Meaning in life can be understood as how much people experience life meaning (i.e., presence of meaning, POM) and how intensely they seek life meaning (i.e., search for meaning, SFM). Previous research has related POM and SFM to the subjective well-being (SWB) of individuals, but the findings are inconsistent. This meta-analysis investigates the overall relationship between POM/SFM and SWB by examining previous studies that have used Steger et al.’s (J Couns Psychol 53:80–93, 2006. https://doi.org/10.1037/0022-022.214.171.124) Meaning in Life Questionnaire to assess POM and SFM. Results of 147 studies, reporting 726 effect sizes (N = 92,169), suggest the effect size for the “POM–SWB” relationship is close to medium (ESz = .418, p < .001, 95% CI [.390, .446]). The effect is larger in life satisfaction and cross-sectional studies. The effect size for the “SFM–SWB” association is small (ESz = − .121, p < .001, 95% CI [− .155,− .087]), with the effect being larger for negative affect, cross-sectional studies, and older participants. Interestingly, SFM is related to more SWB in participants from countries that are more collectivistic. This study shows a robust link between presence of life meaning and greater SWB, and that while search for life meaning may be adverse to SWB, the effect is small and conditional.
Meaning in life refers to “the sense made of, and significance felt regarding, the nature of one’s being and existence”. Historically, it has been considered an indicator of well-being, a facilitator of adaptive coping and a marker of therapeutic growth (Steger et al. 2006). In the recent decade, meaning in life has been understood as having two distinct dimensions (i.e., presence of meaning, POM; and search for meaning, SFM), both related to the extent to which people feel and seek life meaning. Since the proposal of POM and SFM as factors, an increasing number of studies have employed Steger et al.’s (2006) Meaning in Life Questionnaire (MLQ) to assess POM and SFM and to examine their relationships with subjective well-being (SWB). However, existing findings are inconclusive, particularly for SFM. Given the theoretical and practical importance of meaning in life to SWB, it is crucial to understand the extent to which POM and SFM are related to SWB. To take stock of the literature, this study uses a three-level meta-analysis to investigate three research questions based on studies that have used Steger et al.’s (2006) MLQ to measure POM and SFM: (1) what is the average association between POM and SWB? (2) What is the association between SFM and SWB? And (3) what factors moderate the relationship between POM and SWB and that between SFM and SWB? The findings have pivotal theoretical implications for the research into meaning in life and practical insights for improving personal SWB.
Concept, Components, and Assessment of SWB
SWB encompasses a broad range of phenomena comprising people’s domain and global satisfaction of life and emotional responses. As these categories are substantially correlated, researchers often view SWB as “a general area of scientific interest rather than a single specific construct” (Diener et al. 1999, p. 277). In this study, we use Diener’s (2000, p. 34) definition which defines SWB as “people’s evaluations of their lives—evaluations that are both affective and cognitive.”
According to the typologies proposed by Diener et al. (2000; Diener et al. 1999), SWB is made up of four components: pleasant affect, unpleasant affect, life satisfaction, and domain satisfaction). Pleasant affect (also known as positive affect) refers to individuals’ experiences of positive mood and emotion (e.g., happiness, joy, elation, etc.). Unpleasant affect (also known as negative affect) refers to individuals’ experiences of negative mood and emotion (e.g., guilt and shame, anxiety/depression, stress and sadness, etc.). It is notable that the levels of positive affect are relatively independent from that of negative affect (Cacioppo et al. 1999). Life satisfaction refers to one’s cognitive judgment concerning one’s global life experience (e.g., satisfaction with current life, satisfaction with past life, etc.). Domain satisfaction refers to one’s cognitive judgment about important domains in one’s life (e.g., satisfaction with work, satisfaction with family, satisfaction with oneself, etc.). These four components define what a good life is. It is believed that people with high SWB experience increased positive affect and reduced negative affect, and exhibit high satisfaction with their global life and in other important domains.
Self-report measures are the primary instrument used to measure SWB. Early research treated SWB as a construct with a single element and measured it using a single item (e.g., “How do you feel about your life as a whole?” Andrews and Withey 1976). Later, as SWB became understood as a multiple-component construct, researchers tended to measure multiple items to assess components of SWB. For instance, the Satisfaction with Life Scale (Diener et al. 1985) is often used to measure people’s evaluation of global life satisfaction. Domain satisfaction is usually measured with satisfaction scales focusing on a certain domain, such as work satisfaction (Van Saane et al. 2003). To measure pleasant affect and unpleasant affect, the Positive and Negative Affect Scale (Watson et al. 1988) is frequently used. Studies also use scales that measure emotional elation and problems (e.g., happiness, anxiety, depression, etc.) to assess positive and negative affect. These include the Subjective Happiness Scale (Lyubomirsky and Lepper 1999) and the Depression, Anxiety, and Stress Scale (DASS, Lovibond and Lovibond 1995) respectively. SWB experts recommend a separate assessment of each component of SWB, as each may provide rich information about whether and how predictors relate to each aspect of SWB (Diener et al. 1999; Diener 2000). In this study, we not only examine the effect sizes for the overall relationship between POM/SFM and SWB, but also explore whether the magnitude of the association between POM/SFM and each SWB component varies.
Association Between POM/SFM and SWB
Association Between POM and SWB
POM refers to “the degree to which people experience their lives as comprehensible and significant, and feel a sense of purpose or mission in their lives that transcends the mundane concerns of daily life” (Steger et al. 2008a, p. 661, b, c). POM is linked with greater SWB in two ways. On one hand, the meaning maintenance model (Heine et al. 2006) suggests that meaning in life is cognitive in nature and encompasses a number of cognitive skills and dimensions to maintain one’s sense of meaning, sense, and purpose that directly promotes a feeling of well-being. On the other hand, meaning in life may indirectly promote SWB by enhancing relevant factors important to SWB. For instance, scholars contend that meaning in life facilitates self-control by guiding people to transcend momentary urges and thus overrides the more primitive and impulsive mode of living (Li et al. 2019; MacKenzie and Baumeister 2014). Self-control is the ability to modify thoughts, emotions, and behaviors to align with social norms and personal standards (Tangney et al. 2004). Self-control is linearly associated with higher SWB (e.g., Hofmann et al. 2014; Li et al. 2016; Weise et al. 2018). Hence, POM is generally thought of as a robust factor related to personal thriving.
Ample studies have used the MLQ to assess POM and associated it with various SWB components. While most revealed that higher levels of POM relate to more domain-specific satisfaction and global satisfaction, more positive affect and less negative affect (e.g., Chan 2017; Dezutter et al. 2013; Steger et al. 2006), there are inconsistencies. Some studies have found a large effect for the association (r > .50; e.g., To 2016; Vela et al. 2017) while other studies revealed only a small effect (r < .10; e.g., Elekes 2017; To and Sung 2017). Moreover, some researches have failed to disclose any significant relation between POM and SWB at all (e.g., Bailey and Phillips 2016; Dunn and O’Brien 2009; Ivtzan et al. 2017).
Association Between SFM and SWB
SFM refers to “the dynamic, active effort people expend trying to establish and augment their comprehension of the meaning, significance, and purpose of their lives” (Steger et al. 2008a, p. 661, b, c). The relationship between SFM and SWB is debatable. There are currently two opposing views regarding their association. On one hand, SFM may serve as a resilience factor that moderates the negative effect of adverse events on SWB. The meaning-making model assumes that individuals who encounter negative life events may restore SWB by purposively making meaning out of these events (Park 2010). In parallel with the model, it is also considered that when people experience negative events, SFM assists people to look for new opportunities, adopt positive accommodation of the threatening information, re-understand and re-organize past experiences, and overcome challenges, which eventually leads to increased adjustment and positive changes in well-being (Frankl 1963; Joseph and Linley 2005; Vohs et al. 2019). On the other hand, SFM may be an indicator of maladjustment and psychopathology. It is considered that human are inherently motivated to pursue meaningfulness and that search for meaning occurs in individuals whose needs have been frustrated and have not formed clear life meaning (Baumeister 1991; Baumeister and Vohs 2002; Klinger 1998). Moreover, scholar also reckons that the quest of meaning is central motivational principle in everyday living and without it individuals would show apathetic activity, inactivity, and psychopathology (Klinger 2008). To reconcile these opposing views, some have proposed that SFM helps people who have experienced distress to cope with stressful and adverse situations, but that it may be harmful for those not facing frustrated situations as it may represent a loss of life goals (Damásio and Koller 2015a, b). Hence, comparing individuals who have experienced stressful events with those who have not may be key to elaborating the relationship between SFM and SWB.
Many studies have utilized the MLQ to assess SFM and relate it to SWB, but the findings are substantially inconsistent. A number of studies have supported the idea that higher levels of SFM relate to less SWB, including greater negative emotion, less positive emotion and life satisfaction (e.g., Bailey and Phillips 2016; Battersby and Phillips 2016; Boyraz et al. 2013). Conversely, many studies reveal no significant correlative or even contradictory relationship (e.g., Chan 2017; Datu and Mateo 2015; Dezutter et al. 2015).
Conceptually and methodologically, dividing meaning in life into POM and SFM has deepened our understanding of the relationship between meaning in life and SWB. However, studies of the relationships of POM/SFM and SWB are inconclusive in terms of significance, magnitude, and direction. A meta-analysis to examine the overall associations is therefore desirable. Also, the inconsistent findings across studies suggest that one or more factors are likely to moderate the relationships. Hence, in addition to the overall associations, we also investigate some potential moderators.
Components of SWB
Prior research has found that different personality traits are related in different ways to the four components of SWB (DeNeve and Cooper 1998). SWB experts contend that some predictors are related more to cognitive components of SWB (e.g., satisfaction) while other predictors are related more to affective components of SWB (positive and negative affect, Diener 2000; Diener et al. 1999). Following this, in this study we not only examine the overall association between POM/SFM and SWB, but we also explore whether the strength of the relationship between POM/SFM and each component of SWB varies. Specifically, we treat the four components of SWB as a moderator and compare their relationships with POM/SFM.
According to the meaning-making model, individuals who have recently experienced, or are currently experiencing, psychological and/or physical distress may adjust better by making meaning out of the distressful events (Park 2010; Vohs et al. 2019). Some scholars even consider that the SFM may have a positive effect on those in distress but a negative effect on those not in distress (Damásio and Koller 2015a, b). Thus, it may be possible that the degree to which participants are experiencing distress, if at all, may moderate the relationship between meaning in life (particularly SFM) and SWB. We explore this possibility in this research.
The dialectic model of meaning in life (Steger et al. 2008c) suggests that POM has a more important role for people in individualistic cultures than for those in collectivistic cultures, because people in individualistic cultures adopt self-presentation strategies to enhance self-image, and thus they endorse POM but see SFM as a negative indicator that suggests low self-image. The model also assumes that those in collective environments usually see seeking meaning in life as a process of self-improvement, and thus SFM should theoretically relate positively to adjustment (e.g., SWB) for them. Prior research has found a stronger relationship between POM and SWB among U.S. participants than those from Japan. Conversely, the relationship between SFM and SWB is negative for U.S participants, but positive for Japanese ones (Steger et al. 2008c). This implies that individualism may moderate the association between POM/SFM and SWB. In this study we use Hofstede’s individualism index as a measure to explore this possibility.
Searching for and finding life meaning takes time. According to Steger, Oishi, and Kashdan (2009), exploration is a hallmark characteristic of emerging adults because of their needs to determine and establish identity, career and social roles, but such exploration is less important and can be an indicator of poor adjustment in older age groups. They found that POM was positively related to SWB across life stages but SFM was more strongly related to low SWB in later life. This suggests that age may moderate the relationship between meaning in life (particularly SFM) and SWB. We explore this possibility in this research.
Males and females may have different view towards their life meaning and SWB, which may imply that gender may serve as a potential moderator in the relationship between life meaning and SWB. For instance, prior research has found that compared to males, females report higher levels of POM and SFM (Steger et al. 2009a, b). In addition, previous research has documented that women show stronger positive and negative affect than men (for a review, see Diener et al. 1999). Therefore, we are curious about whether the links between POM/SFM and SWB differ between males and females. In this study we also examine whether participant gender moderates the “POM/SFM–SWB” association.
Finally, we explore a methodological factor: study design (whether the data is cross-sectional or longitudinal) to assess whether it acts as a moderator. In cross-sectional design, the covariance between the independent variable (in this case, POM and SFM) and the dependent variable (in this case, SWB) may be inflated because participants answered the questionnaires at the same time and with the same mood. Thus, correlation coefficients from cross-sectional design studies will likely be stronger than those from longitudinal design studies.
This meta-analysis was set up following the guidelines of the PRISMA checklist (Moher et al. 2015). To facilitate scientific transparency, we have made a coding sheet that contains effect sizes and moderating variables available online.
Search of Studies
We searched, and retrieved articles from four databases (Education Resources Information Center, PsychINFO, PubMed, and Web of Science) up to June 15, 2018. Although the current study focuses on the relationship between POM/SFM and SWB, we used only the keywords of meaning in life because research on meaning in life should already include studies about the relationship between meaning in life and SWB. The following key words were used: meaning in life OR life meaning OR search for meaning OR presence of meaning OR meaning in life questionnaire.
We considered studies eligible for inclusion when they fulfilled the following criteria. First, the study had to measure the relationship between (either or both) POM/SFM and any component of SWB. Specifically, it had to report at least one correlation between POM/SFM and SWB. If no correlation was reported in the article, we requested them from the corresponding author.
Second, the study had to use Steger et al.’s (2006) Meaning in Life Questionnaire (MLQ) to measure POM and SFM. We are aware that the literature includes a number of scales for measuring life meaning, but few were developed to assess the extent to which individuals experience and search for meaning. In contrast, the MLQ assesses POM and SFM simultaneously, which leads to more easily comparable results across studies. For this reason, we focused only on studies that utilized the MLQ as the measure of life meaning. As the study that had developed the MLQ was accepted by a journal in 2005 and published in an issue in 2006, the range of years was limited to 2005 to present correspondingly.
Third, the study or studies had to be published in English in a peer-reviewed journal with downloadable full-text. Unpublished work, review articles, book chapters, dissertations and conference abstracts were not included because these outputs are often subsequently published in peer-reviewed journals. Also, research has found that publication bias is just as likely or unlikely whether or not meta-analyses include unpublished studies (Ferguson and Brannick 2012).
Procedure of Selection
As shown in Fig. 1, an initial search of the databases yielded 2547 hits after removal of duplicates. The first two authors screened all titles and abstracts and selected articles eligible for full text coding. This resulted in 468 potentially relevant studies. A number of studies were excluded after being screened with regard to whether or not they met the inclusion criteria. As such, 327 studies were excluded because they were off topic (k = 109), did not use the MLQ (k = 112), were not empirical articles (k = 14), were not published in English (k = 2), did not have full-text available (k = 15), did not include SWB measure (k = 35), or did not include correlation information (k = 40). With regard to the latter 40 studies, we contacted the authors by e-mail to request additional information. Six authors provided additional useful information (15% valid responding rate), therefore 147 studies met the selection criteria and were included in the meta-analysis.
Coding of the Studies
We developed an extensive coding scheme based on the guidelines proposed by Lipsey and Wilson (2001). The coding scheme was used to record a wide range of information from the studies, including authors’ names, years of publication, sample size, study design, effect sizes, and so on. Moderators were also coded as either continuous or categorical variables, as stated below. Two research assistants independently coded the articles after 20 h of training from the first author. To ensure inter-rater reliability, both assistants coded 28 articles and their coding was compared, with categorical coding being tested using Cohen’s Kappa and continuous coding tested using intra-class correlation coefficient (ICC). Except in one case of participant distress (Kappa = 0.882, p < .001), all other coding indicated near-to-perfect agreement (all Kappa/ICC > .990, p < .001). Authors of the study discussed with the two coders to solve any disagreements.
Components of SWB We not only examine the overall relationships between POM/SFM and SWB as an overall construct, but we also examine if SWB component moderates the overall associations by treating SWB as a categorical moderator. The four components of SWB were coded as categorical moderators: 1 = overall life satisfaction, 2 = positive affect, 3 = negative affect and 4 = domain specific satisfaction. In this way, we can examine if the association between POM/SFM and a specific component of SWB differs from each other.
Participant distress Participants’ distress was coded categorically. Specifically, we coded whether participants were in either mental distress (e.g., PTSD, bereavement, etc.) or physical distress (e.g., physical illness) or in a non-distressed state. We coded participants not in distress as “1 = non-distressed” and those in distress as “2 = distressed”.
Individualism-collectivism We coded individualism-collectivism as a continuous moderator. Specifically, we coded the level of individualism of the country from which the data was collected according to Hofstede’s individualism index (see www.hofstede-insights.com). A higher score indicates societies that are more individualistic (e.g., an index of 91 for the U.S.) and a lower score indicates societies that are more collectivistic (e.g., an index of 20 for China).
Participant age We coded participants’ age as a continuous moderator.
Participant gender Participant gender was also coded as a continuous moderator, based on the proportion of females included in the sample.
Study design We coded study design as a categorical moderator: 1 = cross-sectional design and 2 = longitudinal design.
Pearson’s correlation coefficient r is used as effect size in meta-analyses aiming to summarize associations between two constructs (Lipsey and Wilson 2001). This information was extracted from the text of the included studies, or requested from authors if no such information was provided in the full-text. To obtain an accurate estimation of mean effect size and an unbiased test of statistical significance, we used Fisher’s r to z transformation to convert the r into an ESZ score to correct for skewness in the sampling distribution of r (Lipsey and Wilson 2001). We then transformed the ESZ score back to r for interpretation purposes (Field 2001; Lipsey and Wilson 2001).
Many studies have reported multiple effect sizes in one article. It is likely that different effect sizes from the same study are more similar than those from different studies because they are drawn from the same sample and dataset. The assumption that observations are independent, and error terms are uncorrelated, is violated when nested data is used (Lipsy and Wilson 2001). Such a dependency problem needs to be compensated for to reduce the bias of estimates (Hox et al. 2010). Traditional strategies to deal with this problem include selecting one effect size from each study, averaging effect sizes within studies, or simply ignoring the problem (Lipsy and Wilson 2001). In recent years, multilevel meta-analysis has been suggested as a preferable tool to solve this problem because it takes dependency into account while including all effect sizes to maximize statistical power (Assink and Wibbelink 2016; Hox et al. 2010; Van den Noortgate et al. 2013). Thus, it allows all effect sizes from the same study to be used and removes the need to eliminate any. Given the advantage of this method, we applied a multilevel meta-analysis technique to deal with the dependency problem.
In this study, we applied the funnel plot to handle any publication bias problem. Begg (1994) has suggested that an asymmetrical funnel plot would appear if publication bias affected the data. We also employed Egger’s test (Egger et al. 1997) to examine the statistical significance of the asymmetry of the plot. If there is no significance, further analyses stop. If there is significant asymmetry, we continue using the trim and fill method to adjust the asymmetric plots by imputing missing effect sizes (Duval and Tweedie 2000a, b).
All analyses were performed in the Metafor package (Viechtbauer 2010) in R version 3.4.2 (R Core Team 2017). A multilevel model was used to decompose variance in effect sizes from three sources: (1) level-1 takes sampling variance into account, (2) level-2 takes variance of effect sizes of the same study into account allowing effect sizes that vary within the study, and (3) level-3 takes variance of effects sizes of different studies into account allowing effect sizes that vary between studies (Assink and Wibbelink 2016; Hox et al. 2010). Overall, multilevel modeling allows us to include effect sizes based on the same sample and dataset, and provides more precision in estimating mean effect sizes while simultaneously modeling the nestedness of the data (Cheung 2014; Van den Noortgate et al. 2013). Based on the multilevel model, statistical analyses were carried out in several steps. First, we estimated the overall effect size of the association between POM/SFM and SWB. Second, we used a likelihood ratio test (i.e., LRT) to examine the significance of within- and between-study heterogeneity, which allowed us to handle the dependency problem. Third, if there was significant heterogeneity, we continued our moderation analyses as long as each category contained at least five studies (otherwise the statistical power is too low to yield accurate estimates, Weisz et al. 2017). For moderation analyses, we first tested the univariate effect of the moderator; and then fitted a model that included all moderators with significant univariate effects to reduce multicollinearity (Hox et al. 2010).
The current meta-analysis included 147 studies comprising 726 effect sizes. The overall reported sample sizes was N = 92,169 (about 60.24% of females), ranging from 30 to 14,385. Of these, sample size for POM studies was 88,868 and the one for SFM studies was 57,952, with 54,651 overlapping participants who completed both POM and SFM measures. The reported age ranged from 14.47 to 84.25 years old, with a mean age of 31.04 years old. These effect sizes were from studies conducted in numerous countries, with Hofstede’s individualism score ranging from 18 (Korea) to 91 (United States). Regarding indicators of meaning in life, 441 effect sizes were extracted for POM (about 60.74% of total effect sizes) whereas SFM consisted of 285 effect sizes (about 39.20% of total effect sizes). As for SWB components, 76 studies examined only one component of SWB, 48 studies examined two components, 23 studies examine three components and 1 study examined all components. In addition, studies reported 193 effect sizes for life satisfaction (about 26.58% of total effect sizes), 157 for positive affect (about 21.63% of total effect sizes), 336 for negative affect (about 46.28% of total effect sizes), and 40 for domain-specific satisfaction (about 5.51% of total effect sizes). In addition, 689 effect sizes were extracted from cross-sectional study (94.90%) and only about 5.10% of effect sizes were from longitudinal research. Finally, 675 effect sizes (about 93.00% of total effect sizes) were found among participants without explicitly reporting any physical or psychological distress and only 51 effects sizes (about 7.02% of total effect sizes) were extracted from participants explicitly reporting any physical or psychological distress.
Relationship Between Presence of Meaning and Subjective Well-Being
Overall effect The overall effect size of the association between POM and SWB was significant, ESz = 0.418, S.E. = 0.014, t = 29.608, p < .001, 95% CI [0.390, 0.446], with substantial heterogeneity (QE (440) = 5910,669, p < .001). The overall effect size was transformed back to Pearson r for interpretation purpose based on the inverse version of the Fisher’s (1921) r-to-z formula. We found that the association was about 0.395.
Variance of the overall effect The variance at the within-study level (estimate = 0.017, p < .001) and the between-study level (estimate = 0.018, p < .001) were significant. Results of follow-up analyses found that variance at the sampling, within-study, and between-study level was 5.63%, 45.96%, and 48.41%, respectively. According to the criteria suggested by Hunter and Schmidt (1990), it would be fruitful to explore the moderators if less than 75% of the variance is accounted by the sampling level. In the present study, only 5.63% of the variance was explained by the samples, suggesting a continuous investigation of potential moderators is meaningful.
Moderators analyses As shown in Table 1, among the six potential moderators, the moderation effect of SWB components and study design were significant.
Significant moderators Follow-up analysis of main effect and comparison were conducted for each significant moderator found above, and the results are summarized in Table 2. Regarding the SWB components, we found that the effect sizes for the associations between POM and life satisfaction, positive affect, negative affect, and domain-specific satisfaction were all significant. Results of further comparison indicated that the effect size for life satisfaction was larger than that of positive affect, negative affect, and domain-specific satisfaction. In addition, the effect size for positive affect was also larger than that of negative affect and domain-specific satisfaction.
As for study design, the effect sizes for cross-sectional and longitudinal were both significant. Results of follow-up comparison showed that the effect size for cross-sectional design was larger than that for longitudinal design research.
Multiple moderator model A multiple moderator model was fit, with significant moderators found above included. As summarized in Table 3, results of the Omnibus test found a significant result, which suggests that one or more regression coefficients of the moderators deviated from zero significantly. Such findings revealed that positive affect (vs. life satisfaction), negative affect (vs. life satisfaction), domain-specific satisfaction (vs. life satisfaction), and longitudinal design (vs. cross-sectional design) had unique moderating effects on the association between POM and SWB.
Publication bias Results of Egger’s regression showed that there was no significant asymmetry, z = − 0.220, p = .826. This suggests that there is no indication of publication bias for the association between POM and SWB.
Relationship Between Search for Meaning and Subjective Well-Being
Overall effect The overall effect size of the association between SFM and SWB was significant but lower than small effect, ESz = − 0.121, S.E. = 0.017, t = − 7.013, p < .001, 95% CI [− 0.155, − 0.087], with substantial heterogeneity (QE (284) = 3930.189, p < .001). We applied the inverse version of the Fisher’s (1921) r-to-z formula to transform this overall effect size back to Pearson r and found that the association was about − .120.
Variance of the overall effect The variance at the within-study level (estimate = 0.008, p < .001) and the between-study level (estimate = 0.020, p < .001) were significant. Results of follow-up analyses found that variance at the sampling, within-study, and between-study level was 6.76%, 26.30%, and 66.94%, respectively. In the present study, only 6.76% of the variance was explained by the samples, suggesting a continuous investigation of potential moderators is meaningful.
Moderators analyses As shown in Table 4, among the six potential moderators, SWB components, study design, participant age, and Hofstede’s individualism index were significant.
Significant moderators Follow-up analysis of main effect and comparison were conducted for each significant moderator found above, and the results are summarized in Table 5. Regarding the SWB components, we found that the effect sizes for the associations between SFM and life satisfaction and the one between SFM and negative affect were significant, whereas the effect sizes for the relationship between SFM and positive affect and the relationship between SFM and domain-specific satisfaction were not significant. Results of further comparison indicated that the effect size for negative affect was larger than that for life satisfaction and positive affect, and that the effect size for life satisfaction was larger than that for positive affect.
As for study design, the effect size for cross-sectional was significant whereas the one for longitudinal studies was not significant. Results of follow-up comparison showed that the effect size for cross-sectional design was larger than that for longitudinal design research.
With respect to participant age, the intercept was significant. Moreover, the slope of age was also found significant. Since the regression coefficient of age is positive, it suggests that the older of participants, the stronger the effect size is.
Finally, for Hofstede’s individualism index, the intercept was not significant. Nevertheless, the slope of Hofstede’s individualism index was found significant. Since the regression coefficient is negative, it suggests that the lower Hofstede’s individualism index, the stronger the effect size is.
Multiple moderator model A multiple moderator model was fit, with significant moderators found above included. As summarized in Table 6, results of the Omnibus test found a significant result, which suggests that one or more regression coefficients of the moderators deviated from zero significantly. Such findings revealed that positive affect (vs. life satisfaction), negative affect (vs. life satisfaction), participant age and Hofstede’s individualism index had unique moderating effects on the association between SFM and SWB.
Publication bias Results of Egger’s regression showed that there was significant asymmetry, z = − 2.023, p = .043. This suggests that there is indication of publication bias for the association between SFM and SWB. Subsequently, a trim-and-fill approach was conducted to impute missing studies on the right hand side (Fig. 2) and to re-calculate the overall effect size, resulting in an adjusted effect size of ESz = – 0.073, S.E. = 0.011, z = − 6.645, p < .001, 95% CI [− 0.095, − 0.051], Pearson r = − .073.
Psychologists have long striven to understand why some people have a greater feeling of well-being than others. Early researchers assumed that individual differences in SWB are affected by social indicators (e.g., whether people are young, healthy, well-educated, well-paid, married persons with good self-esteem, etc., Wilson 1967). Later SWB researchers have revealed that heredity and psychological factors (e.g., personality) play a more important role in contributing to SWB than object life conditions (DeNeve and Cooper 1998; Diener et al. 1999; Lykken 1999). Among numerous psychological factors, experience of a meaningful life is found to be a crucial predictor of SWB. Knowledge concerning the impact of meaning in life on SWB has progressed greatly in the past decade based on the application of a dual-dimension structure of meaning in life (i.e., POM and SFM). However, inconsistent findings across the literature (particularly regarding SFM) have invoked skepticism as to whether meaning in life is truly important to SWB. The current three-level meta-analysis hopes to summarize the overall magnitude of the relationships between POM/SFM and SWB and to explore potential moderators, to contribute more conclusive evidence to this debate. Several significant findings have been generated and are commented on, point by point, below.
The Overall Association Between POM/SFM and SWB
The association between POM and SWB exhibits a close to medium effect size (i.e., 0.418) according to Cohen’s (1992) standard. The Pearson r transformed back from the effect size was .395, suggesting that POM explains an approximate 16% variance of SWB. These findings indicate that possession of a clear and stable life meaning is associated with more SWB. The effect size for the relationship between SFM and SWB is − 0.121, which is lower than the small effect size (i.e., .20) according to Cohen’s (1992) standard. The Pearson r transformed back from the effect size was − .120, suggesting that SFM explains an approximate 1.44% variance in SWB. These results suggest that extensively seeking life meaning in general is adverse to SWB since the effect size is significant. However, it is notable that the effect size is small (even lower than the small size according to Cohen’s standard), suggesting that SFM seems to lie in the middle zone of the debate regarding whether it is beneficial to one’s growth (e.g., Frankl 1963; Park 2010; Vohs et al. 2019) or harmful to psychological function (e.g., Baumeister 1991; Klinger 1998). This finding results from the fact that a number of studies have found a positive relationship between SFM and SWB and that ample studies also reveal a negative association. Such inconsistencies highlight the needs to consider possible factors that moderate this link.
The Moderation of SWB Components
POM is significantly related to every SWB component, but the effect size for the “POM—life satisfaction” association is stronger than the relationship between POM and other SWB components. As previously mentioned, both life meaning and life satisfaction are organized, stored and evaluated in the cognitive system (Diener 2000; Diener et al. 1999; Heine et al. 2006). Such a stronger effect size may partly exist because assessments of life meaning and life satisfaction substantially overlap in the cognitive system; and partly because individuals with a greater sense of meaning maintain that sense by strategically evaluating their life as more satisfactory, according to the meaning-maintenance model (Heine et al. 2006). It is worthwhile noting that the effect sizes for the “POM—the affective components of SWB” are relatively smaller than the one for the “POM—life satisfaction” link. Existing literature has suggested that besides life meaning, other situational (e.g., daily pleasant/unpleasant events) and demographic factors (e.g., age, sex, education)—factors that have also been related to POM (e.g., Steger et al. 2006, 2009)—are associated with the affective components of SWB (Diener et al. 1999). This implies that the relatively smaller effect sizes for the “POM—affective components of SWB” associations could be (partially) due to the confounding effect of the situational and demographic factors that account for both the POM and the affective components of SWB.
SFM is significantly associated with negative affect and life satisfaction but not with positive affect and domain-specific satisfaction in general. The effect size for the “SFM—negative affect” association is stronger. These findings suggest that SFM is not equivalently adverse to every SWB component, but that it does seem particularly relevant to negative affect. There may be two explanations. On one hand, individuals may actively seek life meaning because their needs are frustrated (Baumeister 1991; Klinger 1998, 2000), suggesting that the search for meaning is linked with negative emotions (e.g., frustration). The process of searching for meaning entails uncertainty and stress (Kiang and Fuligni 2010), which directly increase feelings of negative affect. On the other hand, dealing with uncertainty and stress may reduce one’s ability to control one’s emotions (Duckworth, Kim, and Tsukayama 2013; Metcalfe and Mischel 1999), a crucial psychological function to regulate ongoing and potentially negative emotions in the process of searching for meaning. This may further exacerbate the influence of SFM on negative affect.
The Moderation of Age
Age does not moderate the overall effect for the “POM–SWB” association but it does moderate the one for the “SFM–SWB” association. Our results showed that as participants age, the overall association between SFM and SWB becomes stronger. In the existing literature, there are two views regarding the association between POM and SWB and the one between SFM and SWB. First, some scholars consider that a sense that one’s life is meaningful is crucial across the life span (Wong 2000), implying that the importance of POM to SWB persists across the life span. Second, some developmental theories expect that although SFM appears related to less SWB in older adulthood, SFM seems normative and even adaptive for younger people, who spend considerable resources to explore their self-identity (Erikson 1968). Steger et al. (2009a, Steger, Oishi, et al. 2009) have confirmed these views by showing that no significant difference in the magnitude of the “POM–SWB” association among different age groups but that the “SFM–SWB” association appears stronger in older adults.
The present findings, based on more comprehensive and representative data and statistical techniques, further confirm the pattern that the association between POM and SWB varies little across life span but that the impact of SFM on SWB appears greater for older participants. These results suggest that POM seems particularly important in older adulthood to restore life balance and facilitate successful aging as people change roles, face declining physical capacity, or encounter a greater number of interpersonal losses. Moreover, a stronger association between SFM and SWB in older participants seems to also imply that older participants who do not adjust to the losses of older age through SOC (Selection, Optimization, Compensation, Baltes and Baltes 1990), or are not capable to create an equilibrium between assimilation and accommodation maneuvers (Whitbourne 1986), or adjust positive views on their aging process (Westerhof et al. 2014) are continuing to search for meaning and cannot create the balance required for successful aging.
The Moderation of Hofstede’s Individualism Index
The dialectic model of meaning in life (Steger et al. 2008c) suggests that the association between POM and SWB should be positive and stronger for participants from individualistic countries and the association between SFM and SWB should be negative for participants from individualistic countries and positive for those from collectivistic countries. Our results roughly support these assumptions. POM indeed shows a positive association with SWB and there is a trend that such an association appears stronger for participants from individualistic countries, as evidenced by the fact that the overall effect size was marginally moderated by Hofstede’s individualism index (p = .057, Table 1) although not so much at the conventional significance level. In addition, a negative regression coefficient for the “SFM-SWB” relationship suggests that SFM is related to more positive changes in SWB as Hofstede’s individualism index becomes lower (i.e., represents the country as more collectivistic). These results support the dialectic theory of meaning in life that people in more individualistic societies appear to see possession of POM as enhancing their self-image and that this relates strongly to SWB. Conversely, those in more collectivist societies consider search for meaning a way to improve themselves, and thus SFM may have a positive association with their SWB.
The Moderation of Study Design
Study design also moderates the overall effect sizes. Effect sizes obtained from cross-sectional studies are larger than those from longitudinal studies. This is not surprising given that cross-sectional design allows participants to answer questionnaires in the same mood, which may increase common shared variance. Nevertheless, it should be noted that only a few effect sizes (about 5.1% of the total effect sizes included in the analysis) were extracted from longitudinal studies, so the results are likely to be unstable. Regarding the “SFM–SWB” association, we found that effect size was significant only for cross-sectional research. SFM is a dynamic process and individuals may continue or discontinue seeking meaning within a given period. Therefore, understanding the association between SFM and SWB in a longitudinal study may require more sophisticated research design to capture the window of time during which individuals start, continue, and cease seeking meaning in life, to explore the influences of these processes on various outcomes.
This study has two theoretical implications. First, it has implications for the ongoing debate about whether meaning in life, SFM in particular, is important to SWB (e.g., Baumeister 1991; Frankl 1969; Klinger 1998; Park 2010; Vohs et al. 2019). Our findings suggest that possessing a clear and stable life meaning is indeed substantially important to SWB, while the overall association between SFM and SWB is small and complex. While we are confident that having life meaning is a pivotal source of personal SWB, it is premature to close the debate on the function of SFM in personal SWB. We believe it would be wise and fruitful to continue this debate by considering a number of conditional variables. Moderation analysis of study design suggests that individuals searching for life meaning concurrently report lower SWB but that the association becomes insignificant in the long-run. This implies a short-term reduction of SWB in the process of seeking life meaning, but also that over the long term SFM may not necessarily impair SWB. It is desirable to establish a unified theory regarding the association between POM, SFM, and SWB.
Second, the dialectic model of meaning in life proposes several tenets, one of which is that the function of POM and SFM in SWB may differ across independent and interdependent cultures (Steger et al. 2008c). Although many studies have investigated the relation between POM/SFM and SWB separately, direct discussion of this model is sparse. The current findings bear implications to the dialectic model. On one hand, the moderation effect of Hofstede’s individualism index in the relationship between POM and SWB is only marginally significant, which imply, but not uniformly conclude, that POM appears to be more important for individuals from individualistic cultures in orienting their SWB. On the other hand, our results suggest that SFM plays a special role in positive changes in SWB among individuals from collectivistic cultures. In sum, this model has merit as a theoretical framework for discussing cultural differences in the function of POM/SFM in individuals’ SWB.
In clinical practice, practitioners do not just address disturbances and disorders that ail the client; within the contemporary movement of positive psychology they also facilitate clients’ senses of meaning in life to promote their growth and recovery (Lent 2004). Before we can develop and apply meaning-oriented therapy, we must provide a basic scientific understanding of the relationship of the two constructs. Our findings suggest that assisting clients to experience life meaning is important to helping them thrive. However, therapies that aim to help clients thrive by inviting them to make meaning out of trauma and stressful events need thorough consideration. We should be wary of the negative affect that the therapeutic processes may bring, at least in the short term. Two approaches may circumvent this. The first is that we should have strategies/treatments in place to ease negative emotions that arise in the process of seeking life meaning. The second is that we should consider influencing the mediators and moderators in the “SFM–SWB” relationship. For example, one recent study has found self-control to be a mediator that partly explains how POM and SFM influence psychological distress, including negative affect (Li et al. 2019). The moderation analyses also imply that such circumventing approaches may be particularly needed for older clients and clients from individualistic cultures.
We wish to acknowledge some limitations. First, the number of effect sizes for some moderation analyses are low (e.g., participants with distress, domain-specific satisfaction, etc.). For instance, only 5.1% effect sizes were extracted from longitudinal studies, which may reduce the statistical power and make the findings less comparable. This problem can be solved by further updating the results as more effect sizes are included. To facilitate this, the data included in this meta-analysis is openly accessible. Scholars are free to use it to perform updated meta-analysis. Second, to make the results more comparable, this research includes only studies that have used Steger et al.’s (2006) Meaning in Life Questionnaire to measure POM and SFM. We are aware that this is also a flaw. We suggest that current results be seen as preliminary but not definitive. If interested, scholars may compare the effect sizes obtained from studies using other measures with our effect sizes for even more comprehensive results. Last, we have found significant publication bias in the “SFM–SWB” association, which implies that findings from these studies may be biased.
A sense of meaning in life has long been considered important to one’s personal well-being. This topic has gained increased attention in the current millennium, particularly since the development of a psychometrically sound, easy-to-use, and popular measure, the MLQ (Steger et al. 2006). The MLQ has helped us understand the differential roles of POM and SFM in personal SWB. Although there are inconsistent findings across the literature, this meta-analytic study concludes that the experience of greater life meaning is robustly associated with higher SWB; whereas the association between seeking life meaning and SWB is small on average and, to some degree, conditional. A number of other moderators need to be considered when investigating the “SFM–SWB” relationship. We believe these conclusions have relevance not only for the further study of the role of the POM and SFM in the individual’s sense of well-being, but also for the practical counselling of individuals facing psychological distress.
The data associated with this research are available in the main text and supplemental file.
Full list of references included in the meta-analysis are deposited as online supplementary
Andrews, F. M., & Withey, S. B. (1976). Social indicators of well-being. New York: Plenum Press.
Assink, M., & Wibbelink, C. J. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12, 154–174. https://doi.org/10.20982/tqmp.12.3.p154.
Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 11–34). Cambridge: Cambridge University Press.
Baumeister, R. F. (1991). Meanings of life. New York: Guilford.
Baumeister, R. F., & Vohs, K. D. (2002). The pursuit of meaningfulness in life. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 608–618). New York, NY: Oxford University Press.
Begg, C. B. (1994). Publication bias. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 399–409). New York, NY: Russell Sage Foundation.
Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1999). The affect system has parallel and integrative processing components: Form follows function. Journal of Personality and Social Psychology, 76, 839–855. https://doi.org/10.1037/0022-35126.96.36.1999
Cheung, M. W. L. (2014). Modeling dependent effect sizes with three-level meta-analyses: Astructural equation modeling approach. Psychological Methods, 19, 211–229. https://doi.org/10.1037/a0032968.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. https://doi.org/10.1037/0033-2909.112.1.155.
DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124, 197–229. https://doi.org/10.1037/0033-2909.124.2.197.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55, 34–43. https://doi.org/10.1037/0003-066X.55.1.34.
Diener, E. D., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75. https://doi.org/10.1207/s15327752jpa4901_13.
Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125, 276–302. https://doi.org/10.1037/0033-2909.125.2.276.
Duckworth, A. L., Kim, B., & Tsukayama, E. (2013). Life stress impairs self-control in early adolescence. Frontiers in Psychology, 3, 608. https://doi.org/10.3389/fpsyg.2012.00608.
Duval, S., & Tweedie, R. (2000a). A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95, 89–98. https://doi.org/10.1080/01621459.2000.10473905.
Duval, S., & Tweedie, R. (2000b). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56, 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x.
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629.
Erikson, E. H. (1968). Identity: Youth, and crisis. New York: Norton.
Ferguson, C. J., & Brannick, M. T. (2012). Publication bias in psychological science: Prevalence, methods for identifying and controlling, and implications for the use of meta-analyses. Psychological Methods, 17, 120–128. https://doi.org/10.1037/a0024445.
Field, A. P. (2001). Meta-analysis of correlation coefficients: A Monte Carlo comparison offixed-and random-effects methods. Psychological Methods, 6, 161–180. https://doi.org/10.1037/1082-989X.6.2.161.
Frankl, V. E. (1963). Man’s search for meaning: An introduction to logotherapy. New York: Washington Square Press.
Heine, S. J., Proulx, T., & Vohs, K. D. (2006). The meaning maintenance model: On the coherence of social motivations. Personality and Social Psychology Review, 10, 88–110. https://doi.org/10.1207/s15327957pspr1002_1.
Hofmann, W., Luhmann, M., Fisher, R. R., Vohs, K. D., & Baumeister, R. F. (2014). Yes, but are they happy? Effects of trait self-control on affective well-being and life satisfaction. Journal of Personality, 82, 265–277. https://doi.org/10.1111/jopy.12050.
Hox, J. J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications. Abingdon: Routledge.
Hunter, J. E., & Schmidt, F. L. (1990). Methods of meta-analysis: Correcting error and bias inresearch 1ndings. Newbury Park, CA: Sage.
Joseph, S., & Linley, P. A. (2005). Positive adjustment to threatening events: An organismic valuing theory of growth through adversity. Review of General Psychology, 9, 262–280. https://doi.org/10.1037/1089-26188.8.131.522.
Klinger, E. (1998). The search for meaning in evolutionary perspective and its clinical implications. In P. T. P. Wong & P. S. Fry (Eds.), The human quest for meaning: A handbook of psychological research and clinical application (pp. 27–50). Mahwah, NJ: Lawrence Erlbaum Associates.
Klinger, E. (2008). The search for meaning in evolutionary perspective and its clinical implications. In P. T. P. Wong & P. S. Fry (Eds.), The human quest for meaning: A handbook of psychological research and clinical applications (2nd ed.). Mahwah, NJ: Erlbaum.
Lent, R. W. (2004). Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment. Journal of Counseling Psychology, 51, 482–509. https://doi.org/10.1037/0022-0184.108.40.2062.
Li, J. B., Delvecchio, E., Lis, A., Nie, Y. G., & Di Riso, D. (2016). Positive coping as mediator between self-control and life satisfaction: Evidence from two Chinese samples. Personality and Individual Differences, 97, 130–133. https://doi.org/10.1016/j.paid.2016.03.042.
Li, J. B., Salcuni, S., & Delvecchio, E. (2019). Meaning in life, self-control and psychological distress among adolescents: A cross-national study. Psychiatry Research, 272, 122–129. https://doi.org/10.1016/j.psychres.2018.12.033
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, California: Sage.
Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the beck depression and anxiety inventories. Behaviour Research and Therapy, 33, 335–343. https://doi.org/10.1016/0005-7967(94)00075-U.
Lykken, D. (1999). Happiness: What studies on twins show us about nature, nurture, and the happiness set-point. New York, NY: Golden Books.
Lyubomirsky, S., & Lepper, H. S. (1999). A measure of subjective happiness: Preliminary reliability and construct validation. Social Indicators Research, 46, 137–155. https://doi.org/10.1023/A:1006824100041.
MacKenzie, M. J., & Baumeister, R. F. (2014). Meaning in life: Nature, needs, and myths. In A. Batthyany & P. Russo-Netzer (Eds.), Meaning in positive and existential psychology (pp. 25–37). New York: Springer. https://doi.org/10.1007/978-1-4939-0308-5_2.
Metcalfe, J., & Mischel, W. (1999). A hot/cool-system analysis of delay of gratification: dynamics of willpower. Psychological Review, 106, 3–19. https://doi.org/10.1037/0033-295X.106.1.3.
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., et al. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4, 1. https://doi.org/10.1186/2046-4053-4-1.
Park, C. L. (2010). Making sense of the meaning literature: An integrative review of meaning making and its effects on adjustment to stressful life events. Psychological Bulletin, 136, 257–301. https://doi.org/10.1037/a0018301.
R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/
Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2013). Three-level meta-analysis of dependent effect sizes. Behavior Research Methods, 45, 576–594. https://doi.org/10.3758/s13428-012-0261-6.
Van Saane, N., Sluiter, J. K., Verbeek, J. H. A. M., & Frings-Dresen, M. H. W. (2003). Reliability and validity of instruments measuring job satisfaction: A systematic review. Occupational Medicine, 53, 191–200. https://doi.org/10.1093/occmed/kqg038.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36, 1–48. https://doi.org/10.18637/jss.v036.i03.
Vohs, K. D., Aaker, J. L., & Catapano, R. (2019). It’s not going to be that fun: Negative experiences can add meaning to life. Current Opinion in Psychology, 26, 11–14. https://doi.org/10.1016/j.copsyc.2018.04.014.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. https://doi.org/10.1037/0022-35220.127.116.113.
Weise, C. W., Tay, L., Duckworth, A. L., D’Mello, S., Kuykendall, L., Hofmann, W., et al. (2018). Too much a good thing? Exploring the inverted-U relationship between self-control and happiness. Journal of Personality, 86, 380–396. https://doi.org/10.1111/jopy.12322.
Weisz, J. R., Kuppens, S., Ng, M. Y., Eckshtain, D., Ugueto, A. M., Vaughn-Coaxum, R., et al. (2017). What five decades of research tells us about the effects of youth psychological therapy: A multilevel meta-analysis and implications for science and practice. American Psychologists, 72, 79–117. https://doi.org/10.1037/a0040360.
Westerhof, G. J., Miche, M., Brothers, A. F., Barrett, A. E., Diehl, M., Montepare, J. M., et al. (2014). The influence of subjective aging on health and longevity: A meta-analysis of longitudinal data. Psychology and Aging, 29, 793–802. https://doi.org/10.1037/a0038016
Whitbourne, S. K. (1986). Openness to experience, identity flexibility, and life change in adults. Journal of Personality and Social Psychology, 50, 163–168. https://doi.org/10.1037//0022-3518.104.22.168
Wilson, W. (1967). Correlates of avowed happiness. Psychological Bulletin, 67, 294–306. https://doi.org/10.1037/h0024431.
Wong, P. T. P. (2000). Meaning in life and meaning in death in successful aging. In A. Tomer (Ed.), Death attitudes and older adults: Theories, concepts, and application (pp. 23–35). Philadelphia: Taylor and Francis.
The funding was provided by MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Grant No. 17YJCZH040), National Natural Science Foundation of China (Grant No. 31800938), Natural Science Foundation of Guangdong Province (Grant No. 2018A030313406) and Guangzhou University’s training program for excellent new-recruited doctors (Grant No. YB201707)
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Li, JB., Dou, K. & Liang, Y. The Relationship Between Presence of Meaning, Search for Meaning, and Subjective Well-Being: A Three-Level Meta-Analysis Based on the Meaning in Life Questionnaire. J Happiness Stud 22, 467–489 (2021). https://doi.org/10.1007/s10902-020-00230-y
- Meaning in life
- Life satisfaction, positive affect
- Negative affect
- Domain-specific satisfaction