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. 2023 Feb 1;4(2):401–412. doi: 10.1007/s42761-022-00179-6

Clarity of Emotions and Goals: Exploring Associations with Subjective Well-Being Across Adulthood

Nathaniel S Eckland 1,✉, Howard Berenbaum 1
PMCID: PMC10247608  PMID: 37304560

Abstract

Despite declines in cognition associated with age, emotional health tends to increase. However, extant studies find few differences in the type or number of emotion regulation strategies used by older compared to younger adults. This study tested the hypothesis that older adults have greater clarity of their emotions and goals compared to younger adults. Participants (total N = 709, ages 18–81) recruited in age-stratified samples completed measures of emotional clarity, goal clarity, depression, and life satisfaction. Results suggested that emotional clarity and goal clarity are positively correlated factors, with emotional clarity showing the lowest levels in emerging adults and highest levels in older adults. Goal clarity was lowest among emerging adults, but only small differences were found between middle and older adults. Across adulthood both emotional clarity and goal clarity were linked to lower depressive symptoms and greater life satisfaction. Limitations include data being cross-sectional and self-report based and the youngest sample being recruited differently from the older samples, but the results raise the possibility of developmental changes in emotional clarity across adulthood.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-022-00179-6.

Keywords: Emotional clarity, Goals, Adult development, Depression, Life satisfaction, Subjective well-being

Socioemotional selectivity theory (Carstensen, 1992), which posits that motivation shifts toward emotionally meaningful goals as time is perceived as more limited (e.g., approaching end of life), has been used to explain why emotional health improves with age (Carstensen et al., 2000). Although these increases in emotional health are often attributed to older adults engaging in better emotion regulation, research examining age differences in emotion regulation strategy use has found few differences in the types and number of strategies used by older and younger adults (Eldesouky & English, 2018; Shiota & Levenson, 2009). The present work tests an alternate explanation for why older adults are able to work toward emotionally meaningful goals: greater emotional clarity.

Emotional Clarity

Emotional clarity is a metacognitive process (i.e., a process that deals with the monitoring, evaluation, and control of one’s thoughts; Dunlosky & Metcalffe, 2009) that refers to how clearly people identify and describe their emotions (Gohm & Clore, 2000). Affect-as-information theory emphasizes that emotions provide information to people about their goals, internal states, relationships, and environment (Clore et al., 2001); having a clearer knowledge of one’s emotions is thought to aid in the use of this information. Deficits in emotional clarity are associated with psychopathology, including depression (Boden & Thompson, 2015; Eckland et al., 2021) and worry (Eckland & Berenbaum, 2021). Dizén et al. (2005) found that emotional clarity is also associated with better understanding of one’s psychological needs, suggesting having higher clarity about emotions may support having more clarity about one’s needs and goals. Having a clearer understanding of what one feels, what one needs, and what one’s goals are should facilitate need satisfaction and goal attainment, which are components of a fulfilling life (Freund et al., 2010; Sheldon & Elliot, 1999).

Models of emotional aging emphasize the strengths that older adults possess, including their accrued years of experience and knowledge (Charles, 2010). Emotional clarity may increase in older adults because of their accumulated years of emotional experiences, combined with their relatively intact capacity for metacognition (Hertzog & Hultsch, 2000; Hertzog & Dunlosky, 2011). The present research begins to test the possibility that over the life course, the accumulation of emotional experiences contributes to increased emotional clarity, which in turn accounts for the increased emotional health of older adults.

Goals and Goal Clarity

Goals are internal representations of desired states (Austin & Vancouver, 1996) and are intrinsically linked to emotions. Functional accounts posit that emotions can provide information to people about their goals and facilitate goal attainment (Keltner & Gross, 1999; Tamir et al., 2008). For example, contentment may signal that one’s goals and needs are satisfied, whereas frustration may signal that goals are being blocked (Huntsinger & Clore, 2012). Goal clarity describes how clearly people understand their goals and what they need to do to accomplish them (Winell, 1987). Emmons (1986) found that goal clarity was associated with greater commitment, perceived probability of success, and goal fulfillment.

Socioemotional selectivity theory (Carstensen, 1992) suggests that as people age and they view time as more limited, they will select emotionally meaningful goals. For example, spending time with family (which is likely to provide pleasure and fulfillment) is more likely to be prioritized over spending time learning a new language (which may provide a sense of mastery). Embedded within socioemotional selectivity theory is the idea that people have some knowledge about their feelings (i.e., emotional clarity) that enables them to select goals that promote desired feelings. Furthermore, having a clearer understanding of one’s goals should be important for shifting one’s goal priorities. Thus, emotional and goal clarity seem to be important, potentially overlapping, traits underlying the shifts described in socioemotional selectivity theory that promote emotional health and subjective well-being. To understand the mechanisms of healthy emotional aging, the gaps in knowledge regarding emotional clarity, goal clarity, and aging need to be addressed.

The Present Research

The present study used cross-sectional methods as an initial test of (1) the associations between emotional and goal clarity; (2) differences in levels of clarity across adulthood; and (3) the associations between these types of clarity and subjective well-being across adulthood. Regarding our first goal, we tested a single-factor, correlated factor models, and a bifactor model, to account for the structure of emotional and goal clarity. Regarding our second goal, we hypothesized that older adults would have the highest levels of psychological clarity (i.e., of both emotional and goal clarity). Finally, regarding our third goal, we hypothesized that emotional clarity and goal clarity would be positively associated with life satisfaction and negatively associated with depressive symptoms. Given that a core aspect of depression is emotional disturbance (Clark & Watson, 1991) and life satisfaction is thought to be the cognitive dimension of subjective well-being (Diener, 1984), we hypothesized that emotional clarity would be more strongly linked to depressive symptoms, whereas goal clarity would be more strongly linked to life satisfaction.

Method

Open Science Statements

The data and analytic code for this study are publicly accessible at https://osf.io/mw7ut. This study was not pre-registered.

Participants

Sample 1: Emerging Adult Sample

This sample was collected using the psychology undergraduate subject pool at a large midwestern university. The subject pool includes both psychology and non-psychology majors. Introductory psychology students typically make up most of the subject pool, which is the psychology course most likely to be taken by non-majors. Participants provided informed consent and received course credit for their participation. The final sample included 329 participants (67.8% female; 32.2% male; 0% non-binary) ranging in age from 18 to 25 (MAGE = 19.3; SDAGE = 1.4). Participants self-identified as 41.6% White/European American, 31.9% Asian American, 8.2% Black/African American, 7.6% multiracial, 0.6% Native American, and 10.0% none of listed categories. In terms of ethnicity, 20.4% reported identifying as Latina/Latino or Latinx. The research was approved by an Institutional Review Board.

Sample 2: Younger to Middle Adult Sample

This sample was collected online in October 2020 using the online platform Prolific (www.prolific.co). Participants were eligible if they were over the age of 18, lived in the USA, and reported having English as a first language or being fluent in English. Data from 200 participants in three age-stratified samples (50 participants between 25 and 35; 75 participants between 35 and 45; 75 participants between 45 and 55) were collected to ensure adequate coverage from young to middle adulthood. Usable data1 was available for 185 participants (68.6% female; 75.1% White; 10.3% Black; 4.9% Asian; 6.5% multiracial; 3.2% reported a race other than those previously listed; 8.1% Latino/Latina ethnicity), whose ages ranged from 25 to 55 (M = 40.3 years old; SD = 9.0 years). The research was approved by an Institutional Review Board.

Sample 3: Middle-to-Older Adult Sample

This sample was also collected online in October 2020 using the online platform Prolific (www.prolific.co). The same eligibility criteria were used as Sample 2. Data from 200 participants in four age stratified samples (60 participants between 55 and 60; 60 participants between 60 and 65; 50 participants between 65 and 70; 30 participants ages 70 +) were collected to ensure adequate coverage ranging from midlife to older adulthood. Note that age 55 roughly corresponds to the youngest age that someone from the “Baby Boomer” generation would be in 2020 when the data were collected. Usable data (evaluated based on the same criteria as Sample 2) was available for 195 participants (60.5% female; 91.3% White; 3.1% Black; 2.6% Asian; 1.5% multiracial; 1.5% reported a race other than those previously listed; 3.1% Latino/Latina ethnicity), whose ages ranged from 55 to 81 (M = 63.7 years old; SD = 5.8 years). The research was approved by an Institutional Review Board.

Power Analysis

We conducted power simulations using pwrSEM (Wang & Rhemtulla, 2021; https://yilinandrewang.shinyapps.io/pwrSEM/). We selected the most complex model described below (containing a correlation between emotional and goal clarity and regression paths from both types of clarity to depression and life satisfaction) and used the final sample size for our smallest group (n = 185; young-to-middle-age adults). Based on our hypotheses, we specified target effects of r = 0.35 for the correlation between emotional and goal clarity, β = 0.4 for the association between depression-emotional clarity and life satisfaction-goal clarity associations, and β = 0.25 for the depression-goal clarity and life satisfaction-emotional clarity associations. After 500 simulations, the average power for the correlation was estimated at 98%, the average power for the regression coefficients of β = 0.4 was 94% and 99%, and the average power for the regression coefficients of β = 0.25 was 65% and 66%.

Procedures

All participants gave informed consent to be in this study. Participants completed the measures in a pseudo-randomized order. All participants began by writing about their personal strivings (described below), then in a randomized order completed measures of emotional clarity, depression, and life satisfaction. Participants then completed a subset of three supplemental measures from a set of six possible supplemental measures (not used in the current report). Participants in the emerging adult sample completed all of the supplemental measures. Participants in Sample 1 took approximately 45 min to complete the study and were compensated with course credit. Participants in Samples 2 and 3 took approximately 25 min to complete the study and were compensated with $3.50.

Measures

Emotional Clarity

Emotional clarity of type was measured with 13 items identified through multidimensional scaling by Palmieri et al. (2009). Eight of the items come from the Trait Meta-Mood Scale (TMMS; Salovey et al., 1995) emotional clarity subscale and five of the items come from Toronto Alexithymia (TAS; Bagby et al., 1994) difficulty identifying feeling subscale. Items were rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). An example item is, “I can’t make sense out of my feelings (TMMS; reverse-scored).” Internal consistencies were good for emerging adults (ωt = 0.92, α = 0.91), young-to-middle adults (ωt = 0.95, α = 0.94), and middle-to-older adults (ωt = 0.94, α = 0.93). Emotional clarity of source was measured using the Sources of Emotions Scale developed by Boden and Berenbaum (2011). This scale has 8 items which are rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). An example item is, “I often have to think for a while to figure out what made me happy or excited.” Internal consistencies were good for emerging adults (ωt = 0.88, α = 0.84), young-to-middle adults (ωt = 0.92, α = 0.89), and middle-to-older adults (ωt = 0.92, α = 0.88).

Personal Strivings and Goal Clarity

We measured clarity of goals using procedures originally developed by Emmons (1986) to assess dimensions of personal strivings. These procedures involve writing a list of eight personal strivings (e.g., “I want to be a healthy person,” “I want to avoid gossiping with others”). Each striving was rated using Emmons’ (1986) clarity item (“ How clear of an idea do you have of what you need to do to be successful in this striving?”) on a 7-point scale (1 = extremely unclear, 7 = extremely clear). For the samples collected online, only six strivings were measured in order to reduce the overall time the survey took. For analyses involving the emerging adult sample, we used the first six out of the eight strivings listed, as strivings that were recorded first should have been most salient/accessible in the participant’s mind and should represent the most important goal to the participant (Higgins, 1996). Internal consistencies were good for emerging adults (ωt = 0.77, α = 0.62), young-to-middle adults (ωt = 0.81, α = 0.75), and middle-to-older adults (ωt = 0.83, α = 0.70).

Life Satisfaction

We measured subjective well-being using the Satisfaction with Life Scale (SWLS; Diener et al., 1985). This five-item measure includes items such as, “In most ways my life is close to ideal.” Items are rated on a 7-point scale (1 = strongly disagree, 7 = strongly agree). Internal consistencies were good for emerging adults (ωt = 0.88, α = 0.85), young-to-middle adults (ωt = 0.93, α = 0.92), and middle-to-older adults (ωt = 0.92, α = 0.90).

Depressive Symptoms

Depressive symptoms were measured using the 8-item version of the Mood and Anxiety Symptoms Questionnaire (Watson et al., 1995). Example items include “Felt unattractive” and “Felt withdrawn from people.” Items are rated on a 5-point scale (1 = not at all, 5 = extremely) indicating how much the participant felt that way over the past week. The suicide item was not administered due to IRB concerns. Bredemeier et al. (2010) found that the 8-item version was more strongly associated with current major depressive episode status than other depression measures. Participants in each group reported symptoms ranging from mild levels to levels consistent with major depression. Internal consistencies in the present sample were good for emerging adults (ωt = 0.90, α = 0.86), young-to-middle adults (ωt = 0.93, α = 0.89), and middle-to-older adults (ωt = 0.91, α = 0.86).

Analytic Plan

Analyses took place in three stages corresponding to the three study goals. We tested four measurement models to identify the structural relationship between emotional and goal clarity: a one-factor model where indicators for emotional and goal clarity were loaded onto a single latent variable; a two correlated factor model where indicators for emotional clarity of type and source were loaded onto one latent variable and goal clarity loaded onto another latent variable; a three correlated factor model where emotional clarity of type, source, and goal clarity indicators were all loaded onto their own latent variables, which were allowed to correlate; and a bifactor model with a single general clarity factor that all indicators were loaded onto and then separate specific factors for emotional and goal clarity. Based on the results described below, we proceeded with the two-factor model for subsequent multigroup analyses.

Next, we used multigroup CFA to test measurement invariance across the emerging, young-to-middle, and middle-to-older adult groups. First, we added a mean structure to the model. Then, we tested the model with increasing constraints: first configural invariance (i.e., factor structures constrained to be equal across groups), then metric invariance (i.e., factor structures and factor loadings constrained to be equal across groups), then scalar invariance (i.e., factor structures, factor loadings, and intercepts constrained to be equal across groups), and then strict invariance (i.e., factor structures, factor loadings, intercepts, and residual variances constrained to be equal across groups). Model fit was compared using several fit indices: χ2 difference tests, ΔCFI ≤ 0.01, ΔRMSEA ≤ 0.015, ΔSRMR ≤ 0.030 for the metric model, and ΔSRMR ≤ 0.015 for scalar and strict invariance models (Beaujean, 2014; Chen, 2007; Putnick & Bornstein, 2016). Note that the full sample (n = 708) was overpowered for a χ2 difference test (80% power to detect a medium-size effect, φ = 0.3 with df = 50, is achieved with n = 336). Thus, changes in CFI, RMSEA, and SRMR were prioritized. Scalar invariance (i.e., intercepts constrained to be equal) is needed to test the hypothesis that the eldest group would show the highest levels of emotional and goal clarity (Bontempo & Hofer, 2007). When scalar invariance is met, one group (in our case, the emerging adults) is specified as the reference group for group comparisons. Given that latent variables do not have an inherent scale, we chose to standardize them. Therefore, the intercept (i.e., the expected mean value of that construct for that group) of the latent variable is set to 0 for the reference group, with variance/standard deviation of 1 (the latent intercepts for the comparison groups have the same variance/standard deviation of 1). Because the latent variables are on a standardized scale, z-values are used to compare the intercepts (i.e., latent means) of the comparison groups to the reference group, and a hypothesis test with H0: z = 0 (e.g., group 2 or 3 has the same level of emotional/goal clarity as the reference group) is conducted. If the z-score significantly differs from 0, then its value serves as a standardized effect size of how many standard deviations higher (or lower, in the case of a negative z-value) the comparison group is on that latent construct relative to the reference group.

Finally, we tested associations between emotional and goal clarity and depression and life satisfaction. We then constructed a structural equation model (SEM) with emotional and goal clarity predicting life satisfaction and depressive symptoms. The outcomes were specified as correlated latent variables, with their scale items used as indicators. We tested this model using the same multigroup procedures described above. Metric invariance (i.e., factor loadings constrained to be equal) is needed to make comparisons across groups about covariances and path coefficients (Bontempo & Hofer, 2007).

All models used full information maximum likelihood estimation. In all models, model fit was evaluated using criteria from Hu and Bentler (1998): confirmatory fit index (CFI; ≥ 0.90 is considered “adequate fit”; ≥ 0.95 is considered “good fit”), root mean square error of approximation (RMSEA; ≤ 0.08 is considered “adequate fit”; ≤ 0.06 is considered “good fit”), and standardized root mean square residual (SRMR; ≤ 0.08 is considered “good fit”). To evaluate bifactor models, we calculated (1) the explained common variance of the general factor (i.e., ECV; Rodriguez et al., 2016), or the percent of variance extracted from the indicators explained by the general factor—higher ECV signals that the indicators are measuring a unidimensional construct; (2) indicator-explained common variance (I-ECV; Stucky & Edelen, 2015), or the proportion of variance per indicator explained by the common factor; (3) omega hierarchical (ωH) for common factor (McDonald, 1999), or the proportion of variance in total scores that is attributable to the general factor; (4) and omega hierarchical subscale (ωHS) for each specific factor (Reise et al., 2013), or the reliability of the subscale score after controlling for variance due to the general factor.

Results

Descriptive statistics and correlations among manifest variables are shown in Table 1. Since the emerging adult sample differed from the other samples in terms of racial composition (notably, the emerging adult sample had a larger proportion of Asian/Asian American to White participants than the other samples did), we tested for differences in study variables across Asian/Asian American and White participants within that sample. Asian/Asian American and White participants did not differ in terms of type clarity, t(202.3) = 0.21, p = 0.83, source clarity, t(196.6) =  − 1.24, p = 0.22, goal clarity, t(203.4) =  − 0.51, p = 0.61, or depressive symptoms, t(199.4) = 1.39, p = 0.17. However, White participants (mean = 4.5, SD = 1.2) reported higher levels of life satisfaction, t(205.7) =  − 3.13, p = 0.002, d = 0.4, relative to Asian/Asian American participants (mean = 4.0, SD = 1.4). Given that there were no differences in levels of psychological clarity between Asian/Asian American and White participants, it is unlikely that any differences observed in levels of clarity across age groups would be due to the different demographic make-up of the youngest group.

Table 1.

Descriptive statistics and correlations among study variables by sample

Type clarity Source clarity Goal clarity Life satisfaction Depression Age
Emerging adults (n = 329)
 Source clarity .72 –
 Goal clarity .28 .22 –
 Life satisfaction .32 .24 .19 –
 Depression  − .41  − .33  − .17  − .54 –
 Age .04  − .05  − .04  − .12 .10 –
 Mean (SD) 3.5 (0.7) 3.8 (0.6) 5.6 (0.8) 4.3 (1.3) 2.6 (0.9) 19.3 (1.4)
 Range 1.2–5.0 1.1–5.0 3.1–7.0 1.0–7.0 1.0–4.7 18–25
Young-to-middle adults (n = 185)
 Source clarity .81 –
 Goal clarity .33 .27 –
 Life satisfaction .27 .08 .36 –
 Depression  − .52  − .36  − .34  − .52 –
 Age .28 .28  − .001  − .08  − .18 –
 Mean (SD) 3.8 (0.8) 3.9 (0.7) 5.9 0(.9) 4.0 (1.6) 2.5 (1.0) 40.3 (8.9)
 Range 1.1–5.0 1.5–5.0 2.0–7.0 1.0–7.0 1.0–5.0 25–55
Middle-to-older adults (n = 194)
 Source clarity .76 –
 Goal clarity .21 .20 –
 Life satisfaction .25 .14 .34 –
 Depression  − .44  − .27  − .27  − .55 –
 Age .05  − .05 .03 .02  − .10 –
 Mean (SD) 4.0 (0.6) 4.2 (0.6) 5.9 (0.8) 4.1 (1.5) 2.1 (0.9) 63.7 (5.7)
 Range 2.2–5.0 2.3–5.0 2.8–7.0 1.0–7.0 1.0–4.7 55–81
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Correlations at the manifest, not latent, level

Goal 1: Testing the Structure of Emotional and Goal Clarity

The one-factor model had poor fit to the data, χ2 (324) = 1625.11, p < 0.001, CFI = 0.850, RMSEA = 0.075, and SRMR = 0.062. The single factor showed acceptable reliability, ω = 0.88. The two-factor model had adequate-to-good fit to the data across fit indices, χ2 (323) = 1180.61, p < 0.001, CFI = 0.901, RMSEA = 0.061, and SRMR = 0.045, and suggested emotional and goal clarity factors were moderately correlated, r = 0.36, p < 0.001. Both factors showed acceptable reliability (emotional clarity ω = 0.94; goal clarity ω = 0.71). The three-factor model had good fit to the data across fit indices, χ2 (321) = 840.54, p < 0.001, CFI = 0.940, RMSEA = 0.048, and SRMR = 0.040, and each factor had acceptable reliability (type clarity ω = 0.93; source clarity ω = 0.87; goal clarity ω = 0.71). Both type clarity and source clarity were moderately positively correlated with goal clarity, rtype, goal = 0.36, p < 0.001 and rsource, goal = 0.31, p < 0.001. However, the correlation between emotional clarity of type and source was so strong, r = 0.84, p < 0.001, that the model did not suggest these factors were distinct from each other. The bifactor model also had good fit to the data, χ2 (197) = 655.20, p < 0.001, CFI = 0.959, RMSEA = 0.041, and SRMR = 0.029. The general factor explained greater variance in the emotional clarity items (I-ECV range = [0.43–0.99]), relative to the goal clarity ratings (I-ECV range = [0.07–0.37]). The ECV of the general factor was 0.70. For the common factor, ωH = 0.85. For the emotional clarity factor, ωHS = 0.05, and for the goal clarity factor, ωHS = 0.06. These estimates suggest the common factor was primarily explaining the variance in emotional clarity items and leaving very little reliable variation within the specific factors. Factor loadings for the four measurement models are shown in Supplemental Table S1. Given (1) the emotional and goal clarity factors were only moderately correlated, (2) the emotional clarity of type and source factors were correlated to the point of redundancy in the three-factor model, and (3) the emotion- and goal-specific factors in the bifactor model had very little reliable residual variance, we selected the well-fitting two-factor model for subsequent multigroup analyses.

Goal 2: Examining Differences in Levels of Clarity Across Adulthood

We began by testing the measurement invariance of the two-factor model across age groups. Results are shown in the top section of Table 2. Differences across three of four fit indices suggested that the scalar model could be interpreted, χ2(50) = 121.77, p < 0.001, ΔCFI =  − 0.008, ΔRMSEA = 0.001, and ΔSRMR = 0.003, which allows for mean differences in latent constructs to be examined.

Table 2.

Change in fit indices from measurement invariance analyses

Δχ2 (df) p ΔCFI ΔRMSEA ΔSRMR
Measurement invariance of two-factor model
Configural ➔ metric 109.41 (50)  < .001  − .007  < .001 .014
Metric ➔ scalar 121.77 (50)  < .001  − .008 .001 .003
Scalar ➔ strict 356.71 (54)  < .001  − .036 .007 .006
Measurement invariance of model with subjective well-being outcomes
Configural ➔ metric 126.36 (70)  < .001  − .004  < .001 .008
Metric ➔ scalar 258.99 (70)  < .001  − .014 .002 .002
Scalar ➔ strict 433.51 (78)  < .001  − .026 .004 .003
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ΔCFI change in comparative fit index, ΔRMSEA change in root mean square error of approximation, ΔSRMR change in standardized root mean squared residual. For comparison between configural and metric models, ΔCFI > .01, ΔRMSEA > .015, and ΔSRMR > .030 indicate significantly worsened fit. For comparison between the metric and scalar and between scalar and strict models, ΔCFI > .01, ΔRMSEA > .015, and ΔSRMR > .015 indicate significantly worsened fit. Change in fit indices indicating significantly worsened fit have been bolded. For the two-factor model, scalar invariance was met in three of four fit indices; for the model with subjective well-being outcomes, metric invariance was met in three of four fit indices

Results from the scalar model are shown in Table 3. The young-to-middle adults showed increased levels of emotional clarity relative to the emerging adults, z = 2.88, p = 0.004. This indicates that the young-to-middle group was reporting levels of emotional clarity that were, on average, 2.88 standard deviations higher than the emerging adult group. In line with our hypothesis, the middle-to-older adult group showed the highest levels of emotional clarity relative to other groups, z = 7.81, p < 0.001. In other words, the middle-to-older groups were reporting levels of emotional clarity that were, on average, 7.81 standard deviations higher than the emerging adult group. These results indicate very large magnitudes of difference between the youngest group and elder groups in terms of levels of emotional clarity. The young-to-middle adults also showed increased levels of goal clarity relative to the emerging adults, z = 2.03, p = 0.04. The middle-to-older adult group also showed higher levels of goal clarity relative to emerging adults, z = 2.87, p = 0.005, but this was only slightly higher than the young-to-middle adult group. Taken together, we found strong evidence of age differences in emotional clarity, such that young adults experience the lowest levels of emotional clarity and older adults experience the highest levels. There also appears to be an increase in goal clarity associated with maturing from emerging adulthood into young adulthood, but this increase is less pronounced for older participants.2

Table 3.

Multigroup latent means and covariance model of emotional and goal clarity with scalar invariance constraints

Emerging adults Young-to-middle adults Middle-to-older adults
Latent variables λ z p λ z p λ z p
Emotional clarity
 TC 1 .75 20.26  < .001 .88 20.26  < .001 .69 20.26  < .001
 TC 2 .67 17.55  < .001 .78 17.55  < .001 .61 17.55  < .001
 TC 3 .78 18.38  < .001 .91 18.38  < .001 .71 18.38  < .001
 TC 4 .71 18.18  < .001 .83 18.18  < .001 .65 18.18  < .001
 TC 5 .62 16.48  < .001 .72 16.48  < .001 .56 16.48  < .001
 TC 6 .47 16.54  < .001 .54 16.54  < .001 .43 16.54  < .001
 TC 7 .72 19.33  < .001 .84 19.33  < .001 .65 19.33  < .001
 TC 8 .69 16.87  < .001 .80 16.87  < .001 .62 16.87  < .001
 TC 9 .55 14.28  < .001 .64 14.28  < .001 .50 14.28  < .001
 TC 10 .58 16.59  < .001 .67 16.59  < .001 .53 16.59  < .001
 TC 11 .56 18.27  < .001 .65 18.27  < .001 .51 18.27  < .001
 TC 12 .70 18.47  < .001 .81 18.47  < .001 .63 18.47  < .001
 TC 13 .76 20.25  < .001 .89 20.25  < .001 .69 20.25  < .001
 SC 1 .54 16.00  < .001 .63 16.00  < .001 .49 16.00  < .001
 SC 2 .45 13.85  < .001 .53 13.85  < .001 .41 13.85  < .001
 SC 3 .46 16.40  < .001 .53 16.40  < .001 .42 16.40  < .001
 SC 4 .43 12.97  < .001 .50 12.97  < .001 .39 12.97  < .001
 SC 5 .49 17.15  < .001 .57 17.15  < .001 .45 17.15  < .001
 SC 6 .71 18.07  < .001 .83 18.07  < .001 .65 18.07  < .001
 SC 7 .53 15.09  < .001 .62 15.09  < .001 .48 15.09  < .001
 SC 8 .60 16.75  < .001 .70 16.75  < .001 .55 16.75  < .001
Goal clarity
 GC 1 .33 7.91  < .001 .43 7.91  < .001 .38 7.91  < .001
 GC 2 .48 10.08  < .001 .62 10.08  < .001 .56 10.08  < .001
 GC 3 .60 10.87  < .001 .78 10.87  < .001 .70 10.87  < .001
 GC 4 .76 11.88  < .001 .97 11.88  < .001 .87 11.88  < .001
 GC 5 .76 11.40  < .001 .98 11.40  < .001 .88 11.40  < .001
 GC 6 .77 11.19  < .001 .99 11.19  < .001 .88 11.19  < .001
Covariances r z p r z p r z p
 r (EC, GC) .37 5.70  < .001 .38 3.92  < .001 .26 2.84 .005
Intercepts Est z Var Est z Var Est z Var
 Emotional clarity 0 – 1 .26 2.88** 1 .80 7.81*** 1
 Goal clarity 0 – 1 .20 2.03* 1 .30 2.84** 1
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TC emotional clarity of type item, SC emotional clarity of source item, EC emotional clarity latent variable, GC goal clarity latent variable, Est estimated latent mean. Emerging adults were treated as the reference group for group comparisons; z-tests shown for estimated latent means represent the number of standard deviation increases in emotional and goal clarity the young-to-middle and middle-to-older adult group report compared to the emerging adult group. Only emotional clarity and goal clarity, not the subjective well-being outcomes, are included in this model

***p < .001; **p < .01; *p < .05

Goal 3: Examining Associations with Subjective Well-Being

Finally, we tested an SEM adding depressive symptoms and life satisfaction as correlated latent outcomes to the two-factor emotional and goal clarity model. Again, we began by testing measurement invariance of the full SEM (see bottom section of Table 2 for changes in fit indices).3 Differences across three of four fit indices suggested that the metric model could be interpreted, χ2(70) = 126.36, p < 0.001, ΔCFI =  − 0.004, ΔRMSEA < 0.001, and ΔSRMR = 0.008, which allows for comparisons of covariances and regression coefficients. We therefore tested a model with covariances and regression coefficients from emotional and goal clarity to the subjective well-being outcomes constrained to be equal across the three groups. For three of the four indices, fit did not significantly worsen with the additional constraints, χ2 (215) = 300.43, p < 0.001, ΔCFI = 0.005, ΔRMSEA = 0.001, and ΔSRMR = 0.001. This suggests the associations between emotional clarity and subjective well-being, and goal clarity and subjective well-being did not differ across age groups. Results from this model are shown in Fig. 1. Emotional clarity was negatively associated with depressive symptoms, r =  − 0.39, p < 0.001, and positively associated with life satisfaction, r = 0.17, p < 0.001. Goal clarity was also negatively associated with depressive symptoms, r =  − 0.20, p = 0.01, and positively associated with life satisfaction, r = 0.29, p < 0.001. In line with our hypotheses, emotional clarity was more strongly linked with depression than was goal clarity, whereas goal clarity was more strongly linked to life satisfaction than was emotional clarity.

Fig. 1.

Fig. 1

Open in a new tab

Multigroup latent means and covariance model of emotional and goal clarity predicting subjective well-being with metric invariance constraints. Note. EC = emotional clarity item indicator, GC = goal clarity item indicator, Dep = depressive symptom item indicator, LS = life satisfaction item indicator. The triangle represents the mean structure (i.e., intercepts) of the model. Factor loadings, but not means, are constrained to be equal across groups in the metric model. Since metric invariance was met, covariance and regression paths were constrained to be equal across groups. Constraining covariance and regression paths to be equal did not significantly worsen model fit. All paths are statistically significant at p ≤ .01. Not all indicators are shown for visual clarity

Discussion

Consistent with our hypothesis, levels of emotional clarity were lowest among young adults and highest among older adults. Our key findings also include emotional clarity and goal clarity being moderately associated but distinguishable traits linked to measures of subjective well-being (in this case, life satisfaction and depressive symptoms) across adulthood. Our findings raise the possibility that interventions that increase emotional and goal awareness will be helpful for improving satisfaction with life and mitigating levels of depression across the lifespan.

Emotional clarity appeared to linearly increase across the age groups. Models of emotional aging, such as the strength and vulnerability integration model (Charles, 2010) and socioemotional selectivity theory (Carstensen, 1992), have been used to make predictions about age-related emotion regulation changes. Contrary to these predictions, studies examining age-related differences in emotion regulation strategies find few, if any, age differences in the types, number, or successful use of different strategies (e.g., Shiota & Levenson, 2009; Opitz et al., 2014; Eldesouky & English, 2018; Benson et al., 2019; Growney & English, 2020). However, age differences have been observed in processes that may contribute to emotion regulation. For example, older adults show greater attention to positive stimuli (Isaacowitz, 2012), engage in more problem-focused coping (Blanchard-Fields et al., 2004), and are less reactive to emotional stimuli (Luong & Charles, 2014). The results of the present study, along with the above findings, suggest it is processes that support successful emotion regulation, like emotional clarity, that change with age, rather than the types of strategies employed. Emotional clarity is important because to the extent emotions provide information (Clore et al., 2018) about one’s environment, internal state, and goal progress, having a clear understanding of one’s emotions should facilitate optimal use of that affective information for goal pursuit, emotion regulation, and coping.

Goal clarity also increased from the youngest to the middle group, but did not increase by a significant margin from the middle to the eldest group. It is possible that in emerging adulthood, goals are more transient and difficult to be clear about. Young adulthood is a period characterized by large increases in conscientiousness (Roberts & Mroczek, 2008), which is composed of traits like self-control and perseverance that should facilitate goal attainment. Increasing stability of one’s long-term goals and increasing levels of conscientiousness may explain why goal clarity only increased from the youngest to middle group. Despite levels not increasing later in adulthood, having clear goals appears to be more important living a satisfying life. Older adults, who had the highest levels of emotional clarity, may be best able to use their knowledge of their emotions to guide goal selection and pursuit. Being able to select clear goals based on knowledge of how attaining that goal will make one feel should have a greater positive impact on subjective well-being than will investing oneself in goals that have unknown emotional impact.

The results of the present research suggest that emotional clarity increases across adulthood. It is, of course, possible that a range of other processes, including at least some aspects of emotion regulation, increase over adulthood and may contribute to improved emotional health. We suggest that emotional clarity may be especially likely to increase with the accumulation of emotional experiences because it is thought to be a knowledge-based construct (Park et al., 2022) rather than reflecting habits (e.g., emotion regulation strategies) or beliefs (e.g., believing one’s emotions are overwhelming vs. controllable). Whether emotional clarity changes with age may depend on other factors such as another facet of emotional awareness, attention to emotions. Paying attention to one’s emotions and valuing them as a source of information may be foundational for developing understanding of what one feels. Likewise, more general metacognitive ability may contribute to greater emotional clarity over time. Finally, individual differences in affect valuation, or the extent to which one values their emotional experiences (Tsai, 2007) or believes their emotions to be useful (Chow & Berenbaum, 2012), could contribute to greater clarity over time. To the extent one views emotions as valuable or useful, they may be more likely to make efforts to understand the emotions they feel and their causes. Having a better understanding of the mechanisms driving this change could inform how to better construct interventions such as socioemotional skills taught in schools or emotional awareness training conducted during psychotherapy.

Limitations and Future Directions

Though the present results are cross-sectional, they provide initial evidence that there may be linear growth in levels of emotional clarity through adulthood. Longitudinal studies will be needed to provide further evidence of within-person change over time. Since the groups were compared cross-sectionally, it is possible that the results of the present study may be due to cohort effects. However, it is not clear why this would be the case. In fact, across generations, emotion socialization has become more prioritized in youth across informal (e.g., home) and formal (i.e., school) settings (Durlak et al., 2011) and seen as a strength (e.g., emotional intelligence; Mayer et al., 2008) that should be fostered. The youngest generations would likely have obtained more positive socialization about their emotions than the older generations, which could have resulted in greater emotional clarity among the youngest generation, not less. Consequently, it seems to us less plausible that our results are due to cohort effects rather than developmental differences.

It is worth noting that one limitation of the present study is the emerging adult sample was recruited differently than the young-to-middle and middle-to-older adult samples. There were a greater number of Asian and Asian American participants in the emerging adult group relative to the other groups. We found Asian and Asian American participants reported lower levels of life satisfaction relative to their peers, but no differences in levels of emotional or goal clarity, or depressive symptoms. When demographic covariates were added into analyses, none of the key results changed. Because the emerging adult sample was recruited through an undergraduate subject pool, they were possibly more homogenous in their levels of education and SES than the online samples. SES has been linked to subjective well-being (Tan et al., 2020), but emotional clarity is very weakly associated with SES (Mankus et al., 2016). Thus, it is unlikely that possible differences in SES confounded key results involving emotional or goal clarity.

Older adults were recruited through online sampling. Internet use has been linked to slower cognitive decline (Kilmova, 2016), less isolation, and greater feelings of connectedness (Cotton et al., 2013) among older adults. Thus, it is likely that the older adults sampled in this study are cognitively and emotionally healthier than what may be expected using an unselected community sample of older adults. It will be important to investigate levels of emotional clarity in older adults across a broader continuum of cognitive and emotional functioning.

Constructs in the present study were all measured using self-report. Though indirect measures of emotional clarity exist (e.g., using reaction times of self-reported emotions; Lischetzke et al., 2011), self-reports of emotional clarity are the most widely validated measures available. In fact, our analyses suggest the self-report measure has strong measurement invariance across the adult lifespan, further supporting its use. Boden and Berenbaum (2011) proposed that self-reports of emotional awareness (i.e., attending to and understanding one’s emotions) are most likely accessible through introspection given their meta-cognitive nature. Furthermore, emotional clarity and goal clarity were measured using a single instrument each, making it impossible to distinguish whether the factor structure observed in the present samples reflects the associations between the constructs themselves or the associations between the instruments used to measure the constructs. Future work should seek to replicate the two-factor structure using multiple and alternative measures of emotional and goal clarity.

Conclusion

Across three large samples, using the gold standard method of group comparison, we found strong evidence that emotional clarity increases with age from young to older adulthood. In line with socioemotional selectivity and affect-as-information theories, increases in emotional clarity may help to explain why older adults tend to experience greater emotional health. More work will be needed to understand how and why emotional clarity increases with age.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 36 KB) (35.8KB, docx)

Additional Information

Funding

This research was supported by the American Psychological Foundation Graduate Student Research Scholarship awarded to Nathaniel S. Eckland.

Conflicts of Interest

The authors declare no conflict of interest.

Data Availability

Data have been made available on OSF: https://osf.io/mw7ut/.

Code Availability

Code has been made available on OSF: https://osf.io/mw7ut/.

Author contributions

Both authors conceptualized the study; NSE attained funding and collected the data; NSE conducted the data analysis; both authors contributed to writing the manuscript; both authors approved the final version of the manuscript.

Ethics Approval

The research was approved by an Institutional Review Board.

Consent to Participate

All participants gave informed consent to be in this study.

Consent for Publication

Not applicable.

Footnotes

1

Data quality was assessed based on written responses in the personal striving task (described below). Data were removed in Sample 1 if they reported fewer than six personal strivings and in Sample 2 if they reported fewer than five personal strivings due to low effort. Participants who did not actually list personal strivings (e.g., copy and pasting instructions) were removed for poor data quality.

2

We wanted to rule out the possibility that the differences we observed were due to methodological differences (i.e., data from emerging adults was collected in-person, whereas the two older samples were collected online). Therefore, we tested measurement invariance across the two online samples, the young-to-middle and middle-to-older adult groups. These tests again indicated the scalar model could be interpreted. Results replicated what was found comparing the three groups: (1) middle-to-older adults showed higher levels of emotional clarity relative to young-to-middle adults, z = 4.03, p < .001; and (2) middle-to-older adults and young-to-middle adults did not significantly differ in levels of goal clarity, z = .58, p = .57.

3

We also tested models using gender and race/ethnicity as covariates. None of the results changed. Model summaries with covariates can be found in supplemental materials (see Table S2).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 36 KB) (35.8KB, docx)

Data Availability Statement

Data have been made available on OSF: https://osf.io/mw7ut/.

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