall life satisfaction. A reason for this finding could be that leisure is becoming more and more important (at least in Western cultures), possibly as a counterpart to daily work or family life (work–life balance is often discussed as an important topic). With the planning and execution of leisure activities, everyone has the chance to design their own private domain and therefore act more autonomously, compared to the areas of work and family where most activities are predetermined (e.g., carrying out work orders and taking care of children necessities). Autonomy has been proposed as one of the core psychological mechanisms [12
] enhancing leisure satisfaction and subsequently overall life satisfaction. However, for this to occur, an individual must have the chance to make independent decisions.
With respect to top-down and bottom-up approaches, our current findings suggest that both personality and life satisfaction variables are associated with overall life satisfaction. This view supports an integrative approach towards a model of life satisfaction [13
], where life satisfaction and personality variables together would contribute to the overall life satisfaction score. It is notable that personality is considered as stable over long periods of time [1
], but on the other hand there are various studies showing a close link between personality variables and changes in individual overall life satisfaction [26
]. Therefore, possibly not personality alone but other factors such as various life satisfaction variables are responsible for changes in overall life satisfaction. In this context arguing towards an integrative model of life satisfaction, it is conceivable that the level of life satisfaction is composed of a personality effect representing a stable component over time (trait-like), whereas other factors such as life satisfaction variables, life events, and diseases temporarily modulate the level of life satisfaction (state-like) during the period they are experienced.
After completion of the first two surveys, we continued to focus on the rather moderate influence of personality variables on overall life satisfaction. Although we had some suggestions for this finding (see above), we decided to take a closer look at correlation findings between the BFI-10 and life satisfaction, even though we did not have any evidence to question its reliability. We followed this course of action to insure the absence of a possible bias in our analyses regarding the BFI-10. For this reason, in the third survey, we replaced the BFI-10 [47
] with the NEO-FFI [44
]. The NEO-FFI was chosen because its psychometric quality has been demonstrated in many studies all around the world. However, the research question of the first two surveys remained the same. Compared to the first two surveys, the results of the stepwise regression analyses did not change with respect to the type of the included variables: life satisfaction variables still explained most of the variance (R2
= 0.471) compared to demographic and personality variables (R2
= 0.078). In other words, the personality questionnaire used (BFI-10 vs. NEO-FFI) did not alter our results. This is a favorable result for the psychometric quality of the BFI-10. Furthermore, in Survey 4, where we had our focus on cross-cultural differences in life satisfaction, we administered both the BFI-10 and the NEO-FFI, and the results were quite similar. Therefore, we conclude that the BFI-10 has an adequate psychometric quality as originally suggested by Rammstedt et al. [47
]. The advantage of using a shorter questionnaire in terms of the time saved during processing compared to a longer version (and the potential higher number of participants who can therefore be included in a study) is clearly attractive.
Finally, the fourth survey conducted in China enabled the investigation of possible cross-cultural differences in life satisfaction. As before, the study design and the research question remained the same. Again, in terms of overall life satisfaction, neither personality variables nor demographic variables explained a notable part of the variance. However, in contrast to the findings of the three surveys conducted in Germany, family rather than leisure was the dominate predictor of overall life satisfaction. With respect to the differences between collectivistic cultures, such as in China, and more individualistic cultures, such as in Germany, this result seems understandable. One of the key features of a collectivistic culture is importance of relevant social relationships (e.g., family and friends) and promoting common welfare [25
]. Thus, satisfaction with family could therefore have a higher impact on overall life satisfaction than satisfaction with leisure. On the other hand, in a more individualistic culture as Germany, individual and personal interests are of immense importance. From this perspective, the arrangement of and participation in individual leisure activities might be of much greater importance in determining overall life satisfaction, resulting in the different weighting of satisfaction with family and leisure in China and Germany. In this context, we would like to point out that satisfaction with leisure (in addition to other variables) still had a considerable impact on overall life satisfaction in China as well as satisfaction with family in Germany. However, it appears that the impact of life satisfaction variables on overall life satisfaction is different in China and Germany. The result of our fourth survey implies that cross-cultural differences have the potential to influence life satisfaction. Therefore, in future studies, the influence of diverse cultures should be accounted for when investigating or comparing life satisfaction across cultures.
The present study has several strengths but also some limitations. First, we used a cross-sectional design, so it is impossible for us to make inferences about the causality of the relationship between all included variables. Second, we gathered the data by means of electronic devices [61
] (smartphones, tablets, and personal computers), which excludes any participants not familiar with or not willing to use this kind of technology (however our findings also demonstrate the feasibility of using smartphones for such a research endeavor as the one presented here). Although this could be considered a limitation, it should be kept in mind that the distribution of personal computers, smartphones, and tablets has increased enormously. Moreover, we assume that only people with an affinity for these devices would download an application to use it on their smartphones (Survey 1) or visit an exhibition (Survey 2). Thus the quality of the data, considering the motivation of participants and their ability to use electronic devices, should be excellent. Third, in our second survey, the data were collected within the scope of an exhibition, traveling on a large boat. Since typically only people interested in the topic of the exhibition would consider visiting it, there is a chance that this may have biased our sample. Fourth, all samples differ with respect to their demographic characteristics. The reasons for this are, for example, differences in sample size (ranging from 40,297 to 488 participants), the composition of the samples (students versus non-student samples) and the distribution of gender. Our samples are therefore not fully comparable and the risk of a potential demographic bias exists. Fifth, the life satisfaction variables were ordered following a recommendation of the SOEP to avoid the potential for a biased sample. In doing so the findings could have been possibly influenced by a recency effect (the last item on the list of life satisfaction variables has the highest impact on the judgment of overall life satisfaction). Even though the question concerning overall life satisfaction should be still placed as the final item (SOEP recommendation), it would be advisable (on the basis of our experience from the present study) to counter-balance the life satisfaction domains in future research projects. Finally, for Surveys 1 and 2, we did not gather information on satisfaction with family. This variable was included in Surveys 3 and 4 because it was also one of our goals to investigate cross-cultural effects in terms of life satisfaction. To obtain a more precise view on life satisfaction and for a better comparison of all four surveys, it would have been preferable to have the variable family also included in Surveys 1 and 2.
Besides the limitations mentioned above, there are also several strengths in our study. First, all surveys within this study rely on high sample sizes (up to N
= 40,297) and for the replications we achieved sample sizes between N
= 488 and N
= 4453. Second, the results of the present study are robust because we replicated them in several independent surveys. This is noteworthy, since many research findings do not replicate in psychological science (replication crisis) [63
]. Third, the design of the study made it possible to investigate cross-cultural influences in life satisfaction. Furthermore, we were able to investigate the reliability of BFI-10. This goal was achieved in two separate ways: once by replacing the BFI-10 with the NEO-FFI in Survey 3 and secondly by including both questionnaires in Survey 4. This second approach was especially suited for obtaining detailed information concerning the psychometric quality of the BFI-10. Finally, the use of personal computers, smartphones, and tablets to gather information minimizes the occurrence of transcription errors and therefore increases the accuracy of the data.