Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life

Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life Liam Le Guellaff Pallin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8775727/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The Self-Concordance Model posits that autonomous goal pursuit is a primary driver of well-being, yet standard measurement practices often conflate the presence of autonomy with the absence of external pressure via 'relative' difference scores. This study utilized a high-powered dataset ( N = 429) to systematically test between a Balance Model (where meaning is expected to be associated with the net difference between autonomous and controlled motivation, operationalized as Goal self-concordance) and an Autonomous-Primary Model (where meaning is driven solely by autonomous direction). Using Bayesian Structural Equation Modeling (BSEM) and Directed Acyclic Graphs (DAGs), the analysis indicated that the Goal self-concordance (GSC) score can obscure the true structural relationship between motivation and meaning. Results showed that autonomous motivation was the sole robust predictor of meaning in life judgements, while the GSC scores contributed no unique variance when both were tested simultaneously. A functional double dissociation emerged: autonomous motivation predicted meaning but not depression, while controlled motivation predicted depression but not meaning. Notably, the latent correlation between autonomous and controlled motivation was near zero ( r = − .09), supporting their status as orthogonal constructs rather than opposite ends of a continuum. The interaction between autonomous and controlled motivation was not credible, suggesting that the effect of autonomy on meaning is constant regardless of external pressure. These findings challenge the subtractive logic embedded in traditional self-concordance measurement, and suggest that meaning in life is a product of autonomous direction, not motivational balance. autonomous motivation meaning in life self-determination theory Bayesian structural equation modeling goal self-concordance Figures Figure 1 Figure 2 Figure 3 Introduction The search for meaning has long been recognized as fundamental to human functioning. Across research, perceiving one's life as coherent, significant, and purposeful is reliably associated with better psychological functioning and well-being, including fewer depressive symptoms and greater capacity to cope with adversity (Heintzelman & King, 2014; Hooker et al., 2018; Steger et al., 2006). Given these profound protective benefits, identifying the psychological processes that cultivate meaning is a central task for contemporary psychology. Central to the construction of meaning is the concept of motivation. Distinct from theories that view motivation merely as a quantitative measure of energy, Self-Determination Theory (SDT) posits that the quality or orientation of motivation is the decisive factor for well-being (Deci & Ryan, 2000). Specifically, the Self-Concordance Model posits that pursuing goals for autonomous reasons (reflecting genuine interest and personal values) is the primary driver of sustained effort and psychological well-being (Sheldon & Elliot, 1999). When action proceeds from a self-endorsed locus of causality ("I want to"; Deci & Ryan, 2000), the individual maintains a sense of coherent purpose. This directional quality aligns with what Martela and Steger (2016) identify as the purpose component of Meaning in Life. Defining Meaning Contemporary research views Meaning in Life as a tripartite construct comprising three interlinked dimensions: Coherence (making sense of life), Purpose (having overarching goals), and Mattering (feeling significant) (George & Park, 2016 ; Martela & Steger, 2016 ). However, recent work by Costin and Vignoles ( 2020 ) argues for a theoretical distinction between these cognitive precursors and the phenomenological experience of meaning itself. They suggest that while coherence, purpose, and mattering are the "pillars" that support meaning, they are distinct from the global subjective evaluation that one's life is meaningful. To capture this distinction, the present study utilizes the Multidimensional Meaning in Life Scale (MMLS; Costin & Vignoles, 2020 ), focusing specifically on General Meaning Judgements. This approach aims to capture the global sense that "my life has meaning," separating the feeling of meaning from its cognitive prerequisites. The Measurement Puzzle At an intuitive level, it makes sense that goals pursued from genuine interest or personal value would feel more meaningful than goals pursued from guilt, image concerns, or external rewards. The empirical question is not whether motivation and meaning are related, but what exactly is being measured when that relationship is tested. Most studies operationalize goal self-concordance as a single composite score, and this creates an ambiguity about what component of motivation is responsible for any observed association with meaning. Research in the self-concordance tradition regularly computes goal self-concordance (GSC) as a difference score: (intrinsic + identified) – (external + introjected) (Sheldon & Houser-Marko, 2001 ). In SDT terms, this corresponds to autonomous motivation, which reflects interest and personal value, minus controlled motivation, which reflects guilt or ego pressure and external rewards. Implicit in this formula is a subtractive assumption that external pressure reduces the net level of self-determined motivation captured by the score. This assumption aligns with the simplex or continuum view of motivation, in which regulations are ordered along a continuum of self-determination and can be summarized as relative autonomy (Howard et al., 2017 ). Because the formula subtracts controlled from autonomous motivation it collapses two potentially distinct drives into one index. A high goal self-concordance score can therefore reflect high autonomous motivation, low controlled motivation, or both. When studies report that the difference score predicts meaning, it remains unclear whether meaning is driven by the strength of autonomous motivation, the absence of controlled motivation, or their specific combination. However, this unidimensional approach has faced increasing scrutiny. Chemolli and Gagné ( 2014 ) present evidence that commonly used SDT motivation measures do not conform well to a single continuum structure, and they argue that autonomous and controlled motivations are better treated as distinct dimensions rather than strict opposites. If these motivational forms are not oppositional, then a difference score cannot isolate the source of an observed association with meaning. When research shows that GSC predicts meaning, the result is compatible with an account in which meaning depends on the relative balance between autonomous and controlled motives, and with an account in which meaning depends primarily on autonomous motivation while controlled motivation is largely irrelevant. Previous Research By subtracting one motivational form from the other, researchers may be obscuring the unique variance each contributes to meaning, potentially masking a reality where autonomous motivation drives meaning while controlled motivation drives distinct outcomes such as distress. Yet recent investigations continue to rely on this composite approach, leaving the specific role of controlled motivation opaque. For instance, Siwek et al. ( 2017 ) found that external motivation negatively predicted meaning in life, and they interpret externally pressured striving as exceptionally unfavorable for the experience of meaning. Similarly, Sangeorzan et al. ( 2024 ) recently utilized two samples of university students to examine goal motivation, meaning in life, and psychological symptomatology. Although controlled motivation is mathematically embedded within the Goal Self-Concordance (GSC) score used in their study, they did not model controlled motivation as a separable predictor. As a result, it remains unclear whether meaning in life is driven by the presence of autonomous motivation, the absence of controlled motivation, or their relative balance. This represents a significant gap in the literature. If meaning depends on balance, interventions must focus on reducing external pressure. However, if meaning depends on strength, interventions should focus primarily on cultivating autonomy, as the presence of pressure may be mentally and affectively draining but irrelevant to the generation of meaning. The Present Study The present study performed a secondary analysis of the Sangeorzan et al. ( 2024 ) data. Using Directed Acyclic Graphs (DAGs) and applied Bayesian data analysis, the study estimated the unique contribution of each motivational component. Specifically, this study addresses the following primary research question: Is the experience of meaning in life a function of the relative balance between autonomous and controlled motivation, or is it driven primarily by the magnitude of autonomous direction regardless of external pressure? Inspired by the logic that meaning is a product of directed coherence, the following hypotheses are proposed: H1 Autonomous motivation will be the primary robust predictor of meaning judgments. When tested simultaneously, autonomous motivation will remain significant, while the GSC will become non-significant. H2 : Controlled motivation will demonstrate a functional dissociation: it will strongly predict depressive symptoms (distress) but will demonstrate a negligible direct effect on meaning once distress is controlled for. H3 The positive effect of autonomous motivation on meaning will remain robust regardless of the level of controlled motivation (non-significant interaction), consistent with the interpretation that the autonomy–meaning association does not differ across levels of controlled motivation. H4 These findings will remain stable across different statistical approaches (Linear Regression and Bayesian Ordinal Regression) and when excluding influential outliers. This paper contributes a direct test of two competing interpretations embedded in self-concordance measurement: a subtractive ‘balance’ model versus an autonomous-primary model. By modeling autonomous and controlled motivation as separable predictors, rather than collapsing them into a relative difference score, we clarify which component accounts for variance in meaning in life judgments and which component primarily tracks depressive symptomatology. Methods Research Design This study employed a quantitative, cross-sectional design to investigate the relationship between motivational quality and meaning in life. A cross-sectional approach was selected as the primary aim was to evaluate the structural validity of competing theoretical models (Balance vs. Strength) rather than to establish temporal causality. However, to rigorously evaluate between these theories, causal structures were modeled explicitly using Directed Acyclic Graphs (DAGs), making the assumed causal structure explicit and guiding covariate adjustment. Participants To ensure robust statistical power and replicability, the study utilized secondary data aggregated from two independent samples originally collected by Sangeorzan et al. ( 2024 ) comprising 437 participants. Sample 1 consisted of undergraduate students from California State University, Sacramento (CSUS; n = 168). Sample 2 consisted of undergraduate students from Washington State University (WSU; n = 269). Given that both samples utilized identical measures and procedures, the data obtained from Sample 1 and Sample 2 were aggregated to form a high-powered combined dataset. Power Analysis An a priori Monte Carlo power analysis indicated that a sample size of N = 133 is required to achieve a power of .80 for the typical small to medium effect sizes found in this domain. The current combined sample ( N = 429) exceeds this threshold by a factor of three, providing excellent sensitivity to determine even small effects. Final Sample Following the exclusion of participants with incomplete data on the primary variables ( n = 8), the final analytic sample was N = 429. The sample was predominantly female (82.5%), with 16.3% male participants, and 1.2% other/undisclosed. Ages ranged from 18 to 54 years ( M = 22.48, SD = 6.53). The sample was ethnically diverse, drawing from two distinct university populations in the Western United States. Procedure Data collection occurred online via the SONA research management systems at the respective universities. Participants received course credit for their participation. Upon accessing the survey link, participants were presented with an informed consent form. This form explicitly detailed: (1) the voluntary nature of the study; (2) the guarantee of anonymity and confidentiality; (3) the potential risks (minimal); and (4) the right to withdraw at any time without penalty. After providing digital consent, participants completed a battery of self-report measures. Crucially, the presentation order of the questionnaires was randomized for each participant. This procedural control mitigates potential order effects and fatigue bias, enhancing the internal validity of the measurement. Following the completion of the measures, participants were presented with a demographic questionnaire and a debriefing form explaining the purpose of the study. The original data collection was approved by the Institutional Review Boards of both universities (IRB #: Cayuse-21-22-160 and IRB # 20058-001). Measures Goal Motivation Motivation was assessed using the Self-Concordance Scale (Sheldon & Elliot, 1999 ). Participants listed six current personal goals and rated their reasons for pursuing each on a scale from 1 ( never for this reason ) to 7 ( always for this reason ). The scale measures four motivational regulatory dimensions: intrinsic (enjoyment), identified (personal value), introjected (guilt/ pressure), and external (rewards/situation). Consistent with standard self-concordance goal-striving methodology (Sheldon & Elliot, 1999 ; Sheldon & Houser-Marko, 2001 ), three composite variables were calculated: Autonomous Motivation: The sum of intrinsic and identified ratings ( M = 69.75, SD = 9.42). Controlled Motivation: The sum of introjected and external ratings ( M = 45.62, SD = 14.02). Goal self-concordance (GSC) score: Calculated as a difference score ((Intrinsic + Identified) – (External + Introjected)), which is equivalent to (Autonomous – Controlled) given the composites above, representing net (difference-score) autonomy ( M = 24.13, SD = 17.45). Meaning in Life Meaning was assessed using the General Meaning in Life Judgements subscale of the Multidimensional Meaning in Life Scale (MMLS; Costin & Vignoles, 2020 ). While the full MMLS assesses three cognitive dimensions (Coherence, Purpose, Mattering), this study utilized only the Judgements subscale (4 items) to capture the phenomenological experience of meaning itself (e.g., "My life as a whole has meaning"). Items were rated on a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree ). The subscale demonstrated excellent internal consistency (α = .93). Control Variables To assess the unique contribution of motivation to meaning accounting for the variance shared with general distress, depressive symptomatology was included as a covariate. It was assessed using the CESD-10 (Andresen et al., 1994 ), a 10-item frequency scale (0–3) which has been shown to have good internal consistency (α = .83; Andresen et al., 1994 ). Data Analysis Statistical analyses were conducted in R (R Core Team, 2025 ), utilizing the lavaan (Rosseel, 2012 ) and blavaan (Merkle & Rosseel, 2018 ) packages for structural equation modeling. The analysis proceeded in stages: starting with traditional frequentist regression, then Structural Equation Modeling (SEM), and finally Bayesian item-level analysis. Importantly, this study re-examines the Sangeorzan et al. ( 2024 ) dataset. Whereas the original authors utilized frequentist approaches and binary significance testing (' p < .05'), this study adopts a Bayesian framework. Instead of forcing a strict 'significant vs. non-significant' outcome which makes it difficult to prove that an effect does not exist, Bayesian analysis calculates direct probabilities via Credible Intervals. This offers a more intuitive quantification of uncertainty, allowing us to see not just if an effect exists, but exactly how likely (or unlikely) it is. Primary Hypothesis Testing (Linear Regression) To test the competing utility of the Balance (GSC score) versus Strength (Autonomous) models (H1 and H2), hierarchical linear regression models were constructed. Incremental validity was assessed by examining the change in explained variance (Δ R² ) when adding the GSC score to a model already containing Autonomous motivation, and vice versa. Controlled motivation was also entered as an independent predictor to test its unique contribution to meaning judgements (H2). Bayesian Structural Equation Modeling (BSEM) To compare causal structures (H3) while accounting for measurement error, Bayesian SEM was employed using blavaan (Merkle & Rosseel, 2018 ). Three Directed Acyclic Graphs (DAGs; see Fig. 1 ) were specified: (1) a single-index model using the GSC score; (2) a simplified Autonomous-Only model (where the path from controlled motivation is constrained to zero); and (3) a dual-pathway model where Autonomous and Controlled motivation function as separable predictors. Following the framework of causal inference (Pearl, 2009 ; Rohrer, 2018a ), this approach forces implicit theoretical assumptions to be made explicit and mathematically falsifiable. To address the nested structure of the GSC data (6 goals per participant), an item-parcelling approach was utilized. For each of the six goals, an 'Autonomous' parcel (mean of Intrinsic and Identified items) and a 'Controlled' parcel (mean of External and Introjected items) were computed. This goal-level strategy preserves the ecological structure of the data while reducing model complexity, and is supported by methodological research demonstrating improved parameter stability (Little et al., 2002 ). The models were estimated using Markov Chain Monte Carlo (MCMC) sampling with 3 chains, 1,000 burn-in iterations, and 1,000 sampling iterations. Convergence was verified using the potential scale reduction factor ( R̂ < 1.01; Gelman & Rubin, 1992 ) and effective sample sizes exceeding 1,000 for all parameters (Vehtari et al., 2021 ). Note Three competing causal structures were specified for comparison. Model A (DAG 1) represents a traditional single-index framework using the Goal self-concordance (GSC) score. Model B (DAG 2) represents the proposed ‘Autonomous-Only’ model, where the path from Controlled motivation is constrained to zero. Model C (DAG 3) represents a Dual-Pathway model where Autonomous and Controlled motivation function independently. Bayesian Item-Level Analysis To address the limitations of summing Likert-scale data and to account for the ordinal nature of the psychological measures (H4), a Bayesian multilevel ordinal regression was conducted using the brms package (Bürkner, 2017 ). This addresses a common limitation in psychological research where 1–7 survey scales are treated as if they were continuous metric values, as seen in Sangeorzan et al. ( 2024 ). Standard regression relies on the assumption of equidistance, presuming that the psychological distance between every rating point is identical (e.g., that the step from ' Neutral ' to ' Agree ' is the same size as ' Agree ' to ' Strongly Agree '). The ordinal model avoids this unwarranted assumption by calculating the specific spacing between response categories based on the data. Rather than enforcing a rigid scale where every step is equal, this approach acknowledges that the psychological shift required to move from ' Neutral ' to ' Agree ' is not necessarily the same size as the shift from ' Agree ' to ' Strongly Agree ’. Cumulative Probit models were fitted for both the outcome (Meaning in Life) and the primary covariate (Depression). These models included random intercepts for both participants and items, thereby accounting for participant response styles and specific item characteristics. Weakly informative priors were used to regularize estimates. Model comparison was conducted using Leave-One-Out Cross-Validation (LOO-IC) to determine which theoretical predictor (GSC vs. Autonomous motivation) provided the best out-of-sample prediction at the item level. Following McElreath ( 2020 ), the ordinal response is defined as: R ij ~ Ordered-probit (Φ i , κ) where Φ i is the linear predictor (β Auto · Auto i + β Ctrl · Ctrl i + u 0 i + u 0 j ), κ represents the K – 1 ordered cutpoints, and u 0 i and u 0 j are random intercepts for participants and items, respectively (Bürkner & Vuorre, 2019 ). The model was fitted using Stan (v2.37; Stan Development Team, 2025 ) via the brms package (Bürkner, 2017 ). Default brms priors were used for all parameter estimates. Model fit and convergence was assessed using posterior predictive checks and diagnostics from the Stan sampler. Robustness Checks Finally, to ensure findings were not driven by influential observations, sensitivity analyses were conducted by calculating Cook's Distance. Models were re-estimated after excluding 28 participants exceeding the threshold of 4/ n . Data and Code Availability Data analysis was conducted using the combined dataset (N = 429) to maximize statistical power. To maintain anonymity during peer review, analysis code and supporting materials are hosted in a blinded repository and will be made publicly available upon acceptance. Use of AI-assisted tools AI-based tools were used to assist with the development, testing, and debugging of statistical analysis code (e.g., syntax support and workflow troubleshooting). All analytical decisions (model specifications, priors, inclusion/exclusion choices), interpretation of results, and reporting were performed and verified by the author, who takes full responsibility for the analyses and conclusions. Results Descriptive statistics and zero-order correlations for all study variables are presented in Table 1 . Consistent with theoretical expectations, Autonomous motivation was positively correlated with Meaning in Life judgements ( r = .35, p < .001). Controlled motivation showed a significant negative bivariate correlation with Meaning ( r = − .23, p < .001); however, as subsequent analyses reveal, this relationship is fully accounted for by depressive symptoms in subsequent models. Table 1 Descriptive statistics and zero-order correlations Variable M SD 1 2 3 4 5 Meaning in life 22.38 5.23 (.93) Autonomous motivation 69.75 9.42 .35*** - - Controlled motivation 45.62 14.02 − .23*** − .07 - - Goal self-concordance (GSC) 24.11 17.45 .37*** .60*** − .84*** - - Depression (CESD-10) 12.27 6.02 − .59*** − .17*** .30*** − .33*** (.83) Note. N = 429. Reliability coefficients (α) are presented on the diagonal. *** p < .001. Primary Hypothesis Testing: Balance vs. Strength (H1 & H2) To test the competing utility of the GSC score versus Autonomous motivation (H1), hierarchical linear regression models were constructed. The results of the comparative analysis are presented in Table 2 . In the critical test (Model 3), when both predictors were entered simultaneously, Autonomous motivation remained a robust predictor of Meaning judgements ( b = 0.12, SE = 0.03, p < .001). In contrast, the GSC score became non-significant ( b = 0.02, SE = 0.02, p = .215). Analysis of incremental validity confirmed this pattern. Adding Autonomous motivation to a model already containing GSC resulted in a significant increase in explained variance (Δ R ² = .031, p < .001). However, adding GSC to a model already containing Autonomous motivation yielded a negligible and non-significant increase (Δ R ² = .002, p = .215). Semi-partial correlation analysis indicated that Autonomous motivation explained approximately 14.4 times more unique variance in Meaning than the GSC composite. These findings provide full support for H1, suggesting that the predictive power of the GSC score is driven almost exclusively by its autonomous component. Furthermore, the unique contribution of Controlled motivation was examined (H2). When added to a model controlling for Autonomous motivation, Controlled motivation was not a significant predictor of Meaning ( b = -0.02, p = .22) and explained only 0.21% of the variance. So, H2 was supported: controlled motivation appears irrelevant to the subjective judgement of meaning. Table 2 Hierarchical regression analysis predicting meaning in life Judgements Predictor Model 1 (Autonomous) Model 2 (GSC) Model 3 (Both) b SE b SE b SE Step 1: Covariates Depression − .47*** .05 − .46*** .05 − .46*** .05 Age − .01 .03 − .02 .05 − .01 .03 Sex .90 .48 1.04 .49 .88 .48 Step 2: Motivation Autonomous .14*** .02 - - - - 0.12*** .03 GSC - - - - .06*** .01 0.02 .01 R 2 .41 .38 .41 ΔR 2 .06*** .03*** .00 Note. N = 429. b = Unstandardized coefficient. SE = Standard error of unstandardized b . Model 3 tests whether GSC adds beyond Autonomous. *** p < .001. Interaction Analysis To test whether the effect of autonomous motivation on meaning is dependent on the level of external pressure, a linear model was tested including an interaction term between Autonomous and Controlled motivation. The interaction term was non-significant both with depression included ( b = .002, p = .269) and without it ( b = .002, p = .183). This suggests that the positive predictive value of autonomous motivation is robust and does not vary as a function of controlled motivation. Structural Model Comparison (H3) To compare causal structures while accounting for measurement error, Bayesian SEM was conducted. The motivational measurement model using goal-level parcels demonstrated excellent fit (CFI = .959, RMSEA = .047). The latent correlation between Autonomous and Controlled motivation was negligible ( r = − .09, 95% CI [-.21, .04]), confirming that these constructs function as orthogonal dimensions rather than opposite ends of a continuum. In the structural comparison, the Dual-Pathway DAG provided a definitive test of the causal architecture (see Fig. 2 ). When modeled simultaneously with Depression and demographic covariates: Autonomous motivation had a strong, credibly positive association with Meaning ( b = 0.41, 95% CI [0.28, 0.55], β = .28), and Controlled motivation had no credible effect on Meaning, with a credible interval centered on zero ( b = -0.04, 95% CI [-0.17, 0.10]). The model revealed why prior studies may have observed negative correlations: Controlled motivation was a credible predictor of Depression ( b = 0.37, 95% CI [0.25, 0.50], β = .34), and Depression was a strong negative predictor of Meaning ( b = -0.81, β = − .61). Once this confounding pathway (Distress) was controlled for in the structural model, the direct path from Controlled motivation to Meaning vanished. These results fully support H3, indicating that the 'Autonomous-Only' structure is the most parsimonious representation of the data. Bayesian Item-Level Analysis (H4) To assess robustness while accounting for the ordinal nature of Likert-scale data, a Bayesian multilevel cumulative probit analysis was conducted. Model comparison indicated a statistical tie between the Autonomous-Only and Dual-Predictor models (Δelpd = 0.00, SE = 1.18). Following the principle of parsimony, the simpler model is preferred. Furthermore, examination of the posterior distributions in the dual model confirmed the SEM findings (see Fig. 3 ): the parameter for Autonomous motivation was credibly positive (β = 0.07, 95% CI [0.05, 0.09]), while the parameter for Controlled motivation was not credibly different from zero (β = -0.007, 95% CI [-0.019, 0.005]). This confirms the findings (H1 and H2) are not artifacts of treating ordinal data as metric sums; patterns hold even when modeling raw item-level probabilities. Note Values represent unstandardized posterior estimates. The path from Controlled Motivation to Meaning includes zero (dashed line), indicating no credible effect, while the path to Depression is significant. *** indicate that the effect is credible. Note Horizontal lines represent the 95% Credible Intervals. The Controlled Motivation interval crosses zero, indicating no credible effect. Secondary Analysis: Construct Validity Finally, to ensure that the null result for Controlled motivation was not due to measurement failure, its relationship was examined with Depressive symptoms (CESD-10). In the Bayesian model, Controlled motivation was a credible positive predictor of depression (β = 0.013, 95% CI [0.009, 0.017]), while Autonomous motivation acted as a credible protective factor (β = -0.009, 95% CI [-0.015, -0.003]). This double dissociation validates the measurement: Controlled motivation is functionally active and predicts distress, yet remains structurally irrelevant to the experience of Meaning. Robustness Checks Sensitivity analyses confirmed that the exclusion of 28 influential outliers ( N = 401) did not alter the observed pattern of results. In the re-estimated models, Autonomous motivation remained the sole robust predictor of meaning, while the effects of the GSC score and Controlled motivation remained negligible. Discussion Psychological theory often assumes that well-being arises from motivational concordance: the degree to which our actions align with our values. Embedded in the traditional measurement of this construct is a subtractive assumption: that external pressure mathematically degrades autonomous motivation. This treats motivation as a balance sheet, where obligation cancels out volition. The present study tested whether meaning in life is best predicted by the relative balance of motivational drives (GSC) or by the strength of autonomous motivation alone. Across multiple analytical approaches, the results converged on a clear answer: meaning is strongly associated with autonomous motivation, not relative balance. The Primacy of Autonomous Motivation The primary hypothesis, that autonomous motivation would emerge as the dominant predictor of meaning, was strongly supported. When the GSC score was included alongside autonomous motivation in hierarchical models, the GSC score contributed no unique variance ( b = 0.02, p = .215). The Bayesian structural model confirmed this pattern, showing that the unique contribution of the balance score centered near zero. This contrasts with the original analysis by Sangeorzan et al. ( 2024 ), which used the composite GSC score to predict well-being. This decomposition suggests that the predictive power they observed appears to have been driven primarily by the autonomous component, not the relative balance itself. By using a difference score, previous research obscured the fact that the positive variance appears to reflect wanting to pursue a goal, not from the absence of pressure to do so. Autonomous and Controlled Motivation as Distinct Constructs A central assumption of standard SDT measurement is that autonomy and control exist on a continuum. However, the data support the critique by Chemolli and Gagné ( 2014 ), who argue that these forms of motivation are qualitatively distinct. The Bayesian measurement model estimated the latent correlation between autonomous and controlled motivation to be virtually zero ( r = − .09), indicating they function as orthogonal dimensions rather than opposite ends of a spectrum. This undermines the psychometric rationale for the GSC formula. A person can simultaneously experience strong autonomous motivation and strong external pressure. By collapsing these distinct dimensions into a single score, the traditional formula discards information about their independent effects. Resolving the Distress Confound The structural model helps explain a persistent finding in the literature: the apparent negative association between controlled motivation on meaning. Previous studies (e.g., Siwek et al., 2017 ) found that controlled motivation negatively predicted meaning, leading to the conclusion that pressure is harmful for well-being. The results suggest these findings reflected a confound with distress. A functional double dissociation was observed: Autonomous motivation predicted meaning but not depression; Controlled motivation predicted depression but not meaning. Notably, once the path to depression was accounted for, the estimated direct effect of controlled motivation on meaning vanished (β = -0.04, 95% CI crossing zero). This aligns with distinctions between hedonic and eudaemonic well-being (Huta & Ryan, 2010 ). Controlled motivation is associated with distress, but it shows no direct association with meaning. It creates psychological cost, but it does not block the sense that one's pursuits are worthwhile. No Evidence For an Undermining Effect The subtractive assumption was directly tested via interaction analysis. If controlled motivation undermines autonomous motivation, the positive association between autonomy and meaning should weaken at higher levels of pressure. The interaction was non-significant ( p > .18). This null result has substantive meaning: the observed relationship between Autonomous motivation and Meaning does not appear to vary across pressure levels. An individual experiencing high external pressure derives the same Meaning benefit, as indexed by the autonomy-meaning association, from their autonomous motivation as someone experiencing low pressure: The strength of association of autonomous motivation into meaning does not depend on perceived pressure. The results of the present study are inconsistent with the subtractive logic embedded in the GSC formula, which assumes controlled motivation is associated with lower autonomous engagement. The data suggest instead that pressure and meaning operate on parallel tracks—controlled motivation adds cost (distress) without diminishing the strength of the autonomy-meaning association. Methodological Implications This study illustrates the advantages of Bayesian modeling over traditional frequentist approaches. While the regression models identified the redundancy of the GSC score, frequentist inference is limited to "failing to reject" the null, leaving ambiguity about whether an effect is truly absent or simply undetected. The Bayesian posterior distributions showed a credible interval for controlled motivation tightly centered on zero, supporting the conclusion with confidence that the direct effect is negligible rather than merely ‘non-significant’. Furthermore, ordinal models confirmed that these findings are not artifacts of treating Likert scales as continuous. The primacy of autonomous motivation held even when modeling raw item-level response probabilities, suggesting this pattern is unlikely to be an artifact of treating Likert scales as continuous. Limitations and Future Research The primary limitation is the cross-sectional design, which prevents establishing temporal causality. While Directed Acyclic Graphs were used to make causal assumptions explicit, it is not possible to rule out unobserved confounders. For instance, acute life stressors could simultaneously generate feelings of pressure and depressive symptoms. The observed relationship between controlled motivation and depression might therefore be confounded rather than causal. Longitudinal research is needed to determine whether pressure precedes increases in distress or whether stressful environments simply activate both. In addition, all focal constructs were assessed via self-report, which introduces the possibility of common-method variance and shared response tendencies (e.g., negative affectivity) inflating associations. Further, meaning in life was operationalized as a subjective global judgment rather than an objective or metaphysical construct; the present findings therefore speak to perceived meaning and its correlates, not meaning per se. Multi-method work (e.g., informant reports, behavioral indicators, or longitudinal designs) would help clarify the robustness and temporal ordering of these relationships. A further limitation concerns the sample characteristics. While the sample was adequately powered ( N = 429) and ethnically diverse, it consisted primarily of university students. For students, controlled motivation often reflects social expectations or academic requirements. For populations facing severe economic hardship, controlled motivation may be a matter of survival. Whether the independence of pressure and meaning holds in such contexts remains an open question. Additionally, both samples originated from universities in the United States, and the meaning of autonomous versus controlled motivation may differ across cultural contexts where collectivist values shape goal pursuit differently. Another consideration relates to the operationalization of meaning in life. This study measured meaning via global judgements rather than the tripartite dimensions (coherence, purpose, mattering). While analyzing specific dimensions can offer granular insights, I focused on global judgements to capture the holistic phenomenological experience of Meaning in Life (Costin & Vignoles, 2020 ). Additionally, because our primary predictor (GSC) is inherently goal-oriented, there is a risk of conceptual overlap when predicting the specific 'Purpose' dimension. By focusing on the global judgement, I aimed to test whether autonomous goals translate into a broader sense of existential significance, distinct from the mere presence of goal-directedness itself. Furthermore, the causal direction between controlled motivation and depression cannot be empirically adjudicated with cross-sectional data since the models are statistically equivalent. The specified direction (controlled motivation → depression) follows theoretical precedent in SDT, but the reverse pathway where depressive symptoms lead individuals to perceive their goals as more externally pressured, remains plausible and would require longitudinal data to distinguish. Finally, these findings raise questions about the temporal dynamics of motivation. Experience-sampling research could investigate whether the sense of meaning remains stable during moments of high pressure, or whether it fluctuates with immediate distress. This would clarify whether meaning functions as a stable readout of autonomous direction or as a momentary affective state. Conclusion Nietzsche (as cited in Frankl, 1959 ) wrote, "he who has a why to live can bear almost any how". The present findings are consistent with this insight. The experience of meaning in life is not a delicate equilibrium that must be maintained by minimizing pressure. Rather, it appears to be a stable product of authentic engagement. External demands may create distress, but they do not subtract from the meaningfulness of our pursuits. Interpreted through SDT, Nietzsche's "why", as used by Frankl, corresponds to autonomous motivation; the sense that one's pursuits genuinely matter. The "how", the difficulty, the pressure, the cost, corresponds to controlled motivation, which generates distress but leaves meaning intact. Meaning is not a product of motivational balance, but of autonomous direction. To increase meaning, we do not need to remove the pressure—we need to clarify the direction. Declarations Author Contribution LLGP conceived the study, conducted the analyses, and wrote the manuscript. Acknowledgement The author expresses gratitude to Geoffrey Patching for valuable academic guidance, trust, and intellectual freedom throughout the development of this work. Data Availability The data analyzed in this study were obtained from an existing dataset. Analysis code and supporting materials will be made publicly available upon acceptance. References Andresen, E. M., Malmgren, J. A., Carter, W. B., & Patrick, D. L. (1994). Screening for depression in well older adults: Evaluation of a short form of the CES-D. American Journal of Preventive Medicine , 10 (2), 77–84. https://doi.org/10.1016/S0749-3797(18)30622-6 Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software , 80 (1), 1–28. https://doi.org/10.18637/jss.v080.i01 Bürkner, P. C., & Vuorre, M. (2019). Ordinal regression models in psychology: A tutorial. Advances in Methods and Practices in Psychological Science , 2 (1), 77–101. https://doi.org/10.1177/2515245918823199 Chemolli, E., & Gagné, M. (2014). Evidence against the continuum structure underlying motivation measures derived from self-determination theory. Psychological Assessment , 26 (2), 575–585. https://doi.org/10.1037/a0036212 Costin, V., & Vignoles, V. L. (2020). Meaning is about mattering: Evaluating coherence, purpose, and existential mattering as precursors of meaning in life judgments. Journal of Personality and Social Psychology , 118 (4), 864–884. https://doi.org/10.1037/pspp0000225 Deci, E. L., & Ryan, R. M. (2000). The what and why of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry , 11 (4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01 Frankl, V. E. (1959). Man's search for meaning . Beacon Press. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical Science , 7 (4), 457–472. https://doi.org/10.1214/ss/1177011136 George, L. S., & Park, C. L. (2016). Meaning in life as comprehension, purpose, and mattering: Toward integration and new research questions. Review of General Psychology , 20 (3), 205–220. https://doi.org/10.1037/gpr0000077 Heintzelman, S. J., & King, L. A. (2014). Life is pretty meaningful. American Psychologist , 69 (6), 561–574. https://doi.org/10.1037/a0035049 Hooker, S. A., Masters, K. S., & Park, C. L. (2018). A meaningful life is a healthy life: A conceptual model linking meaning and meaning salience to health. Review of General Psychology , 22 (1), 11–24. https://doi.org/10.1037/gpr0000115 Howard, J. L., Gagné, M., & Bureau, J. S. (2017). Testing a continuum structure of self-determined motivation: A meta-analysis. Psychological Bulletin , 143 (12), 1346–1377. https://doi.org/10.1037/bul0000125 Huta, V., & Ryan, R. M. (2010). Pursuing pleasure or virtue: The differential and overlapping well-being benefits of hedonic and eudaimonic motives. Journal of Happiness Studies , 11 (6), 735–762. https://doi.org/10.1007/s10902-009-9171-4 Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling , 9 (2), 151–173. https://doi.org/10.1207/S15328007SEM0902_1 Martela, F., & Steger, M. F. (2016). The three meanings of meaning in life: Distinguishing coherence, purpose, and significance. The Journal of Positive Psychology , 11 (5), 531–545. https://doi.org/10.1080/17439760.2015.1137623 McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.).. CRC. Merkle, E. C., & Rosseel, Y. (2018). blavaan: Bayesian structural equation models via parameter expansion. Journal of Statistical Software , 85 (4), 1–30. https://doi.org/10.18637/jss.v085.i04 Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge University Press. R Core Team (2025). R : A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ Rohrer, J. M. (2018a). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science , 1 (1), 27–42. https://doi.org/10.1177/2515245917745629 Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software , 48 (2), 1–36. https://doi.org/10.18637/jss.v048.i02 Sangeorzan, P. C., Goodson, W. L., & Bohon, L. M. (2024). The why to bear any how: Goal self-concordance, meaning, and depressive and anxious symptomatology. International Journal of Applied Positive Psychology , 9 (4), 879–898. https://doi.org/10.1007/s41042-024-00158-1 Sheldon, K. M., & Elliot, A. J. (1999). Goal striving, need satisfaction, and longitudinal well-being: The self-concordance model. Journal of Personality and Social Psychology , 76 (3), 482–497. https://doi.org/10.1037/0022-3514.76.3.482 Sheldon, K. M., & Houser-Marko, L. (2001). Self-concordance, goal attainment, and the pursuit of happiness: Can there be an upward spiral? Journal of Personality and Social Psychology , 80 (1), 152–165. https://doi.org/10.1037/0022-3514.80.1.152 Siwek, Z., Oleszkowicz, A., & Słowińska, A. (2017). Values Realized in Personal Strivings and Motivation, and Meaning in Life in Polish University Students. Journal of Happiness Studies , 18 , 549–573. https://doi.org/10.1007/s10902-016-9737-x Stan Development Team (2025). Stan modeling language users guide and reference manual (Version 2.37). https://mc-stan.org Steger, M. F., Frazier, P., Oishi, S., & Kaler, M. (2006). The meaning in life questionnaire: Assessing the presence of and search for meaning in life. Journal of Counseling Psychology , 53 (1), 80–93. https://doi.org/10.1037/0022-0167.53.1.80 Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., & Bürkner, P. C. (2021). Rank-normalization, folding, and localization: An improved R̂ for assessing convergence of MCMC. Bayesian Analysis , 16 (2), 667–718. https://doi.org/10.1214/20-BA1221 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 03 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8775727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591314916,"identity":"fdc759c2-019c-428d-8e56-366190ec2f18","order_by":0,"name":"Liam Le Guellaff Pallin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIie3PMU7DMBTG8S+yFJaAV0/JFRxFgqJyGGehC0hMqFPiKWfo0EN06oqtJ3WKmBmRujIYdWXALkwIR3Rj8H+ILCu/vBcglfqHXTDujJMzf2QmPMJlZqZIzlDb1YMIRwUTSA5ME6ChwgVSyD+SMyi7kqIEHw+H92FeXlUaVKArJxYz/l9EA3G/FXZYNNcDAqEmTjIdprRPL+db2IHaze5ITKujhPkXpOg1H/fOk/6bdH2c5EeigDv4xUjJL8JUfLECYbFai9tLMT4v6s2u1XYtqY5N4Xxkzn3cVOC0d8vHeSWJ7OvbsqtiU34r89+Xp4BUKpVK/ewTQ9BT/quaPHEAAAAASUVORK5CYII=","orcid":"","institution":"Lund University","correspondingAuthor":true,"prefix":"","firstName":"Liam","middleName":"Le Guellaff","lastName":"Pallin","suffix":""}],"badges":[],"createdAt":"2026-02-03 12:23:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8775727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8775727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104911524,"identity":"0dfad6e8-5119-4b9d-8f7e-d0b19d0a4881","added_by":"auto","created_at":"2026-03-18 15:16:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102584,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized Structural Models of Motivation and Meaning\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Three competing causal structures were specified for comparison. Model A (DAG 1) represents a traditional single-index framework using the Goal self-concordance (GSC) score. Model B (DAG 2) represents the proposed ‘Autonomous-Only’ model, where the path from Controlled motivation is constrained to zero. Model C (DAG 3) represents a Dual-Pathway model where Autonomous and Controlled motivation function independently.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8775727/v1/4135d3452ef87b0d72af06e0.png"},{"id":104911525,"identity":"dc720655-fa84-4de1-b992-71dd50468ef9","added_by":"auto","created_at":"2026-03-18 15:16:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97612,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian Path Analysis of the Dual-Pathway Model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eValues represent unstandardized posterior estimates. The path from Controlled Motivation to Meaning includes zero (dashed line), indicating no credible effect, while the path to Depression is significant. *** indicate that the effect is credible.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8775727/v1/e4540067b8de5ae1e021e9b5.png"},{"id":104911526,"identity":"e977f7c0-913e-407c-9f25-c40258e15082","added_by":"auto","created_at":"2026-03-18 15:16:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96279,"visible":true,"origin":"","legend":"\u003cp\u003ePosterior Distributions of Motivational Predictors on Meaning in Life\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eHorizontal lines represent the 95% Credible Intervals. The Controlled Motivation interval crosses zero, indicating no credible effect.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8775727/v1/812838d971ddbad7db692794.png"},{"id":105034509,"identity":"d37c4919-5a4d-46ea-b41b-d77be9f5ccce","added_by":"auto","created_at":"2026-03-20 07:23:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8775727/v1/3f045c87-245b-49e3-a788-6a979eb114f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe search for meaning has long been recognized as fundamental to human functioning. Across research, perceiving one\u0026apos;s life as coherent, significant, and purposeful is reliably associated with better psychological functioning and well-being, including fewer depressive symptoms and greater capacity to cope with adversity (Heintzelman \u0026amp; King, 2014; Hooker et al., 2018; Steger et al., 2006). Given these profound protective benefits, identifying the psychological processes that cultivate meaning is a central task for contemporary psychology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCentral to the construction of meaning is the concept of motivation. Distinct from theories that view motivation merely as a quantitative measure of energy, Self-Determination Theory (SDT) posits that the quality or orientation of motivation is the decisive factor for well-being (Deci \u0026amp; Ryan, 2000). Specifically, the Self-Concordance Model posits that pursuing goals for autonomous reasons (reflecting genuine interest and personal values) is the primary driver of sustained effort and psychological well-being (Sheldon \u0026amp; Elliot, 1999). When action proceeds from a self-endorsed locus of causality (\u0026quot;I want to\u0026quot;; Deci \u0026amp; Ryan, 2000), the individual maintains a sense of coherent purpose. This directional quality aligns with what Martela and Steger (2016) identify as the purpose component of Meaning in Life.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDefining Meaning\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eContemporary research views Meaning in Life as a tripartite construct comprising three interlinked dimensions: Coherence (making sense of life), Purpose (having overarching goals), and Mattering (feeling significant) (George \u0026amp; Park, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martela \u0026amp; Steger, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, recent work by Costin and Vignoles (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argues for a theoretical distinction between these cognitive precursors and the phenomenological experience of meaning itself. They suggest that while coherence, purpose, and mattering are the \"pillars\" that support meaning, they are distinct from the global subjective evaluation that one's life is meaningful. To capture this distinction, the present study utilizes the Multidimensional Meaning in Life Scale (MMLS; Costin \u0026amp; Vignoles, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), focusing specifically on General Meaning Judgements. This approach aims to capture the global sense that \"my life has meaning,\" separating the feeling of meaning from its cognitive prerequisites.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe Measurement Puzzle\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAt an intuitive level, it makes sense that goals pursued from genuine interest or personal value would feel more meaningful than goals pursued from guilt, image concerns, or external rewards. The empirical question is not whether motivation and meaning are related, but what exactly is being measured when that relationship is tested. Most studies operationalize goal self-concordance as a single composite score, and this creates an ambiguity about what component of motivation is responsible for any observed association with meaning. Research in the self-concordance tradition regularly computes goal self-concordance (GSC) as a difference score: (intrinsic\u0026thinsp;+\u0026thinsp;identified) \u0026ndash; (external\u0026thinsp;+\u0026thinsp;introjected) (Sheldon \u0026amp; Houser-Marko, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In SDT terms, this corresponds to autonomous motivation, which reflects interest and personal value, minus controlled motivation, which reflects guilt or ego pressure and external rewards.\u003c/p\u003e \u003cp\u003eImplicit in this formula is a subtractive assumption that external pressure reduces the net level of self-determined motivation captured by the score. This assumption aligns with the simplex or continuum view of motivation, in which regulations are ordered along a continuum of self-determination and can be summarized as relative autonomy (Howard et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Because the formula subtracts controlled from autonomous motivation it collapses two potentially distinct drives into one index. A high goal self-concordance score can therefore reflect high autonomous motivation, low controlled motivation, or both. When studies report that the difference score predicts meaning, it remains unclear whether meaning is driven by the strength of autonomous motivation, the absence of controlled motivation, or their specific combination.\u003c/p\u003e \u003cp\u003eHowever, this unidimensional approach has faced increasing scrutiny. Chemolli and Gagn\u0026eacute; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) present evidence that commonly used SDT motivation measures do not conform well to a single continuum structure, and they argue that autonomous and controlled motivations are better treated as distinct dimensions rather than strict opposites. If these motivational forms are not oppositional, then a difference score cannot isolate the source of an observed association with meaning. When research shows that GSC predicts meaning, the result is compatible with an account in which meaning depends on the relative balance between autonomous and controlled motives, and with an account in which meaning depends primarily on autonomous motivation while controlled motivation is largely irrelevant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePrevious Research\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy subtracting one motivational form from the other, researchers may be obscuring the unique variance each contributes to meaning, potentially masking a reality where autonomous motivation drives meaning while controlled motivation drives distinct outcomes such as distress. Yet recent investigations continue to rely on this composite approach, leaving the specific role of controlled motivation opaque. For instance, Siwek et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that external motivation negatively predicted meaning in life, and they interpret externally pressured striving as exceptionally unfavorable for the experience of meaning.\u003c/p\u003e \u003cp\u003eSimilarly, Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) recently utilized two samples of university students to examine goal motivation, meaning in life, and psychological symptomatology. Although controlled motivation is mathematically embedded within the Goal Self-Concordance (GSC) score used in their study, they did not model controlled motivation as a separable predictor. As a result, it remains unclear whether meaning in life is driven by the presence of autonomous motivation, the absence of controlled motivation, or their relative balance.\u003c/p\u003e \u003cp\u003eThis represents a significant gap in the literature. If meaning depends on balance, interventions must focus on reducing external pressure. However, if meaning depends on strength, interventions should focus primarily on cultivating autonomy, as the presence of pressure may be mentally and affectively draining but irrelevant to the generation of meaning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe present study performed a secondary analysis of the Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) data. Using Directed Acyclic Graphs (DAGs) and applied Bayesian data analysis, the study estimated the unique contribution of each motivational component. Specifically, this study addresses the following primary research question: Is the experience of meaning in life a function of the relative balance between autonomous and controlled motivation, or is it driven primarily by the magnitude of autonomous direction regardless of external pressure?\u003c/p\u003e \u003cp\u003eInspired by the logic that meaning is a product of directed coherence, the following hypotheses are proposed:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eAutonomous motivation will be the primary robust predictor of meaning judgments. When tested simultaneously, autonomous motivation will remain significant, while the GSC will become non-significant.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eH2\u003c/em\u003e: Controlled motivation will demonstrate a functional dissociation: it will strongly predict depressive symptoms (distress) but will demonstrate a negligible direct effect on meaning once distress is controlled for.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eThe positive effect of autonomous motivation on meaning will remain robust regardless of the level of controlled motivation (non-significant interaction), consistent with the interpretation that the autonomy\u0026ndash;meaning association does not differ across levels of controlled motivation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH4\u003c/strong\u003e \u003cp\u003eThese findings will remain stable across different statistical approaches (Linear Regression and Bayesian Ordinal Regression) and when excluding influential outliers.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis paper contributes a direct test of two competing interpretations embedded in self-concordance measurement: a subtractive \u0026lsquo;balance\u0026rsquo; model versus an autonomous-primary model. By modeling autonomous and controlled motivation as separable predictors, rather than collapsing them into a relative difference score, we clarify which component accounts for variance in meaning in life judgments and which component primarily tracks depressive symptomatology.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study employed a quantitative, cross-sectional design to investigate the relationship between motivational quality and meaning in life. A cross-sectional approach was selected as the primary aim was to evaluate the structural validity of competing theoretical models (Balance vs. Strength) rather than to establish temporal causality. However, to rigorously evaluate between these theories, causal structures were modeled explicitly using Directed Acyclic Graphs (DAGs), making the assumed causal structure explicit and guiding covariate adjustment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ensure robust statistical power and replicability, the study utilized secondary data aggregated from two independent samples originally collected by Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) comprising 437 participants. Sample 1 consisted of undergraduate students from California State University, Sacramento (CSUS; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;168). Sample 2 consisted of undergraduate students from Washington State University (WSU; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;269). Given that both samples utilized identical measures and procedures, the data obtained from Sample 1 and Sample 2 were aggregated to form a high-powered combined dataset.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePower Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAn a priori Monte Carlo power analysis indicated that a sample size of \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;133 is required to achieve a power of .80 for the typical small to medium effect sizes found in this domain. The current combined sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429) exceeds this threshold by a factor of three, providing excellent sensitivity to determine even small effects.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFinal Sample\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFollowing the exclusion of participants with incomplete data on the primary variables (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), the final analytic sample was \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429. The sample was predominantly female (82.5%), with 16.3% male participants, and 1.2% other/undisclosed. Ages ranged from 18 to 54 years (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.48, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.53). The sample was ethnically diverse, drawing from two distinct university populations in the Western United States.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eData collection occurred online via the SONA research management systems at the respective universities. Participants received course credit for their participation. Upon accessing the survey link, participants were presented with an informed consent form. This form explicitly detailed: (1) the voluntary nature of the study; (2) the guarantee of anonymity and confidentiality; (3) the potential risks (minimal); and (4) the right to withdraw at any time without penalty.\u003c/p\u003e\u003cp\u003eAfter providing digital consent, participants completed a battery of self-report measures. Crucially, the presentation order of the questionnaires was randomized for each participant. This procedural control mitigates potential order effects and fatigue bias, enhancing the internal validity of the measurement. Following the completion of the measures, participants were presented with a demographic questionnaire and a debriefing form explaining the purpose of the study. The original data collection was approved by the Institutional Review Boards of both universities (IRB #: Cayuse-21-22-160 and IRB # 20058-001).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eGoal Motivation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMotivation was assessed using the Self-Concordance Scale (Sheldon \u0026amp; Elliot, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Participants listed six current personal goals and rated their reasons for pursuing each on a scale from 1 (\u003cem\u003enever for this reason\u003c/em\u003e) to 7 (\u003cem\u003ealways for this reason\u003c/em\u003e). The scale measures four motivational regulatory dimensions: intrinsic (enjoyment), identified (personal value), introjected (guilt/ pressure), and external (rewards/situation).\u003c/p\u003e \u003cp\u003eConsistent with standard self-concordance goal-striving methodology (Sheldon \u0026amp; Elliot, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sheldon \u0026amp; Houser-Marko, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), three composite variables were calculated:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003col\u003e\n \u003cli\u003eAutonomous Motivation: The sum of intrinsic and identified ratings (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;69.75,\u0026nbsp;\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.42).\u003cbr\u003e\u003cbr\u003e\u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eControlled Motivation: The sum of introjected and external ratings (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45.62, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.02).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eGoal self-concordance (GSC) score: Calculated as a difference score ((Intrinsic\u0026thinsp;+\u0026thinsp;Identified) \u0026ndash; (External\u0026thinsp;+\u0026thinsp;Introjected)), which is equivalent to (Autonomous \u0026ndash; Controlled) given the composites above, representing net (difference-score) autonomy (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24.13, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.45).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeaning in Life\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMeaning was assessed using the General Meaning in Life Judgements subscale of the\u003c/p\u003e \u003cp\u003eMultidimensional Meaning in Life Scale (MMLS; Costin \u0026amp; Vignoles, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the full MMLS assesses three cognitive dimensions (Coherence, Purpose, Mattering), this study utilized only the Judgements subscale (4 items) to capture the phenomenological experience of meaning itself (e.g., \"My life as a whole has meaning\"). Items were rated on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;\u003cem\u003eStrongly Disagree\u003c/em\u003e to 7\u0026thinsp;=\u0026thinsp;\u003cem\u003eStrongly Agree\u003c/em\u003e). The subscale demonstrated excellent internal consistency (α\u0026thinsp;=\u0026thinsp;.93).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eControl Variables\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo assess the unique contribution of motivation to meaning accounting for the variance shared with general distress, depressive symptomatology was included as a covariate. It was assessed using the CESD-10 (Andresen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), a 10-item frequency scale (0\u0026ndash;3) which has been shown to have good internal consistency (α\u0026thinsp;=\u0026thinsp;.83; Andresen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStatistical analyses were conducted in R (R Core Team, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), utilizing the lavaan (Rosseel, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and blavaan (Merkle \u0026amp; Rosseel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) packages for structural equation modeling. The analysis proceeded in stages: starting with traditional frequentist regression, then Structural Equation Modeling (SEM), and finally Bayesian item-level analysis. Importantly, this study re-examines the Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) dataset. Whereas the original authors utilized frequentist approaches and binary significance testing ('\u003cem\u003ep\u003c/em\u003e \u0026lt; .05'), this study adopts a Bayesian framework. Instead of forcing a strict 'significant vs. non-significant' outcome which makes it difficult to prove that an effect does not exist, Bayesian analysis calculates direct probabilities via Credible Intervals. This offers a more intuitive quantification of uncertainty, allowing us to see not just if an effect exists, but exactly how likely (or unlikely) it is.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePrimary Hypothesis Testing (Linear Regression)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test the competing utility of the Balance (GSC score) versus Strength (Autonomous) models (H1 and H2), hierarchical linear regression models were constructed. Incremental validity was assessed by examining the change in explained variance (Δ\u003cem\u003eR\u0026sup2;\u003c/em\u003e) when adding the GSC score to a model already containing Autonomous motivation, and vice versa. Controlled motivation was also entered as an independent predictor to test its unique contribution to meaning judgements (H2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBayesian Structural Equation Modeling (BSEM)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo compare causal structures (H3) while accounting for measurement error, Bayesian SEM was employed using blavaan (Merkle \u0026amp; Rosseel, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Three Directed Acyclic Graphs (DAGs; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were specified: (1) a single-index model using the GSC score; (2) a simplified Autonomous-Only model (where the path from controlled motivation is constrained to zero); and (3) a dual-pathway model where Autonomous and Controlled motivation function as separable predictors. Following the framework of causal inference (Pearl, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rohrer, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e), this approach forces implicit theoretical assumptions to be made explicit and mathematically falsifiable.\u003c/p\u003e \u003cp\u003eTo address the nested structure of the GSC data (6 goals per participant), an item-parcelling approach was utilized. For each of the six goals, an 'Autonomous' parcel (mean of Intrinsic and Identified items) and a 'Controlled' parcel (mean of External and Introjected items) were computed. This goal-level strategy preserves the ecological structure of the data while reducing model complexity, and is supported by methodological research demonstrating improved parameter stability (Little et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The models were estimated using Markov Chain Monte Carlo (MCMC) sampling with 3 chains, 1,000 burn-in iterations, and 1,000 sampling iterations. Convergence was verified using the potential scale reduction factor (\u003cem\u003eR̂\u003c/em\u003e \u0026lt; 1.01; Gelman \u0026amp; Rubin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and effective sample sizes exceeding 1,000 for all parameters (Vehtari et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eThree competing causal structures were specified for comparison. Model A (DAG 1) represents a traditional single-index framework using the Goal self-concordance (GSC) score. Model B (DAG 2) represents the proposed \u0026lsquo;Autonomous-Only\u0026rsquo; model, where the path from Controlled motivation is constrained to zero. Model C (DAG 3) represents a Dual-Pathway model where Autonomous and Controlled motivation function independently.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBayesian Item-Level Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo address the limitations of summing Likert-scale data and to account for the ordinal nature of the psychological measures (H4), a Bayesian multilevel ordinal regression was conducted using the brms package (B\u0026uuml;rkner, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This addresses a common limitation in psychological research where 1\u0026ndash;7 survey scales are treated as if they were continuous metric values, as seen in Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Standard regression relies on the assumption of equidistance, presuming that the psychological distance between every rating point is identical (e.g., that the step from '\u003cem\u003eNeutral\u003c/em\u003e' to '\u003cem\u003eAgree\u003c/em\u003e' is the same size as '\u003cem\u003eAgree\u003c/em\u003e' to '\u003cem\u003eStrongly Agree\u003c/em\u003e'). The ordinal model avoids this unwarranted assumption by calculating the specific spacing between response categories based on the data. Rather than enforcing a rigid scale where every step is equal, this approach acknowledges that the psychological shift required to move from '\u003cem\u003eNeutral\u003c/em\u003e' to '\u003cem\u003eAgree\u003c/em\u003e' is not necessarily the same size as the shift from '\u003cem\u003eAgree\u003c/em\u003e' to '\u003cem\u003eStrongly Agree\u003c/em\u003e\u0026rsquo;.\u003c/p\u003e \u003cp\u003eCumulative Probit models were fitted for both the outcome (Meaning in Life) and the primary covariate (Depression). These models included random intercepts for both participants and items, thereby accounting for participant response styles and specific item characteristics. Weakly informative priors were used to regularize estimates. Model comparison was conducted using Leave-One-Out Cross-Validation (LOO-IC) to determine which theoretical predictor (GSC vs. Autonomous motivation) provided the best out-of-sample prediction at the item level.\u003c/p\u003e \u003cp\u003eFollowing McElreath (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the ordinal response is defined as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eR\u003c/em\u003e \u003csub\u003e \u003cem\u003eij\u003c/em\u003e \u003c/sub\u003e ~ Ordered-probit (Φ\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, κ)\u003c/p\u003e \u003cp\u003ewhere Φ\u003cem\u003ei\u003c/em\u003e is the linear predictor (β\u003csub\u003eAuto\u003c/sub\u003e \u0026middot; Auto\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003eCtrl\u003c/sub\u003e \u0026middot; Ctrl\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e + u\u003csub\u003e0\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e + u\u003csub\u003e0\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e), κ represents the \u003cem\u003eK\u003c/em\u003e \u0026ndash; 1 ordered cutpoints, and u\u003csub\u003e0\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and u\u003csub\u003e0\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are random intercepts for participants and items, respectively (B\u0026uuml;rkner \u0026amp; Vuorre, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model was fitted using Stan (v2.37; Stan Development Team, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) via the brms package (B\u0026uuml;rkner, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Default brms priors were used for all parameter estimates. Model fit and convergence was assessed using posterior predictive checks and diagnostics from the Stan sampler.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRobustness Checks\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFinally, to ensure findings were not driven by influential observations, sensitivity analyses were conducted by calculating Cook's Distance. Models were re-estimated after excluding 28 participants exceeding the threshold of 4/\u003cem\u003en\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eData and Code Availability\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData analysis was conducted using the combined dataset (N\u0026thinsp;=\u0026thinsp;429) to maximize statistical power. To maintain anonymity during peer review, analysis code and supporting materials are hosted in a blinded repository and will be made publicly available upon acceptance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eUse of AI-assisted tools\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAI-based tools were used to assist with the development, testing, and debugging of statistical analysis code (e.g., syntax support and workflow troubleshooting). All analytical decisions (model specifications, priors, inclusion/exclusion choices), interpretation of results, and reporting were performed and verified by the author, who takes full responsibility for the analyses and conclusions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDescriptive statistics and zero-order correlations for all study variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Consistent with theoretical expectations, Autonomous motivation was positively correlated with Meaning in Life judgements (\u003cem\u003er\u003c/em\u003e = .35, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Controlled motivation showed a significant negative bivariate correlation with Meaning (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.23, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001); however, as subsequent analyses reveal, this relationship is fully accounted for by depressive symptoms in subsequent models.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and zero-order correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeaning in life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomous motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.35***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControlled motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoal self-concordance (GSC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.37***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.60***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.84***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression (CESD-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.30***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.33***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429. Reliability coefficients (α) are presented on the diagonal. ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003ePrimary Hypothesis Testing: Balance vs. Strength (H1 \u0026amp; H2)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test the competing utility of the GSC score versus Autonomous motivation (H1), hierarchical linear regression models were constructed. The results of the comparative analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the critical test (Model 3), when both predictors were entered simultaneously, Autonomous motivation remained a robust predictor of Meaning judgements (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). In contrast, the GSC score became non-significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e = .215).\u003c/p\u003e \u003cp\u003eAnalysis of incremental validity confirmed this pattern. Adding Autonomous motivation to a model already containing GSC resulted in a significant increase in explained variance (Δ\u003cem\u003eR\u003c/em\u003e\u0026sup2; = .031, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). However, adding GSC to a model already containing Autonomous motivation yielded a negligible and non-significant increase (Δ\u003cem\u003eR\u003c/em\u003e\u0026sup2; = .002, \u003cem\u003ep\u003c/em\u003e = .215). Semi-partial correlation analysis indicated that Autonomous motivation explained approximately 14.4 times more unique variance in Meaning than the GSC composite. These findings provide full support for H1, suggesting that the predictive power of the GSC score is driven almost exclusively by its autonomous component.\u003c/p\u003e \u003cp\u003eFurthermore, the unique contribution of Controlled motivation was examined (H2). When added to a model controlling for Autonomous motivation, Controlled motivation was not a significant predictor of Meaning (\u003cem\u003eb\u003c/em\u003e = -0.02, \u003cem\u003ep\u003c/em\u003e = .22) and explained only 0.21% of the variance. So, H2 was supported: controlled motivation appears irrelevant to the subjective judgement of meaning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical regression analysis predicting meaning in life Judgements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1 (Autonomous)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2 (GSC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3 (Both)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 1: Covariates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.46***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStep 2: Motivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.14***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e- -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.06***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e.03***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429. \u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Unstandardized coefficient. \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Standard error of unstandardized \u003cem\u003eb\u003c/em\u003e. Model 3 tests whether GSC adds beyond Autonomous. *** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eInteraction Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo test whether the effect of autonomous motivation on meaning is dependent on the level of external pressure, a linear model was tested including an interaction term between Autonomous and Controlled motivation. The interaction term was non-significant both with depression included (\u003cem\u003eb\u003c/em\u003e = .002, \u003cem\u003ep\u003c/em\u003e = .269) and without it (\u003cem\u003eb\u003c/em\u003e = .002, \u003cem\u003ep\u003c/em\u003e = .183). This suggests that the positive predictive value of autonomous motivation is robust and does not vary as a function of controlled motivation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eStructural Model Comparison (H3)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo compare causal structures while accounting for measurement error, Bayesian SEM was conducted. The motivational measurement model using goal-level parcels demonstrated excellent fit (CFI = .959, RMSEA = .047). The latent correlation between Autonomous and Controlled motivation was negligible (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.09, 95% CI [-.21, .04]), confirming that these constructs function as orthogonal dimensions rather than opposite ends of a continuum.\u003c/p\u003e \u003cp\u003eIn the structural comparison, the Dual-Pathway DAG provided a definitive test of the causal architecture (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When modeled simultaneously with Depression and demographic covariates: Autonomous motivation had a strong, credibly positive association with Meaning (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41, 95% CI [0.28, 0.55], β\u0026thinsp;=\u0026thinsp;.28), and Controlled motivation had no credible effect on Meaning, with a credible interval centered on zero (\u003cem\u003eb\u003c/em\u003e = -0.04, 95% CI [-0.17, 0.10]).\u003c/p\u003e \u003cp\u003eThe model revealed why prior studies may have observed negative correlations: Controlled motivation was a credible predictor of Depression (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, 95% CI [0.25, 0.50], β\u0026thinsp;=\u0026thinsp;.34), and Depression was a strong negative predictor of Meaning (\u003cem\u003eb\u003c/em\u003e = -0.81, β = \u0026minus;\u0026thinsp;.61). Once this confounding pathway (Distress) was controlled for in the structural model, the direct path from Controlled motivation to Meaning vanished. These results fully support H3, indicating that the 'Autonomous-Only' structure is the most parsimonious representation of the data.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eBayesian Item-Level Analysis (H4)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo assess robustness while accounting for the ordinal nature of Likert-scale data, a Bayesian multilevel cumulative probit analysis was conducted. Model comparison indicated a statistical tie between the Autonomous-Only and Dual-Predictor models (Δelpd\u0026thinsp;=\u0026thinsp;0.00, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18). Following the principle of parsimony, the simpler model is preferred. Furthermore, examination of the posterior distributions in the dual model confirmed the SEM findings (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): the parameter for Autonomous motivation was credibly positive (β\u0026thinsp;=\u0026thinsp;0.07, 95% CI [0.05, 0.09]), while the parameter for Controlled motivation was not credibly different from zero (β = -0.007, 95% CI [-0.019, 0.005]). This confirms the findings (H1 and H2) are not artifacts of treating ordinal data as metric sums; patterns hold even when modeling raw item-level probabilities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eValues represent unstandardized posterior estimates. The path from Controlled Motivation to Meaning includes zero (dashed line), indicating no credible effect, while the path to Depression is significant. *** indicate that the effect is credible.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eHorizontal lines represent the 95% Credible Intervals. The Controlled Motivation interval crosses zero, indicating no credible effect.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eSecondary Analysis: Construct Validity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFinally, to ensure that the null result for Controlled motivation was not due to measurement failure, its relationship was examined with Depressive symptoms (CESD-10). In the Bayesian model, Controlled motivation was a credible positive predictor of depression (β\u0026thinsp;=\u0026thinsp;0.013, 95% CI [0.009, 0.017]), while Autonomous motivation acted as a credible protective factor (β = -0.009, 95% CI [-0.015, -0.003]). This double dissociation validates the measurement: Controlled motivation is functionally active and predicts distress, yet remains structurally irrelevant to the experience of Meaning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eRobustness Checks\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSensitivity analyses confirmed that the exclusion of 28 influential outliers (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;401) did not alter the observed pattern of results. In the re-estimated models, Autonomous motivation remained the sole robust predictor of meaning, while the effects of the GSC score and Controlled motivation remained negligible.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePsychological theory often assumes that well-being arises from motivational concordance: the degree to which our actions align with our values. Embedded in the traditional measurement of this construct is a subtractive assumption: that external pressure mathematically degrades autonomous motivation. This treats motivation as a balance sheet, where obligation cancels out volition. The present study tested whether meaning in life is best predicted by the relative balance of motivational drives (GSC) or by the strength of autonomous motivation alone. Across multiple analytical approaches, the results converged on a clear answer: meaning is strongly associated with autonomous motivation, not relative balance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eThe Primacy of Autonomous Motivation\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe primary hypothesis, that autonomous motivation would emerge as the dominant predictor of meaning, was strongly supported. When the GSC score was included alongside autonomous motivation in hierarchical models, the GSC score contributed no unique variance (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e = .215). The Bayesian structural model confirmed this pattern, showing that the unique contribution of the balance score centered near zero. This contrasts with the original analysis by Sangeorzan et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which used the composite GSC score to predict well-being. This decomposition suggests that the predictive power they observed appears to have been driven primarily by the autonomous component, not the relative balance itself. By using a difference score, previous research obscured the fact that the positive variance appears to reflect wanting to pursue a goal, not from the absence of pressure to do so.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eAutonomous and Controlled Motivation as Distinct Constructs\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA central assumption of standard SDT measurement is that autonomy and control exist on a continuum. However, the data support the critique by Chemolli and Gagn\u0026eacute; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who argue that these forms of motivation are qualitatively distinct. The Bayesian measurement model estimated the latent correlation between autonomous and controlled motivation to be virtually zero (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.09), indicating they function as orthogonal dimensions rather than opposite ends of a spectrum. This undermines the psychometric rationale for the GSC formula. A person can simultaneously experience strong autonomous motivation and strong external pressure. By collapsing these distinct dimensions into a single score, the traditional formula discards information about their independent effects.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eResolving the Distress Confound\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe structural model helps explain a persistent finding in the literature: the apparent negative association between controlled motivation on meaning. Previous studies (e.g., Siwek et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that controlled motivation negatively predicted meaning, leading to the conclusion that pressure is harmful for well-being. The results suggest these findings reflected a confound with distress.\u003c/p\u003e \u003cp\u003eA functional double dissociation was observed: Autonomous motivation predicted meaning but not depression; Controlled motivation predicted depression but not meaning.\u003c/p\u003e \u003cp\u003eNotably, once the path to depression was accounted for, the estimated direct effect of controlled motivation on meaning vanished (β = -0.04, 95% CI crossing zero). This aligns with distinctions between hedonic and eudaemonic well-being (Huta \u0026amp; Ryan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Controlled motivation is associated with distress, but it shows no direct association with meaning. It creates psychological cost, but it does not block the sense that one's pursuits are worthwhile.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eNo Evidence For an Undermining Effect\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe subtractive assumption was directly tested via interaction analysis. If controlled motivation undermines autonomous motivation, the positive association between autonomy and meaning should weaken at higher levels of pressure. The interaction was non-significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; .18).\u003c/p\u003e \u003cp\u003eThis null result has substantive meaning: the observed relationship between Autonomous motivation and Meaning does not appear to vary across pressure levels. An individual experiencing high external pressure derives the same Meaning benefit, as indexed by the autonomy-meaning association, from their autonomous motivation as someone experiencing low pressure: The strength of association of autonomous motivation into meaning does not depend on perceived pressure.\u003c/p\u003e \u003cp\u003eThe results of the present study are inconsistent with the subtractive logic embedded in the GSC formula, which assumes controlled motivation is associated with lower autonomous engagement. The data suggest instead that pressure and meaning operate on parallel tracks\u0026mdash;controlled motivation adds cost (distress) without diminishing the strength of the autonomy-meaning association.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eMethodological Implications\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study illustrates the advantages of Bayesian modeling over traditional frequentist approaches. While the regression models identified the redundancy of the GSC score, frequentist inference is limited to \"failing to reject\" the null, leaving ambiguity about whether an effect is truly absent or simply undetected. The Bayesian posterior distributions showed a credible interval for controlled motivation tightly centered on zero, supporting the conclusion with confidence that the direct effect is negligible rather than merely \u0026lsquo;non-significant\u0026rsquo;.\u003c/p\u003e \u003cp\u003eFurthermore, ordinal models confirmed that these findings are not artifacts of treating Likert scales as continuous. The primacy of autonomous motivation held even when modeling raw item-level response probabilities, suggesting this pattern is unlikely to be an artifact of treating Likert scales as continuous.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eLimitations and Future Research\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe primary limitation is the cross-sectional design, which prevents establishing temporal causality. While Directed Acyclic Graphs were used to make causal assumptions explicit, it is not possible to rule out unobserved confounders. For instance, acute life stressors could simultaneously generate feelings of pressure and depressive symptoms. The observed relationship between controlled motivation and depression might therefore be confounded rather than causal. Longitudinal research is needed to determine whether pressure precedes increases in distress or whether stressful environments simply activate both.\u003c/p\u003e \u003cp\u003eIn addition, all focal constructs were assessed via self-report, which introduces the possibility of common-method variance and shared response tendencies (e.g., negative affectivity) inflating associations. Further, meaning in life was operationalized as a subjective global judgment rather than an objective or metaphysical construct; the present findings therefore speak to perceived meaning and its correlates, not meaning per se. Multi-method work (e.g., informant reports, behavioral indicators, or longitudinal designs) would help clarify the robustness and temporal ordering of these relationships.\u003c/p\u003e \u003cp\u003eA further limitation concerns the sample characteristics. While the sample was adequately powered (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429) and ethnically diverse, it consisted primarily of university students. For students, controlled motivation often reflects social expectations or academic requirements. For populations facing severe economic hardship, controlled motivation may be a matter of survival. Whether the independence of pressure and meaning holds in such contexts remains an open question. Additionally, both samples originated from universities in the United States, and the meaning of autonomous versus controlled motivation may differ across cultural contexts where collectivist values shape goal pursuit differently.\u003c/p\u003e \u003cp\u003eAnother consideration relates to the operationalization of meaning in life. This study measured meaning via global judgements rather than the tripartite dimensions (coherence, purpose, mattering). While analyzing specific dimensions can offer granular insights, I focused on global judgements to capture the holistic phenomenological experience of Meaning in Life (Costin \u0026amp; Vignoles, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, because our primary predictor (GSC) is inherently goal-oriented, there is a risk of conceptual overlap when predicting the specific 'Purpose' dimension. By focusing on the global judgement, I aimed to test whether autonomous goals translate into a broader sense of existential significance, distinct from the mere presence of goal-directedness itself.\u003c/p\u003e \u003cp\u003eFurthermore, the causal direction between controlled motivation and depression cannot be empirically adjudicated with cross-sectional data since the models are statistically equivalent. The specified direction (controlled motivation \u0026rarr; depression) follows theoretical precedent in SDT, but the reverse pathway where depressive symptoms lead individuals to perceive their goals as more externally pressured, remains plausible and would require longitudinal data to distinguish.\u003c/p\u003e \u003cp\u003eFinally, these findings raise questions about the temporal dynamics of motivation. Experience-sampling research could investigate whether the sense of meaning remains stable during moments of high pressure, or whether it fluctuates with immediate distress. This would clarify whether meaning functions as a stable readout of autonomous direction or as a momentary affective state.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNietzsche (as cited in Frankl, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) wrote, \"he who has a why to live can bear almost any how\". The present findings are consistent with this insight. The experience of meaning in life is not a delicate equilibrium that must be maintained by minimizing pressure. Rather, it appears to be a stable product of authentic engagement. External demands may create distress, but they do not subtract from the meaningfulness of our pursuits.\u003c/p\u003e \u003cp\u003eInterpreted through SDT, Nietzsche's \"why\", as used by Frankl, corresponds to autonomous motivation; the sense that one's pursuits genuinely matter. The \"how\", the difficulty, the pressure, the cost, corresponds to controlled motivation, which generates distress but leaves meaning intact. Meaning is not a product of motivational balance, but of autonomous direction.\u003c/p\u003e \u003cp\u003eTo increase meaning, we do not need to remove the pressure\u0026mdash;we need to clarify the direction.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLLGP conceived the study, conducted the analyses, and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author expresses gratitude to Geoffrey Patching for valuable academic guidance, trust, and intellectual freedom throughout the development of this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data analyzed in this study were obtained from an existing dataset. 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Rank-normalization, folding, and localization: An improved R̂ for assessing convergence of MCMC. \u003cem\u003eBayesian Analysis\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 667\u0026ndash;718. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/20-BA1221\u003c/span\u003e\u003cspan address=\"10.1214/20-BA1221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-applied-positive-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iapp","sideBox":"Learn more about [International Journal of Applied Positive Psychology](http://link.springer.com/journal/41042)","snPcode":"41042","submissionUrl":"https://submission.springernature.com/new-submission/41042/3","title":"International Journal of Applied Positive Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"autonomous motivation, meaning in life, self-determination theory, Bayesian structural equation modeling, goal self-concordance","lastPublishedDoi":"10.21203/rs.3.rs-8775727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8775727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Self-Concordance Model posits that autonomous goal pursuit is a primary driver of well-being, yet standard measurement practices often conflate the presence of autonomy with the absence of external pressure via 'relative' difference scores. This study utilized a high-powered dataset (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;429) to systematically test between a Balance Model (where meaning is expected to be associated with the net difference between autonomous and controlled motivation, operationalized as Goal self-concordance) and an Autonomous-Primary Model (where meaning is driven solely by autonomous direction). Using Bayesian Structural Equation Modeling (BSEM) and Directed Acyclic Graphs (DAGs), the analysis indicated that the Goal self-concordance (GSC) score can obscure the true structural relationship between motivation and meaning. Results showed that autonomous motivation was the sole robust predictor of meaning in life judgements, while the GSC scores contributed no unique variance when both were tested simultaneously. A functional double dissociation emerged: autonomous motivation predicted meaning but not depression, while controlled motivation predicted depression but not meaning. Notably, the latent correlation between autonomous and controlled motivation was near zero (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.09), supporting their status as orthogonal constructs rather than opposite ends of a continuum. The interaction between autonomous and controlled motivation was not credible, suggesting that the effect of autonomy on meaning is constant regardless of external pressure. These findings challenge the subtractive logic embedded in traditional self-concordance measurement, and suggest that meaning in life is a product of autonomous direction, not motivational balance.\u003c/p\u003e","manuscriptTitle":"Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 15:15:59","doi":"10.21203/rs.3.rs-8775727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-14T10:27:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-04T04:48:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T04:42:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Applied Positive Psychology","date":"2026-02-03T11:41:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-applied-positive-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iapp","sideBox":"Learn more about [International Journal of Applied Positive Psychology](http://link.springer.com/journal/41042)","snPcode":"41042","submissionUrl":"https://submission.springernature.com/new-submission/41042/3","title":"International Journal of Applied Positive Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"46cf3784-653f-4f0d-a0d1-717bc1a72a51","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T15:16:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 15:15:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8775727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8775727","identity":"rs-8775727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}