Growth mindset of meaning in Life: Viewing meaning in life as malleable matters

Abstract

Meaning in life contributes to psychological well-being. However, few studies have adopted an implicit theory approach to studying people's beliefs about the malleability of meaning in life. We propose the growth mindset of meaning in life (GMML) as the belief that meaning in life can be developed. In Study 1, we construct the Mindset of Meaning in Life Scale (MMLS) to measure GMML and demonstrate that GMML is distinct from growth mindset of intelligence and meaning in life. As predicted, GMML is positively associated with psychological well-being indicators, including life satisfaction and positive coping styles, and negatively correlated with ill-being measures such as depression and anxiety. In Study 2, tolerance of uncertainty was found to partially mediate the relationships between GMML and depression, anxiety, purpose in life, the presence of and search for meaning in life. Taken together, our findings suggest that GMML is a distinct construct relevant to individuals’ well-being and meaning in life.

Keywords

growth mindset of meaning in life meaning in life lay theories well-being tolerance of uncertainty Mindset of Meaning in Life Scale

Meaning in life, as a hallmark of psychological well-being (e.g., Heintzelman & King, 2014, Steger, 2009), predicts individuals’ mental health outcomes (e.g., Zika & Chamberlain, 1992; Steger et al., 2006). A meaningful life has a cognitive facet (it is comprehensible; it makes sense, or has coherence), a motivational facet (it is purposeful; it is directed towards a valued goal), and a evaluative facet (it is significant; it matters in the world) (Martela & Steger, 2016; George & Park, 2016). Among these facets, the cognitive facet is closely related to people's lay theories about meaning in life (George & Park, 2016). These lay theories constrain how people perceive and understand their lives and may have important well-being ramifications. Yet they are rarely studied in the meaning-in-life literature.

People have beliefs in the malleability (vs. immutability) of human attributes, referred to as the growth (vs. fixed) mindset, and these beliefs are closely related to individual achievements (e.g., Dweck, 2008) and mental health outcomes (Schleider et al., 2015). In the present study, we ask and provide answers to two questions: Do people also have beliefs about the malleability of life meaning? Is believing in the malleability of meaning in life linked to psychological well-being?

We propose that some people have the growth mindset of meaning in life (GMML), which refers to the belief that meaning in life can be changed or developed. Following the well-received assumption that mindset is an interpretive sense-making tool (Molden & Dweck, 2006), we propose that when facing uncertainty in life, having the GMML, compared to the beliefthat meaning in life is fixed (FMML), could help individuals construct a more coherent understanding of their life circumstances, thuspositively associated with positive emotions, coping styles, and higher meaning in life.

To test these hypotheses, we first developed the Mindset of Meaning in Life Scale (MMLS), an instrument that measures GMML (vs. FMML; Study 1a) and examined its associations with psychological well-being (Study 1b). We further investigated whether tolerance of uncertainty mediated the relationships between GMML and well-being (Study 2).

The present research makes contributions to two research fields. First, GMML may help clarify why some people have higher meaning in life and have more positive emotions and healthier coping styles than others. Second, the present research extends the mindset research to the domain of meaning in life. Mindset research has examined the growth and fixed mindsets in many domains, including intelligence (Dweck et al., 1995; Hong et al., 1999), personality (Chiu et al., 1997; Dweck 2008), emotion (Tamir et al., 2007), willpower (Job, Dweck, & Walton, 2010), and interest (O’Keefe et al., 2018). Having a malleable view in one domain does not necessarily ensure having a malleable view in another (e.g., Dweck et al., 1995; Schroder et al., 2017; Zhu et al., 2020). Moreover, following the correspondence principle of measurement in mindset research (Chan et al., this issue), it is reasonable to expect strong predictive relationship between GMML and life-meaning-related outcomes.

Meaning in life from a mindset perspective

Finding life meaningful is important for psychological well-being. Research has shown that meaning in life is associated with positive emotions, life satisfaction, and personal growth. In contrast, absence or confusion of life meaning is linked to negative emotions or even mental health symptoms (e.g., Steger et al., 2006; Heintzelman & King, 2014; Taubman-Ben-Ari & Weintroub, 2008). As mentioned above, the construct of meaning in life has a cognitive aspect (comprehension), a motivational aspect (purpose), and an evaluative aspect (significance). Cognitively, people with a meaningful life find their lives to be comprehensible and have a coherent understanding about their life experiences. Motivationally, these individuals have a sense of direction and are motivated towards valued goals. Evaluatively, these individuals find value in their existence or possess a sense of existential mattering (George & Park, 2016; also see Martela & Steger, 2016).

The Meaning in Life Questionnaire (MLQ; Steger et al., 2006) is the mostly widely used measure of meaning in life. Its Presence of Meaning in Life Subscale (hereafter referred to as ML-Presence) measures the global subjective perception that one's life has meaning. Its Search for Meaning in Life Subscale (hereafter referred to as ML-Search) measures the degree of effort invested in establishing and developing life meaning (Steger, et al., 2008a).

People do not just search for meaning in life, they also reflect on their own life meaning search experiences and based on from their experiences form a mental representation of the nature of life meaning (Baumeister, 1991, p. 15). In this reflective inferential process, the nature of life meaning is turned into an object of cognition. Individuals may ask: “Does life meaning evolve or grow from an effortful construction process? Or is it a fixed, hidden meaning meant to be found or not found?” The mental representation about the nature of life meaning, other self-attributes, the world, and their interactions together constitute a meaning system (e.g., Hong et al., 1999), which is organized axiomatically around the mindsets (Molden & Dweck, 2006). Thus, mindsets color how people interpret and anticipate past, present, and future events, and influence their thoughts, emotions, and behaviors (e.g., Dweck, 1999; Dweck, Chiu, & Hong, 1995; Levy et al., 2001).

Thus, theoretically, in the domain of meaning in life, how one constructs the malleable (vs. immutable) nature of meaning in life should be an axiomatic assumption in the meaning system people use to comprehend, select, and evaluate the significance of their life experiences (George & Park, 2016). Wong (1998) proposed to use an implicit theory approach to studying meaning in life, but he only proposed the idea. The present research extends the mindset literature to the new domain of meaning in life and examines its potential well-being implications.

Growth versus fixed mindset of meaning in life

We conceptualize the growth (vs. fixed) mindset about meaning in life as the degree to which an individual believes in the malleability (vs. immutability) and growth potential of meaning in life. We propose that GMML can be distinguished from ML-Presence and ML-Search. The ML-Presence is the overall subjective judgement of the extent to which one's life has meaning, and ML-Search relates to the intensity of effort spent on finding life meaning. In the present research, we treated both ML-Presence and ML-Search as outcomes of the life meaning construction process. GMML and FMML are beliefs about the nature of life meaning that channel the life meaning construction process. Furthermore, given the domain-specific nature of mindsets (Dweck et al., 1995), GMML and FMML, being mindsets about the nature of meaning in life, should be conceptually and empirically different from mindsets in other domains (intelligence).

Proposition 1:

GMML is distinct from ML-Presence, ML-Search, and mindset of intelligence.

Predictability of life events affords meaning in life because predictability renders one's life experiences comprehensible. If there is a lot of uncertainty in life, people may feel that life is unknowable and that meaning in life cannot be found. Under these circumstances, belief about the malleability of life meaning should predict whether one would continue to search for meaning. If people have an FMML, they may read uncertainty in life as a sign that meaning in life does not exist and cannot be found. Thus, they may accept a meaningless life to be an inevitable outcome under life uncertainty and give up the search for life meaning prematurely. In contrast, when people have a GMML, they would read life uncertainty as a sign of the need to broaden and deepen the search in order to find meaning in life. Therefore, in the FMML, life uncertainty engenders premature abortion of life meaning construction search, whereas in the GMML, life uncertainty primes search effort. These differential responses to life uncertainty would increase the likelihood that people with the GMML (vs. FMML) would eventually find meaning in life and be happy and satisfied with their life.

Therefore, we further hypothesize that:

Hypothesis 1a:

GMML would be positively correlated with ML-Presence and ML-Search.

Hypothesis 1b:

GMML would be positively correlated with positive psychological outcomes such as life satisfaction, positive affect, positive coping, and negatively correlated with negative outcomes, such as depression, anxiety, negative affect, and negative coping.

The mediating role of tolerance of uncertainty

Tolerance of uncertainty (TU) is defined as the cognitive tendency to tolerate uncertain situations and events (Dugas, Buhr, & Ladouceur, 2004). This cognitive predilection is positively correlated with positive emotions, life satisfaction, and negatively correlated with negative emotions and anxiety (Garrison, Lee & Ali, 2017). Recent studies showed that TU and meaning in life are protective factors against risks of depression and anxiety during COVID-19 (Korkmaz & Güloğlu, 2021). TU is positively correlated with ML-Presence (Garrison & Lee, 2017; Korkmaz & Güloǧlu, 2021). However, TU's relationship with ML-Search is unclear: a positive relationship was found among Korean college students (Garrison & Lee, 2017) and a negative relationship was found among Turkish adults (Korkmaz & Güloğlu, 2021). TU can strengthen the positive relationship between ML-Search and ML-Presence (Morse et al., 2021). TU also influences how people perceive situational information and how they react to uncertainties. Individuals with lower levels of TU are more likely to retrieve uncertain (vs. neutral) information and are more likely to perceive ambiguous situations as threats (Dugas et al., 2005). College graduates who can tolerate the uncertain periods of job seeking tend to secure better opportunities (see Trevor-Roberts (2006) for a review).

The analysis presented in the previous section already highlights the role of TU as possible mediator of the well-being benefits of GMML. If having GMML primes mastery-oriented coping of uncertain life events and FMML primes a helpless coping, GMML (FMML) should be associated with higher (lower) TU, and TU should mediate the positive (negative) relationships between GMML and meaning in life and other well-being outcomes. Thus, we further hypothesize that:

Hypothesis 2:

GMML would be positively related to TU.

Hypothesis 3:

TU would mediate the relationships between GMML and positive psychological outcomes. Specifically, TU would positively be related to ML-Presence and Purpose in Life (Hypothesis 3a). GMML would be linked to higher TU, which would in turn be associated with higher ML-Presence and Purpose in Life (Hypothesis 3b).

Hypothesis 4:

TU would mediate the relationships between GMML and negative psychological outcomes. Specifically, TU would be negatively related to depression and anxiety (Hypothesis 4a). GMML would be linked to higher TU, which would in turn be associated with lower depression and anxiety (Hypothesis 4b).

Finally, we also hypothesize that TU would mediate the positive relationship between GMML and the search for meaning in life (Hypothesis 5). However, given the mixed relationship between TU and ML-Search, we do not have any directional prediction regarding the relationship between them.

The current studies

We conducted two studies to establish the discriminant validity of GMML as a new construct, and to test the relationship of GMML with psychological well-being, together with the mediating role of TU in the psychological benefits of GMML. In Study 1, we developed and validated the MMLS using exploratory factor analysis (Study 1a) and confirmatory factor analysis (Study 1b). In Study 1b, we evaluated the discriminant and predictive validity of the MMLS, focusing on the predictive relationship of GMML with well-being (life satisfaction, positive and negative affect), emotional (depression and anxiety), behavioral outcomes (coping styles), as well as the presence of and search for meaning in life. In Study 2, to explore the mechanism how GMML may relate to psychological outcomes, we further examined the mediating role of TU in the relationships between GMML and depression, anxiety, purpose in life, ML-presence, and ML-search. Data files for all studies and the Appendix, are available at https://osf.io/28zrs.

Study 1

Study 1 aimed to construct and validate the MMLS. In Study 1a, we generated an item pool, and selected items based on exploratory factor analysis (EFA) results. Using a different sample in Study 1b, we verified GMML's unifactor structure via confirmatory factor analysis (CFA), and sought to establish GMML's discriminant validity by distinguishing it from measures of meaning in life and measures of mindsets in another domain (intelligence) using structural equation modeling (SEM). We also evaluated the association of GMML with ML-Presence and ML-Search, positive and negative affect, well-being outcomes (e.g., depression, anxiety, and life satisfaction), coping styles, and grit.

Study 1a

Method

To construct the MMLS, we first generated 20 items by adapting the extant mindset measures in other domains (e.g., Blackwell et al., 2007; Dweck, 2008; O’Keefe et al., 2018). Next, an expert committee composed of two postdocs and two graduate students in psychology evaluated the items based on the criteria proposed by Dawis (2000), which specified that the wordings of the items need to be clear and specific to the construct. After three rounds of discussions, we removed items with unclear or ambiguous wordings, items that were not specifically related to the malleability of meaning in life, or items that might elicit socially desirable responding. Eight items were retained.

A total of 1,038 participants were recruited via the “Tsinghua Positive Psychology Research Center” official WeChat (an online platform) account. The participants were from 34 provinces and regions across China (80.35% female, age ranged from 10 to 65, M = 38.20, SD = 9.90). We did not collect the participants’ educational background and professional information for this sample. Nonetheless, followers in our WeChat account were mainly public and private school teachers, K12 educators, and parents from the middle- and high-income class. The participants filled out the questionnaire after giving consent and having passed the attention checks. Aside from the GMML items, they also completed the following measures:

Growth mindset of intelligence

Participants reported their level of agreement with the three-item Theory of Intelligence Scale (Dweck, 1999). Sample items include “You have a certain amount of intelligence, and you really can’t do much to change it” and “You can learn new things, but you can’t really change your basic intelligence” (1 = “strongly disagree”; 6 = “strongly agree”; α = .89). The scoring was reversed so that higher scores indicate stronger endorsement of growth mindset of intelligence.

Meaning in life

Meaning in Life was measured using the Meaning in Life Questionnaire – Chinese (MLQ-C). The original scale was developed by Steger et al. (2006) and revised by Wang (2013). This ten-item measure consists of five items measuring ML-Presence (MLQ-P, α = .86) (e.g., “I understand my life's meaning”) and five measuring ML-Search (MLQ-S, α = .88) (e.g., “I am searching for meaning in my life”). Participants responded to each item on a scale from 1 (“absolutely untrue”) to 7 (“absolutely true”).

Depression and anxiety

Depression and anxiety were measured by five items that assess symptoms of depression in the Patient-Reported Outcome Measurement Information System (PROMIS) short forms (Varni et al., 2014). The Chinese version had good reliability and validity (Zhao et al., 2019). Sample items are “In the past two weeks, I felt sad” (depression) and “In the past two weeks, I felt nervous” (anxiety). Participants responded to each item on a 5-point Likert scale from 1 (“absolutely disagree”) to 5 (“absolutely agree”). Internal consistency in the present study was good (α = .92 for depression, and α = .91 for anxiety).

Life satisfaction

Satisfaction with life was measured by the five-item Satisfaction with Life Scale (SWLS; Diener et al., 1985). Sample items are “In most ways my life is close to my ideal” and “I am satisfied with my life.” Participants responded to each item on a 7-point Likert scale from 1 (“strongly disagree”) to 7 (“strongly agree”). The Chinese SWLS has been widely used in China and has good reliability and structural validity (see Wang et al., 2016). Internal consistency was good (α = .88) in the present study.

Results and discussion

Item analysis and exploratory factor analysis

The eight GMML items’ item-total correlations ranged from .65 to .76. We performed a principal-component analysis (PCA) on the correlation matrix of the 8 items using STATA 15.1.

The Kaiser–Meyer–Olkin (KMO) measure was .85, higher than .60, the threshold suggested by Kaiser (1974), establishing sampling adequacy. The Bartlett's test of sphericity was significant, $χ^{2} (28) = 3327.48, p < .001$ , indicating factor analysis was appropriate for this sample.

Table 1 presents the means, standard deviations, and the rotated factor loadings based on oblique promax rotation of the EFA results. Scree-plot analysis showed that two components could be extracted, with an eigenvalue of 4.05 and 1.05, respectively. The first factor explained 50.56% of total variance, and second factor explained an additional 13.66%. The first four-item factor captured the belief about the growth potentials of meaning in life. The second four-item factor captured fatalistic beliefs about life meaning, which is an interesting concept by itself but was not the focus of the present study. Therefore, we used the four items from the first factor as our measure of GMML.

Table 1.

EFA rotated factor loadings (oblique promax rotation) of the preliminary mindset of meaning in life scale items (study 1a, N = 1038).

Item	M	SD	Factor1	Factor2
gmml1	5.07	1.02	.77
gmml2	4.95	1.11	.89
gmml5	4.83	1.10	.69
gmml6	5.16	.91	.80
gmml3	4.34	1.45	.37	.44
gmml4	4.42	1.39		.82
gmml7	4.38	1.39		.82
gmml8	3.74	1.55		.81

As shown in Table 2, the GMML was positively correlated with growth mindset of intelligence, ML-Presence, ML-Search, and life satisfaction, and negatively correlated with depression and anxiety in Study 1a. We further tested the items using CFA in Study 1b.

Table 2.

Correlations of growth mindset of meaning in life with other measured variables in all samples.

	Study 1a N = 1,038	Study1b N = 296
Growth mindset of intelligence	.34***	.20***
Meaning in life-Presence	.23***	.10⁺
Meaning in life-Search	.13***	.06⁺
Depression	−.25***	–
Anxiety	−.20***	–
Life satisfaction	.12***	–
Coping styles-Positive	–	.15^**
Coping styles-Negative	–	−.15^**
Positive affect	–	.17^**
Negative affect	–	−.13^*
Grit–Consistency of interest	–	.04⁺
Grit–Perseverance of effort	–	.16^**

Note: ⁺p < .10, *p < .05, **p < .01, ***p < .001.

Study 1b

Method

In Study 1b, we evaluated GMML's discriminant validity by examining its correlation with growth mindset of intelligence. We also evaluated GMML's convergent validity (its correlations with ML-Presence and ML-Search, positive and negative affect, well-being outcomes, coping styles, and grit). The participants were 296 first-year college students (40.54% female) from a reputable university in the Hubei Province. The participants were freshmen aged between 17 and 18.

In addition to the four-item MMLS, Theory of Intelligence Scale, and Meaning in Life Questionnaire, participants also completed the following measures:

Positive and negative affect

Participants’ subjective feelings of well-being and ill-being were measured by the 12-item Scale of Positive and Negative Experience (SPANE, Diener et al., 2010), with six items measuring positive affect (e.g., “happy,” “pleasant”; α = .90) and six items measuring negative affect (e.g., “sad,” “angry”; α = .67). Participants responded to each item on a scale from 1 (“very rarely or never”) to 5 (“very often or always”).

Coping styles

Positive and negative coping styles were measured by the 20-item Coping Styles Questionnaire developed by Xie (1998), which consists of both positive and negative ways of coping. Positive coping style is measured using 12 items (e.g., “talk to others about inner troubles,” “try to see the bright side of things”) and negative coping styles is measured using 8 items (e.g., “smoking, drinking, taking medicine, or eating,” “trying to forget the whole thing”). Participants responded to each item on a scale from 0 = “not used at all” to 3 = “used very often.” Scores were averaged separately for the two subscales. Internal consistency were good (α = .74 for positive coping styles, and α = .68 for negative coping styles) in the present sample.

Grit

Grit is measured by the eight-item Short Form Grit Scale (Grit-S, Duckworth and Quinn, 2009). It includes two four-item subscales: consistency of interest (e.g., “New ideas and projects sometimes distract me from previous ones.”; α = .71) and perseverance of effort (e.g., “I finish whatever I begin.”; α = .67). Participants responded to each item on a scale from 1 (“not like me at all”) to 5 (“very much like me”).

Results

Descriptive statistics

As shown in Table 3, mean of GMML (α = .77) was high (M = 5.00, SD = 0.76, skewness = −.77). Gender was unrelated to GMML.

Table 3.

Descriptive data for the mindset of meaning in life scale in all samples.

	Study 1a	Study 1b	Study 2
N	1,038	296	1,493
M (4 items)	5.00	5.00	4.59
SD	0.84	0.76	1.04
Skewness	−1.12	−0.77	−0.77
Kurtosis	4.85	4.06	3.55
Cronbach's α	0.82	0.77	0.87

CFA was performed to evaluate the unifactorial model the MMLS. Model fit was measured by the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root-mean-square error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR). A good fit has CFI and TLI close to or above .95, and RMSEA and SRMR lower than .06 and .08, respectively (Hu & Bentler, 1998). CFA results showed that $χ^{2} (2) = 3.556$ , $χ^{2} / d f = 1.778,$ CFI = .995, TFI = .984, RMSEA = .051 with 90% CI [.00, .14], SRMR = 0.016, indicating good model fit. Figure 1 presents the factor loadings and residuals of individual items on the factor.

Figure 1.

Confirmatory factor analysis of the GMML items: one-factor solution.

Discriminant validity

As shown in Table 2, GMML achieved moderate correlations with the growth mindset of intelligence (.34 in Study 1a; .20 in Study 1b), and small or marginally significant correlations with ML-Presence (.23 in Study 1a; .10 in Study 1b) and ML-Search (.13 in Study 1a; .06 in Study 1b), suggesting that GMML is empirically distinguishable from these constructs.

To further evaluate whether GMML is distinct from growth mindset of intelligence, ML-Presence and ML-Search, we performed SEM on the four scales. A model that specified GMML as a distinct factor was compared to three alternative models that specified GMML as a component of ML-Presence, ML-Search, and growth mindset of intelligence, respectively. Table 4 lists the model fit indices. Model 1 which treated GMML as a distinct construct, as illustrated in Figure 2, fitted the data better than the alternative models (Model 2-4) in terms of $χ^{2}$ , CFI, TFI, RMSEA, and SRMR.

Figure 2.

A structural equation model (model 1 in Table 4) that treats GMML as a construct distinct from presence of meaning in life (mlq-p), search for meaning in life (mlq-s), and growth mindset of intelligence (gm).

Table 4.

Comparison of Model 1 that treated growth mindset of meaning in life (GMML) as a distinct construct and other models (Model 2-4) that treated GMML as a facet of meaning in life or growth mindset of intelligence.

Model	χ²	df	CFI	TFI	RMSEA [90%CI]	SRMR
1	209.62	113	.95	.95	.05 [.042, .065]	.05
2	508.32	116	.81	.78	.11 [.097, .116]	.10
3	743.56	116	.69	.64	.14 [.126, .145]	.13
4	659.51	116	.74	.69	.13 [.117 .135]	.10

Note: To establish discriminant validity, we tested four structural equation models to determine whether GMML is distinct from growth mindset of intelligence (GM), ML-Presence, and ML-Search. In Model 1, GMML, GM, ML-Presence, and ML-Search were set up as four distinct constructs. In Model 2, the items of GMML and ML-Presence were nested under the umbrella concept of “presence of meaning in life,” while GM and ML-Search were set up separately. In Model 3, the items of GMML and ML-Search were nested under the umbrella concept of “search for meaning in life,” while GM and ML-Presence were set up separately. In Model 4, the items of GMML and GM were nested under the umbrella concept of “mindsets of intelligence,” while ML-Presence and ML-Search were set up separately. The best-fitting model (Model 1, also see Figure 2) showed that GMML was a unique construct that is distinct from ML-Presence, ML-Search, and GM.

Convergent validity

GMML was positively correlated with life satisfaction, and negatively correlated with depression and anxiety in Study 1a. It was also positively associated with positive affect and positive coping strategies, and negatively correlated with depression, anxiety, negative affect, and negative coping strategies in Study 1b. Finally, GMML was positively associated with the perseverance subscale and not with the consistency of interest subscale of grit (See Table 3).

Discussion

Consistent with proposition 1, GMML is a construct distinct from meaning in life and growth mindset of intelligence. Moreover, GMML was positively associated with life satisfaction, positive affect, positive coping, and the perseverance facet of grit, and negatively associated with negative affect, depression, anxiety, and negative coping. Therefore, hypotheses 1a and 1b were supported.

GMML was high in our samples (mean = 5.00 out of 6 in both Study 1a and 1b), possibly due to sampling biases in Study 1. The participants in Study 1a were followers of the official Wechat account of the “Tsinghua Positive Psychology Research Center” and very likely endorsers of positive psychology. The participants in Study 1b were freshmen from an elite university in China. Participants in both studies might have strong self-improvement motivation, which may explain their high GMML scores. Study 2 was carried out to address this possible sampling bias and to investigate the role of TU as a mediator of the psychological well-being benefits of GMML.

Study 2

Overview

Study 2 aimed to study tolerance of uncertainty (TU) as a possible mechanism that links GMML to its psychological outcomes, including depression, anxiety, purpose in life, ML-Presence and ML-Search. We extended our research to vocational high school students in China, because adolescence is a critical stage of self-identity development according to Wang et al.’s (2016) survey on the meaning of life across Chinese adolescents and young adults. At these stages, both meaning in life and GMML tend to be low.

Method

Participants and procedure

We recruited students from a vocational high school in the Shandong Province and administered the survey via a smartphone link. A total of 1,493 participants (66.9% female; age ranged from 15 to 22 years, M = 16.40, SD = 0.99) completed the survey.

Measures

In addition to measures of GMML, meaning in life, growth mindset of intelligence, depression, and anxiety used in Study 1, we also included TU to evaluate its potential mediation role. Purpose in life was added to provide additional convergent validity evidence. All measures except gender were standardized before performing the mediational analysis.

Tolerance of uncertainty

TU was measured using the 12-item Chinese version of the Intolerance of Uncertainty Scale Short Form (IUS-12; Carleton et al., 2007; Zhang et al., 2017). Sample items are “Unforeseen events upset me greatly” and “It frustrates me not having all the information I need.” Participants responded to each item on a scale from 1 (“strongly disagree”) to 5 (“absolutely agree”). Scores were reversed so that higher scores indicate greater TU (α = .89).

Purpose in life

Purpose in life was measured using the four-item Purpose in Life-Short Form (PIL-SF; Schulenberg et al., 2011; Xiao et al., 2017), which was adapted from the original 20-item Purpose in Life Test (Crumbaugh and Maholick 1964, 1969; Xiao et al., 2017). The rating scales were customized for individual items. For example, participants responded to the statement of “In my life I have”: on a scale from 1 (“utterly meaningless, without purpose”) to 7 (“purposeful and meaningful”). The mean of the four items was used to form the measure (α = .88).

Results and discussion

Descriptive statistics and correlation analysis

Table 5 presents the descriptive statistics and inter-correlations of the measured variables. As expected, GMML was negatively associated with depression (r = −.23, p < .001) and anxiety (r = −.21, p < .001), and positively correlated with purpose in life (r = .27, p < .001), ML-Presence (r = .23, p < .001), and TU (r = .26, p < .001). GMML had a tiny positive correlation with ML-Search (r = .06, p < .05). Further analysis revealed that among those who valued but had not found meaning in life, the association of GMML and ML-Search was stronger (r = .15, p < .01).

Table 5.

Descriptive statistics and correlations (Study 2, N = 1,493).

Variables	1	2	3	4	5	6	7	8
1. Growth mindset of meaning in life	–
2. Tolerance of uncertainty	.26***	–
3. Depression	−.23***	−.49***	–
4. Anxiety	−.22***	−.49***	.82***	–
5. Purpose in life	.27***	.26***	−.47***	−.41***	–
6. Meaning in life-Presence	.23***	.24***	−.44***	−.38***	.72***	–
7. Meaning in life-Search	.06^*	−.12***	−.06^*	−.01	.24***	.37***	–
8. Growth mindset of intelligence	.42***	.33***	−.28***	−.26***	.29***	.29***	.05*	–
M (SD)	4.59 (1.04)	3.44 (.75)	2.35 (1.00)	2.23 (.96)	19.30 (5.27)	23.99 (5.96)	25.80 (5.39)	4.16 (1.27)
Range (min, max)	1, 6	1, 5	1, 5	1, 5	4, 28	5, 35	5, 35	1, 6
Cronbach's alpha	.87	.89	.94	.92	.88	.84	.85	.91

Note: *p < .05, **p < .01, ***p < .001.

Mediation analysis

We tested the mediation role of TU between GMML and depression, anxiety, purpose in life, ML-Presence, and ML-Search by performing the three-step regression analysis as in Baron and Kenny (1986). Figure 3 and Table 6 report the mediation analysis results controlling for age and gender in each regression. Table 7 reports the direct, indirect, and total effect.

Figure 3.

Tolerance of uncertainty (TU) mediated the relationships between growth mindset of meaning in life (GMML) and depression, anxiety, purpose in life, ML-Presence, and ML-Search (Study 2). The upper graph combines two mediational analyses (depression and anxiety as the dependent variable), and so does the middle graph (purpose in life and ML-Presence). The first and second coefficient in the same path show the results of the models with the first and second dependent variables, respectively.

Table 6.

Summary of mediation analyses (Study 2, N = 1,493).

	β	SE	t	p	95% CI
Depression
Depression ← GMML	−.23	.03	−9.06	< .001	[−.28, −.18]
TU ← GMML	.26	.03	1.29	< .001	[.21, .31]
Depression ← GMML (controlling for TU)	−.11	.02	−4.71	< .001	[−.15, −.06]
Depression ← TU (controlling for GMML)	−.46	.02	−19.95	< .001	[−.51, −.42]
Anxiety
Anxiety ← GMML	−.22	.03	−8.53	< .001	[−.27, −.17]
TU ← GMML	.26	.03	10.29	< .001	[.21, .31]
Anxiety ← GMML (controlling for TU)	−.09	.02	−4.07	< .001	[−.14, −.05]
Anxiety ← TU (controlling for GMML)	−.47	.02	−20.37	< .001	[−.52, −.43]
Purpose in life
Purpose in life ← GMML	.27	.02	10.72	< .001	[.22, .32]
TU ← GMML	.26	.03	10.29	< .001	[.21, .31]
Purpose in life ← GMML (controlling for TU)	.22	.03	8.53	< .001	[.17, .26]
Purpose in life ← TU (controlling for GMML)	.20	.03	7.91	< .001	[.15, .25]
Presence of meaning in life
Presence of meaning ← GMML	.23	.03	9.04	< .001	[.18, .28]
TU ← GMML	.26	.03	10.29	< .001	[.21, .31]
Presence of meaning ← GMML (controlling for TU)	.18	.03	6.97	< .001	[.13, .23]
Presence of meaning ← TU (controlling for GMML)	.19	.03	7.48	< .001	[.14, .24]
Search for meaning in life
Search for meaning ← GMML	.06	.03	2.19	.028	[.01, .11]
TU ← GMML	.26	.03	10.29	< .001	[.21, .31]
Search for meaning ← GMML (controlling for TU)	.09	.03	3.53	< .001	[.04, .15]
Search for meaning ← TU (controlling for GMML)	−.14	.03	−5.38	< .001	[−.19, −.09]

Note: Standardized coefficients. Age and gender were additionally controlled in each regression.

Table 7.

Total, direct, and indirect effect of GMML on psychological outcomes.

	β	SE	Z	p	Bootstrapped 95% CI	Proportion of total effect
Depression
Indirect effect	−.12	.01	−9.15	<.001	[−.15, −.09]	52%
Direct effect	−.11	.02	−4.71	<.001	[−.16, −.06]	48%
Total effect	−.23	.03	−9.06	<.001	[−.28, −.17]
Anxiety
Indirect effect	−.12	.01	−9.19	<.001	[−.15, −.09]	56%
Direct effect	−.09	.02	−4.07	<.001	[−.15, −.04]	44%
Total effect	−.22	.03	−8.53	<.001	[−.27, −.16]
Purpose in life
Indirect effect	.05	.01	6.27	<.001	[.03, .07]	19%
Direct effect	.22	.03	8.53	<.001	[.16, .27]	81%
Total effect	.27	.03	10.72	<.001	[.21, .32]
Presence of meaning in life
Indirect effect	.05	.01	6.05	<.001	[.03, .07]	21%
Direct effect	.18	.03	6.97	<.001	[.12, .24]	79%
Total effect	.23	.03	9.04	<.001	[.17, .29]
Search for meaning in life
Indirect effect	−.04	.01	−4.77	<.001	[−.06, −.02]	−65%
Direct effect	.09	.03	3.53	<.001	[.03, .16]	163%
Total effect	.06	.03	2.19	0.028	[−.01, .12]

Note: Z statistics and p-values were Sobel test results (Baron and Kenny, 1986; Sobel, 1982). The confidence intervals for indirect effects were calculated based on 3,000 bootstrapped samples.

Supporting Hypothesis 3, TU partially mediated the effect of GMML on depression, anxiety, purpose in life, ML-Presence, and ML-Search. For the initial relationships between GMML and depression (β = −.23, p < .001) and anxiety (β = −.22, p < .001) were reduced after adding TU as a mediator (β = −.11, p < .001 for depression; β = −.09, p < .001 for anxiety). Sobel tests showed the indirect effects of TU were significant (Z = −9.15, p < .001 for depression; Z = −9.19, p < .001 for anxiety), accounting for 52% of the total effect on depression and 56% of the total effect on anxiety.

Supporting Hypothesis 4, the initial relationships between GMML and purpose in life (β = .27, p < .001) and ML-Presence (β = .23, p < .001) were also reduced when additionally controlled for TU (β = .22, p < .001 for purpose in life; β = .18, p < .001 for ML-Presence). Sobel tests showed the indirect effects of TU were significant (Z = 6.27, p < .001 for purpose in life; Z = 6.05, p < .001 ML-Presence), accounting for 19% and 21% of the total effects, respectively. After additional controlling for growth mindset of intelligence, the significance of the results did not change.

Though the mediation effect of TU on the relationship between GMML and ML-Search was also statistically significant, the relationship between TU and ML-Search was negative (β = −.14, p < .001). Thus, the hypothesis regarding the mediation of TU for the positive relationship between GMML and ML-Search (Hypothesis 5) was not supported. This negative result was consistent with the small correlations between GMML and ML-Search.

Together, our findings suggest TU significantly mediated the relationships between GMML and psychological outcomes. Individuals with higher GMML were associated with less depression and anxiety and more purpose and meaning in life via higher tolerance of uncertainty.

General discussion

Our results provided support for GMML as a distinct construct that is associated with a variety of psychological benefits. In the present research, we defined GMML as individuals’ implicit belief about the malleability of meaning in life and developed a scale (MMLS) to measure it (Study 1a). This measure had small correlations with extant measures of mindset about intelligence and meaning in life. GMML was also positively related to life satisfaction, positive affect, positive coping, and perseverance of effort, and negatively associated with depression, anxiety, negative affect, and negative coping (Study 1a and 1b). Study 2 further showed that GMML is linked to its psychological benefits through TU; TU partially mediated the relationships between GMML and depression, anxiety, purpose in life, and ML-Presence.

These findings make meaningful contributions to the mindset and meaning in life literature. First, the growth (vs. fixed) mindset of meaning in life is people's lay belief about life meaning. Dweck (2017) posits that people's mental constructions of the malleability of personal and social attributes (e.g., intelligence, personality) are linked to psychological well-being. Extending the mindset theory to the domain of meaning in life, people's assumptions about the malleability of meaning in life have profound psychological implications. By establishing the associations between GMML and psychological well-being, our findings extend the mindset literature to the new domain of meaning in life, and bring new insight to the meaning in life literature (George & Park, 2016).

Furthermore, TU mediates the relationship between GMML and psychological outcomes. Our findings are consistent with the existing theories and practices about the coping strategies in facing life uncertainties. Most people inevitably would face unpredictable life events. Many health practitioners have argued that exerting control may cause psychological distress when faced with unplanned situations, while accepting and embracing the uncertainties might bring better adjustment outcomes (e.g., Hoare, McIlveen, & Hamilton, 2012). For example, Gelatt (1989) has proposed the term “positive uncertainty” as a counselling strategy by reframing unexpected events as opportunities for growth and learning. The chaos theory of careers (Bright & Pryor, 2011) and the planned happenstance theory (Mitchell, Levin, & Krumboltz, 1999) have adapted this idea into counselling strategies in career development. Our results suggest that GMML is accompanied by an increased tolerance of uncertainty, which helps prepare people to accept uncertainty as a self-improvement challenge instead of being a threat.

Taken together, our findings contribute to the mindset and meaning in life literature by providing a fresh look at the lay theories of meaning in life. Our results also provide insights on TU as a mediator of the relationships between GMML and its psychological benefits.

Limitations and future directions

The present research has limitations. First, only cross-sectional self-report data were collected. Hence, we cannot infer causality from these data. Particularly, reverse causality in the relationship between GMML and TU may exist. This issue cannot be addressed in the present studies due to their limitations in research design. For this reason, we welcome experimental and longitudinal studies to establish the causal impact of GMML and to identify the antecedents of GMML and effective GMML interventions to help people manage their existential problems. Second, our results need to be replicated with more demographically diverse samples, such as samples with a wider age range, greater cultural diversity (Steger et al., 2008b), and people facing stressful situations (Klinger, 1998; Thompson and Janigian, 1988). Finally, future studies are needed to understand when and how meaning mindsets develop with age. It would be important to identify the critical period for the construction of meaning mindsets or when meaning mindset construction would become a critical developmental issue.

Conclusion

The mindset of meaning in life is an implicit theory about meaning in life. It refers to a belief in the malleability of meaning in life. Two contrastive mindsets are the growth and fixed mindsets of meaning in life. The GMML represents the belief that meaning in life can grow in a dynamic process and change over time, whereas the FMML represents the opposite belief that meaning in life is fixed and cannot change. GMML is positively correlated with a variety of psychological benefits, and these correlations are partly mediated by tolerance of uncertainty. These findings shed light on the role of mindsets on meaning in life and provide implications for developing psychological interventions addressing existential meaning.

Supplemental Material

sj-docx-1-pac-10.1177_18344909231166758 - Supplemental material for Growth mindset of meaning in Life: Viewing meaning in life as malleable matters

Supplemental material, sj-docx-1-pac-10.1177_18344909231166758 for Growth mindset of meaning in Life: Viewing meaning in life as malleable matters by Zhen Huang, Yiwen Wu, Yukun Zhao and Kaiping Peng in Journal of Pacific Rim Psychology

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Zhen Huang

Yiwen Wu

Supplemental material

Supplemental material for this article is available online.

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