Abstract
Introduction
Over the past two decades, personality development has evolved into a frontrunner of the field and one—if not the—major driving force leading the renaissance of personality science (Roberts & Yoon, 2022). Co-occurring with the increasing availability of large-scale data that allow researchers to trace human personality development (Roberts & Yoon, 2022), a fast-growing base of evidence is providing ample support for both stability and continuous plasticity of personality across the lifespan. This base of evidence is powered both by longitudinal datasets that assess within-person change and large-scale cross-sectional datasets that assess age-graded between-person differences (as in the present work), which have tended to produce largely consistent evidence (Roberts & Yoon, 2022). 1 Specifically, while recognizing cultural (Bleidorn et al., 2013) and individual (Graham et al., 2020; Schwaba et al., 2023) differences in the rate, timing, and direction of personality change, as well as some variation in observed developmental trends across different personality measures of the same traits (Costa et al., 2019), this body of work suggests several common developmental patterns. That is, during adolescence, humans tend to undergo a period of antagonism and rebellion—often referred to as the disruption hypothesis (Denissen et al., 2013; Soto & Tackett, 2015). As people enter adulthood, they typically become more well-adjusted, responsible, communal, and emotionally stable (Bleidorn, 2015; Bleidorn et al., 2013; Roberts et al., 2008)—a pattern known as the maturity principle (Bleidorn et al., 2013; Roberts et al., 2008). Continuous albeit less-drastic changes appear to occur throughout middle age (Kandler et al., 2015). While less is known about old age, there is some evidence that individuals tend to regress at that life stage (Graham et al., 2020; Mõttus et al., 2012; Wagner et al., 2016)—a pattern that has been described as a reversal of personality maturation (Bleidorn & Hopwood, 2019). Reflecting their pre-eminent position in the field of personality psychology as a whole, most of the work in personality development has focused on the Big Five trait domains (Bleidorn et al., 2021; Costa et al., 2019; Specht, 2017)—that is, Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism (OCEAN for short; Costa & McCrae, 1992; Digman, 1990; John, 2021). However, just like there is more to personality than the Big Five domains, there is also more to personality development.
Horizontal and Vertical Expansions of the Personality-Development Literature
Consistent with a push to move beyond the “Big Few” (Mõttus et al., 2020), new work is actively expanding the scope of the personality-development literature both horizontally and vertically. In keeping with a broad understanding of personality as subsuming any form of relatively stable psychological differences between people, regardless of their content and breadth (Baumert et al., 2017; Rauthmann, 2020), horizontal extensions have begun to describe lifespan development of other personality constructs such as narrative identity (McAdams & Olson, 2010), subjective well-being (Luhmann, 2017), or Machiavellianism (Götz, Bleidorn, & Rentfrow, 2020). Meanwhile, vertical extensions have stayed within the Big Five taxonomy but started to explore age-related trends in the more narrowly-defined lower-level elements of the Big Five. Zooming in on Big Five facets, a number of studies have demonstrated that more differentiated patterns emerge as trait specificity increases, hence offering a more complete understanding of personality development (Jackson et al., 2009; Roberts et al., 2006; Schwaba et al., 2022; Soto et al., 2011; Soto & John, 2012). Encouraged by this, recent studies have argued—and shown—that even more age-related information is available at the lowest, most granular level of the Big Five hierarchy (Hang et al., 2021; Mõttus & Rozgonjuk, 2021). This level has been termed personality nuances (Condon et al., 2020; McCrae, 2015; McCrae & Mõttus, 2019; Mõttus et al., 2017), is often understood as comprising the most basic building blocks of the personality trait hierarchy (Achaa-Amankwaa et al., 2021; Seeboth & Mõttus, 2018), and is commonly operationalized through single personality items (Hang et al., 2021; Mõttus, 2016; Mõttus et al., 2017; Seeboth & Mõttus, 2018). The additional developmental information afforded at the nuance level is considerable. Nuances have been found to contain over 40% more age-related information than facets and over 130% more age-related information than the Big Five domains (Mõttus & Rozgonjuk, 2021). Importantly, revealing nuance-specific associations is not just a technical exercise but may be the key to (a) reconciling inconsistent findings across studies (due to different personality measurements and, in turn, different nuance sampling), and (b) achieving a better understanding of personality development that is at once more holistic and more specific (Hang et al., 2021). Thus, an intuitive next step for the field of personality development would be to combine horizontal and vertical extensions by examining age-graded changes in constructs beyond the Big Five at different hierarchical levels, with varying granularity. Indeed, initial evidence from intelligence-development research suggests that the notion of nuances as meaningful and information-rich conceptual units can also be fruitfully applied to other personality variables (Schroeders et al., 2021). Building on this, in the current research, we adopt a hierarchical approach to the investigation of personality development. That is, we examine age-graded differences in personal values across the lifespan at three hierarchically stacked levels (from least to most granular): higher-order values, basic values, and value nuances.
Personal Values and Their Development Across the Lifespan
Values are broad, trans-situational goals and represent guiding principles in people’s lives that reflect what is important and desirable to them (Rokeach, 1973; Sagiv et al., 2017; Schwartz, 1992). Values are a core component of the self (Hitlin, 2003; Sagiv & Schwartz, 2022) and human personality (Rauthmann, 2020) and affect a wide range of consequential emotions, cognitions, perceptions, attitudes, behaviors, and life outcomes (Sagiv & Roccas, 2021), from religiosity (Roccas & Elster, 2014; Saroglou et al., 2004; Schnabel & Grötsch, 2015), pro-sociality (Arieli et al., 2020; Bardi & Schwartz, 2003; Maio et al., 2009), and self-esteem (Du et al., 2023; Fetvadjiev & He, 2019; Grosz et al., 2021), via aggression (Benish-Weisman et al., 2017), ethical transgressions (Feldman et al., 2015; Pulfrey & Butera, 2013), and delinquent behaviors (Aquilar et al., 2018; Bilsky & Hermann, 2016; Liu et al., 2007) to voting (Aspelund et al., 2013; Caprara et al., 2006, 2017), political activism (Roets et al., 2014; Sanderson & McQuilkin, 2017; Vecchione et al., 2015), and career choices (Arieli et al., 2016; Gandal et al., 2005; Knafo & Sagiv, 2004).
The predominant, most empirically validated value theory (Knafo et al., 2011; Maio, 2010; Rohan, 2000; Sagiv & Roccas, 2021) is Schwartz’s circumplex model (Schwartz, 1992, 1994, 2012). The model comprises four higher-order value constructs, which—in turn—are composed of 10 basic values. Reflecting the interrelated nature of values, the model also embeds two basic value conflicts (Sagiv & Roccas, 2021; Sagiv & Schwartz, 2022). The first conflict (personal versus social) contrasts the higher-order value
Overview of the Schwartz Human Values as Measured Through the Twenty-Item Values Inventory (TwIVI).
While hedonism is sometimes considered to be part of
Despite the prominent position of personal values in personality psychology and their substantial and multi-faceted effects on how humans live (Sagiv et al., 2017; Sagiv & Roccas, 2021; Sagiv & Schwartz, 2022), relatively few studies have examined their development across the lifespan (Bardi et al., 2014; Borg, 2021). While existing studies do not always produce consistent results and may not allow for a particularly fine-grained and nuanced perspective, a few general age trends emerge. First, in line with the disruption hypothesis (Denissen et al., 2013; Soto & Tackett, 2015), during adolescence, individual value priorities become more self- and growth-focused and less other- and protection-focused (Daniel & Benish-Weisman, 2019; Sagiv & Schwartz, 2022; Vecchione et al., 2020) as reflected in increases in
In addition to investigating these common age trends, which might be largely driven by life stage-specific demands and opportunities (Bardi et al., 2014), as well as biological and psychological aging processes (Schwartz, 2005), some studies examine the impact of personal experiences (e.g., migration; [Bardi et al., 2014; Lönnqvist et al., 2011, 2013], going to college [Bardi et al., 2009], parenthood [Lönnqvist et al., 2018]) and societal and economic events (e.g., the COVID-19 pandemic [Daniel et al., 2022], the 2008 financial crisis [Sortheix et al., 2019], exposure to war [Daniel et al., 2013]) on value development. Taken together, while there is evidence for at least some degree of common and experience-based value development across the lifespan and while scholars have flagged up value development as an emerging research topic that warrants further attention (Schuster et al., 2019), most reviews summarizing the value literature tend to emphasize their stability (rather than change) across the lifespan (Sagiv et al., 2017; Sagiv & Schwartz, 2022; Schuster et al., 2019). Furthermore, the vast majority of value studies are conducted with relatively small and homogeneous samples across relatively short time frames, and all studies reviewed here were conducted at the level of higher-order or basic values (Schuster et al., 2019) as is typical for the field of value research more generally (Sagiv et al., 2017). This leaves value nuances and their dynamics across the lifespan largely unexplored.
Against this background, we argue that value development may be more nuanced than typically represented and propose that a systematic investigation of age-graded differences in personal values from late teenage years to post-retirement across different levels of the value hierarchy (i.e., higher-order values, basic values, value nuances) may reveal underappreciated dynamics.
The Current Research
In the current research, we examine—both individually and in comparison to other values—the extent to which age differences in personal values can be attributed to different levels of the value hierarchy. To this end, we adopt Schwartz’s circumplex value model (Schwartz, 1992, 1994, 2012), a multi-dimensional value framework that combines 4 higher-order values, 10 basic values, and 20 value nuances—as operationalized through individual items—harnessing a large dataset of over 80,000 individual answers to the Twenty-Item Values Inventory (TwIVI; Sandy et al., 2017). In doing so, we follow previous research (Achaa-Amankwaa et al., 2021; Hang et al., 2021; Mõttus & Rozgonjuk, 2021; Schroeders et al., 2021) and adopt an integrative modeling approach (Hofman et al., 2021) that leverages predictive machine-learning models to advance conceptual understanding (Bleidorn & Hopwood, 2019). This approach is rooted in the notion that predictive approaches may not only maximize prediction but can also enrich exploratory and explanatory approaches by highlighting the practical relevance and real-world meaning of observed patterns and by fostering a deeper understanding of the phenomena in question (Bleidorn et al., 2017; Hofman et al., 2021; Rocca & Yarkoni, 2021).
With this in mind, we predict chronological age from a series of regular- and machine-learning regression models based on (a) 4 higher-order values, (b) 10 basic values, and (c) 20 value nuances. In line with recent personality-development research (Hang et al., 2021; Mõttus & Rozgonjuk, 2021; Schroeders et al., 2021), we do not interpret the resulting prediction models as causal models but rather use them as a statistical tool to locate, quantify, and compare where—within the value hierarchy—age-related information is contained (Stachl et al., 2020).
After discussing the research design and methods, we empirically investigate how values vary across age groups from 18 to 75 years and report the results of three predictive models (i.e., traditional ordinary least squares (OLS) regression, Elastic Net, and M5P Decision Tree) fitted at each level of the value hierarchy (i.e., higher-order values, basic values, and value nuances). We examine the conceptual and methodological relevance of our research findings and close with cautionary remarks. The present study is exploratory and has not been pre-registered. Based on recent conceptually and methodologically similar work on lifespan personality development in intelligence (Schroeders et al., 2021) and the Big Five (Hang et al., 2021; Mottus & Rozgonjuk, 2021), we hypothesize that value nuances will contain more age-sensitive information and hence be more predictive of age than basic human values, which in turn will contain more age-sensitive information and hence be more predictive of age than higher-order values.
Materials and Methods
Data-Collection Procedure and Participant Sample
This research uses values and age data from the TIME Magazine Basic Human Values dataset (Du et al., 2023, 2024). The TIME Magazine Basic Human Values Dataset was collected between December 2017 and September 2023. In line with previous projects carried out as part of this research partnership (Ebert et al., 2019; Götz, Bleidorn, & Rentfrow, 2020; Zmigrod et al., 2021), data collection ensued through an interactive online survey, in which participants’ personal values were assessed using scientifically validated measures. The survey (https://time.com/5063406/star-wars-character-quiz/) was launched and promoted via websites and social media channels (e.g., Facebook, Twitter) by TIME Magazine and its media partners (e.g., People, Entertainment Weekly) as a tribute to the global release of the movie “Star Wars: Episode VII—The Last Jedi.” Participants who completed the survey received automatic customized feedback on which Star Wars characters most closely resembled them based on their values. We report all manipulations, measures, and exclusions in these studies (see Online Supplemental Appendix B for a detailed description of the survey). The full analysis code with markdown of results for the current research is available on the OSF (https://osf.io/8sauh/?view_only=5b986a2c970c44ce838ec9c941cd9182). The TIME Sorting Hat Dataset is proprietary and may not be publicly shared but is available upon request from the senior author.
Participants provided informed consent before answering the survey and had the option to receive customized feedback without sharing their data for research purposes. Those who opted in were also asked to answer a short battery of demographic questions (i.e., age, annual income, ethnicity, gender, and place of residence). Overall, completion of the survey took approximately 10 minutes, and after receiving their Star Wars character matches, participants were provided with a more detailed outline of the aims of the associated research project.
The original sample consisted of 122,580 participants. For the current research, we included all participants who self-reported ages between 18 and 75 years and had no missing responses on the value items. The final sample consisted of 80,814 participants, with 57.4% identifying as female, 35.8% identifying as male, and 6.8% reporting other gender identities. The average age was skewed toward younger participants (
Measures
Personal values were assessed using TwIVI (Sandy et al., 2017), a semi-short scale adapting the 40-item Portrait Values Questionnaire (Schwartz, 2003). The TwIVI features 20 portrait-type items (e.g., “Being very successful is important to him or her. S/he likes to impress other people.”), administered on a six-point Likert-type scale on which participants rate how much the described fictional people resemble them (anchors: “not like me at all”; “very much like me”). The TwIVI has been specifically designed for contexts in which semi-short scales are needed, such as the study at hand in which a large-scale sample was recruited through an interactive online survey that would not take up more than 10 minutes. This 20-item scale has been shown to successfully capture the patterns of the longer 40-item Portrait Values Questionnaire, with the average convergence between the TwIVI and standard PVQ measurements being
Data Analysis Strategy
We adopted a three-stage analysis approach: Description (Stage 1), Prediction (Stage 2), and Simulation (Stage 3). In the first stage (Description), we charted age-graded differences in personal values across the human lifespan at all three levels of the value hierarchy to provide an exploratory, visual summary of the changes happening across the lifespan on all three levels (i.e., higher-order values, basic values, and value nuances). In the second stage (Prediction), we fitted three models (traditional OLS regression, Elastic Net, and M5P Decision Tree; see Online Supplemental Appendix A for technical details) at each level of the value hierarchy to understand the relationship between value change and age more systematically. In a third and final stage (Simulation), we dove more deeply into scrutinizing the actual predictive abilities of the different hierarchical levels of the personal value system. To do so, we leveraged a simulation-based approach, in which we conducted 10,000 decisions for each of the nine models, identifying the older of two randomly drawn participants based on the personal values they endorse. This last stage translates statistical findings into an intuitive and interpretable metric: the probability that the algorithm correctly identifies the older of the two randomly drawn participants based solely on their personal values. Furthermore, it contextualizes how the accuracy is impacted by the age difference between the participants, as well as the aggregation level of their values.
In the prediction stage, we employed classical econometric, as well as machine-learning models to optimize for interpretability and accuracy. By including linear and non-linear models, we aimed to predict changes accurately, while keeping interpretability in mind. The dataset was split into an 80/20 test and training dataset (Schroeders et al., 2021). Random sampling occurred within each age percentile. The 20% test data therefore had a similar age distribution as the training data but represented a different partition of the data kept separate throughout the training process. This approach—resulting in nine training and nine testing models (each one per model type and value hierarchy layer)—helped prevent both overfitting through cross-validation/out-of-sample testing (Rocca & Yarkoni, 2021; Seeboth & Mõttus, 2018; Yarkoni & Westfall, 2017) and underfitting through comparisons across different models with varying complexity (Jacobucci & Grimm, 2020; Stachl et al., 2020; Yarkoni & Westfall, 2017).
The linear OLS approach was chosen as a baseline for ease of coefficient interpretation. Following previous research (Hang et al., 2021; Mõttus & Rozgonjuk, 2021; Schroeders et al., 2021; Stewart et al., 2022), we predicted age through Elastic Net regressions. Finally, we implemented the M5P algorithm, which produces a decision tree with a linear regression model at each node (Please see Online Supplemental Appendix A for rationales for and introductions to each model choice).
Following Mõttus and Rozgonjuk’s (2021) caution about age predictions being skewed by sample distributions, we replicated all our analyses using a sample stratification approach as a general robustness check. Specifically, we created four age bins, with
Results
Stage 1: Description—Charting Values Across the Lifespan
Figure 1 shows
Response for Each Item of the Questionnaire by Age (Polynomial Fit, 95% CI).
Assessment at the more granular level of basic values suggested both convergence and divergence of basic values that belonged to the same higher-order value. For example, among the
The same approach was used to examine value nuances (i.e., individual items). We saw evidence of both convergence (e.g.,
Stage 2: Predicting Age From Values at Three Hierarchical Levels
The visual impression that there were shared trajectories across the hierarchical levels of the value systems, as well as unique information at each level, was further supported by our predictive modeling results. Tables 2 (higher-order values), 3 (basic values), and 4 (value nuances) show the training data OLS and Elastic Net coefficients at each of the three levels of the value hierarchy. No traditional
Multiple Regression Predicting Age From Higher-Order Values.
Multiple Regression Predicting Age From Basic Values.
Multiple Regression Predicting Age From Value Nuances.
R2 Across Model and Level Choices for Test Data.
At the higher-order level,
At the basic value level, the
At the value nuance level, there were basic values for which nuances were consistently associated with being older or younger. This was the case for both
As a robustness check, we subsequently drew 500 samples, each comprising 1,000 participants (Götz et al., 2021) and calculated the Spearman correlations between actual and predicted age across the nine training and nine test models, resulting in 18 correlation plots (Figure 3). This approach allowed us to assess whether the observed relationship between actual and predicted age would replicate across smaller, randomly drawn subsets of data. By averaging these correlations and constructing confidence intervals, we ensured that the reported effects were not driven by idiosyncrasies in the full sample but represented a stable and replicable pattern. Consistent with the results described earlier, we found that the Spearman correlations between actual age and predicted age rose with increasing value granularity, with the out-of-sample correlations being
Spearman Correlations Across Model and Level Choices for Test and Training Data.
Stage 3: Contextualizing Findings and Benchmarking Predictive Accuracy
In the third stage of our analysis, we aimed to contextualize our findings and cast the age-sensitive information from each level of the value hierarchy into practical, accessible terms. We plotted the test dataset predictions for each of the nine models against actual participant age (Figure 4).
Observed Versus Predicted Age Across Model and Level Choices for Test Data.
Given the striking discrepancies between predicted versus actual age, we conducted a simple simulation to further understand the observed patterns. We randomly drew two participants and let the algorithms predict each individual’s age, dichotomously encoding whether the algorithm successfully predicted which participant was older (Achaa-Amankwaa et al., 2021). If two drawn participants had the same real age, they were discarded from the analyses. We drew 10,000 pairs for each level (i.e., higher-order values, basic values, and value nuances) and each analysis method (OLS, Elastic Net, and M5P decision tree approach), resulting in 90,000 decisions. Figure 5 depicts accuracies across hierarchical value levels and model choices.
Proportion of Correct Predictions Across Model and Level Choices for Test Data.
In keeping with the aforementioned results, accuracy increased with value granularity. Across models, higher-order value predictions performed ~6% above chance level (which is 50% for binary decisions), basic value prediction accuracies performed ~10% above chance level, and value nuances performed ~12% above chance level.
To better understand the abilities and limits of the different algorithms to predict age based on value information, we charted the prediction success rate as a function of the age difference of the two randomly drawn individuals (Figure 6). We noted that algorithms fared better when predicting participant seniority among pairs with larger age differences. That is, while basic values and—to a greater extent—value nuances continued to consistently outperform predictions based on higher-order values across model choices and hierarchical value levels, accuracies at or above 70% were only achieved for randomly drawn pairs with age differences larger than 20 years. Practically speaking, this means that while the values measured here cannot be used to predict the exact age of individuals, they can be used to effectively infer who is likely to be older based on the values they hold.
Proportion of Correct Predictions by Age Across Model and Level Choices for Test Data.
General Discussion
We drew from a large-scale online sample and adopted an integrative modeling approach (Hofman et al., 2021) to examine (a) whether age-graded differences in personal values emerge across the lifespan and, if so, (b) at which level of the value hierarchy these differences are most pronounced. To that end, in Stage 1 (Description), we plotted age-graded differences in higher-order values, basic values, and value nuances from age 18 to 75 years. Then, in Stage 2 (Prediction), we employed three different modeling approaches (i.e., OLS regressions, Elastic Net regularization, M5P decision trees) to predict individuals’ age based on personal values at each of the three hierarchical values. Finally, in Stage 3 (Simulation), we conducted a series of additional analyses to further explore the practical meaning of our findings and better contextualize the observed effects.
Across all analytical steps and algorithmic models, we found consistent support for our hypothesis (see Figure 7). That is—mirroring prior research on personality development (Big Five; Hang et al., 2021; Mõttus & Rozgonjuk, 2021, intelligence; Schroeders et al., 2021)—values exhibit systematic age-graded differences across all stages of the lifespan. We further showed that value nuances contained more age-sensitive information and had greater predictive power than basic values, which in turn contained more age-sensitive information and greater predictive power than higher-order values.
Summary of Age-Graded Differences Throughout the Lifespan in Schwartz Human Values Across Three Hierarchical Levels.
Specifically, in the first, descriptive stage, our initial plotting of lifespan age-graded differences across higher-order values, basic values, and value nuances suggests both shared trajectories and unique patterns at each of the hierarchical levels of the value system. That is, the age-graded differences observed among the higher-order values (i.e., increasing
In the second, predictive stage, the formal quantification of the relationships between values and age indicated that
From a conceptual perspective, some of our observed broad and specific age-graded differences in personal values provide empirical support for prior theorizing on adaptive aging (Heckhausen et al., 2010). For example, a strong decline in valuing
In our third and final analysis step, we sought to further contextualize the observed effects (Funder & Ozer, 2019; Götz et al., 2022) and translate them into accessible, practical terms by highlighting the actual meaning of our predictive power (Rocca & Yarkoni, 2021; Yarkoni & Westfall, 2017). We found that exact age predictions were not accurate across models and hierarchical value levels, but our models were fairly accurate in differentiating younger from older participants based on their values. In the random pairwise participant comparisons, models could predict the older participant with accuracies ranging from just above chance level 55.47% (higher order–Elastic Net) to 61.47% (value nuances–Elastic Net). Accuracies generally improved as more granular value nuances were considered and as the age gap between participants increased. The value nuance performance averaged 80.93% for the four highest age-gap bands of 36–40, 41–45, 46–50, and 51+.
Research Contributions and Implications
Our paper offers conceptual and methodological contributions to the values- and personality-development literature at large.
First—in conjunction with mounting evidence from other constructs (Hang et al., 2021; Mõttus & Rozgonjuk, 2021; Schroeders et al., 2021)—our findings suggest that age-graded variation in personality does occur along many dimensions at different hierarchical levels and that to get the complete picture of personality development, we will need to consider not only the broad domains that we are most familiar with but also the small nuances that they consist of. To be clear, like others who have reported similar findings for other components of personality, we are neither denying the relevance, legitimacy, and parsimony of higher-order constructs nor arguing for a radical shift wherein we will always and only consider personality nuances. Rather, we think of this as a bandwidth-fidelity tradeoff (John et al., 1991; Mõttus & Rozgonjuk, 2021; Rentfrow, 2010) and simply caution researchers to deliberately choose the level of the personality hierarchy that best corresponds to their research questions and goals, rather than defaulting to the broadest hierarchical levels. Whenever possible, we recommend not choosing at all, but rather reporting multiple hierarchical levels in parallel, so as to maximize the information that can be gained. We note that this argument—and the incremental power of personality nuances—is not limited to personality development. Similar empirical findings and theoretical arguments are emerging across personality science, from life outcomes (Seeboth & Mõttus, 2018; Stewart et al., 2022) and geographical ambiance (Elleman et al., 2020) to culture (Achaa-Amankwaa et al., 2021).
On a methodological level, we note that while we did our best to produce accurate estimates, bringing together large samples, cross-validation, regularization, and cross-model comparisons (Del Giudice, 2021; Mõttus et al., 2020; Stachl et al., 2020; Yarkoni & Westfall, 2017), and while we yielded what is conventionally regarded as sizable correlations between predicted ages and participants’ reported ages (Funder & Ozer, 2019; Gignac & Szodorai, 2016), the actual ability of our models to predict participants’ actual age was relatively weak. This may surprise some readers—and certainly surprised us at first—but is actually not uncommon, even with stronger correlations than ours. As Stachl and colleagues (2020, p. 618) put it: “An almost perfect correlation can be found even when predictions are off in absolute terms by a large degree.” This also becomes apparent when engaging in the sobering exercise of trying to use correlations to draw meaningful conclusions about specific individuals (Mõttus, 2022)—as attempted in our pairwise comparisons. Our point here is not to say that this means the current research is uninformative. On the contrary, we believe that if anything, our integrative modeling approaches enriched the descriptive goals of our work. However, we think this is a potent reminder to heed the advice of other scholars to seize the opportunities that predictive modeling affords to benchmark our findings and scrutinize their real-world applicability (Bleidorn et al., 2017; Bleidorn & Hopwood, 2019; Rocca & Yarkoni, 2021; Yarkoni & Westfall, 2017). If used in this way, integrative modeling may enable us to better understand the reach and limits of our theories and findings (Hofman et al., 2021) and may helpfully contribute to the ongoing discussion in the field of how to determine the meaning and relevance of empirical effects (Anvari et al., 2023; Anvari & Lakens, 2021; Funder & Ozer, 2019; Götz et al., 2022, 2024). Indeed, the actual age correlations we find across the higher-order, basic, and nuance value levels are all on par with—or even larger than—those reported in the Big Five literature when using measures of similar length, such as the Big Five Inventory 2 (BFI-2) (Hang et al., 2021).
Furthermore, while we observed a considerable increase in accuracy when moving toward more fine-grained value nuances, we note that across analytical applications, a simple OLS-based linear regression model achieved statistical out-of-sample performances that were comparable—and at times even slightly superior—to the considerably more sophisticated and complex machine-learning models (i.e., Elastic Net, M5P Decision Tree). While this may be surprising, it is a regular occurrence (Christodoulou et al., 2019; Del Giudice, 2021; Jacobucci & Grimm, 2020) that should caution researchers against the blind use of advanced machine-learning models and highlights the utility of cross-model comparisons.
Limitations and Future Research Directions
Our study has several limitations. First, as the study of personality nuances is only just emerging (Hang et al., 2021; Mõttus & Rozgonjuk, 2021; Schroeders et al., 2021), the TwIVI scale we used was originally designed to measure higher-order and basic values, not value nuances (Sandy et al., 2017). There is currently no formally developed value nuance framework. This means that the items used may not be the most suitable or representative set of value nuances. It may also mean that our findings underestimate the predictive power that could be reached with a more comprehensive, carefully selected set of value nuances (Stewart et al., 2022). Future research should develop a systematic, hierarchical value taxonomy (Condon et al., 2020) that encompasses a more deliberately selected and wider set of nuances.
Second, our research was based on a self-selected online survey. While it is large, diverse, and regionally representative within the United States (Du et al., 2023), it skews toward younger participants (mean age = 27.4 years;
Third, our study was restricted to cross-sectional data, which means that we inferred rather than directly observed value change and that we are unable to rule out the possibility of confounding cohort effects (Schaie, 1977), for example, individuals who were in their 60s in 2020, were born in the 1960s, and came of age in the 1980s at the height of capitalism, during the “greed is good” era. This upbringing might have instilled in them an achievement-focused mind-set that prizes self-interest above all else. Although longitudinal designs come with their own set of limitations, such as attrition effects, time-of-measurement effects, and self-selection effects (Robinson, 2013; Schaie, 1996), and although the personality-development literature has so far observed a clear convergence between findings from cross-sectional and longitudinal studies (Roberts & Yoon, 2022), future research should aim to provide further longitudinal evidence for the stability and change of values at all hierarchical levels across the lifespan. Such work may then fruitfully inform—and be informed by—evolving theoretical models of value change, such as the dual route model of value change (Bardi & Goodwin, 2011), as well as feed into theoretical models of how values affect behavior, such as Sagiv and Roccas’ (2021) process model.
Finally, aside from its specific content focus, on a methodological level, the current research illustrates the power of big data and machine learning to make inferences about individuals and their personal characteristics. Of note, the present work itself may be a comparatively innocuous demonstration of that—requiring individuals to proactively select into (a) providing informed consent, (b) completing a 20-item self-report personal values questionnaire, and (c) opting in to donate their data in order for us to study what their personal values reveal about their age. However, other work has shown that far more easily obtainable data—such as digital footprints on social media, including Facebook likes, natural language on Twitter, and headshots—can be used to accurately infer highly intimate and sensitive personal attributes, such as personality, political ideology, or sexual orientation (Kosinski et al., 2013, 2024; Park et al., 2015; Youyou et al., 2015). In this new world of big data, machine learning, and—increasingly developing—generative artificial intelligence, ethical sensitivity is thus of paramount importance (Alexander et al., 2020; Kosinski et al., 2015), and while it is crucial not to fall prey to the false dichotomy of “privacy versus insight” (Matz et al., 2022), it is just as crucial for researchers to ascertain that the collection, storage, analysis, and interpretation of their data are in line with state-of-the-art ethical, legal, and professional standards. Then—and likely only then—can research live up to its mandate of beneficence, generating novel insights that benefit citizens, while protecting their anonymity and privacy in the process.
Conclusions
The current research brought together large-scale data and an integrative modeling approach to examine how age-related information is distributed across three hierarchical levels of personal values. Our results suggest that value nuances capture considerable unique age-sensitive information, above and beyond basic values and higher-order values, which also manifests in improved predictive performance. As such, the present work contributes to the field in three ways; that is, by extending the literature on personality development, personal values, and personality nuances. In conjunction with a fast-growing body of knowledge that highlights their utility and relevance (Achaa-Amankwaa et al., 2021; Condon et al., 2020; Elleman et al., 2020; Hang et al., 2021; Mõttus et al., 2017, 2019; Mõttus & Rozgonjuk, 2021; Revelle et al., 2020; Seeboth & Mõttus, 2018; Stewart et al., 2022; Wessels et al., 2020), the current findings underscore that nuances are here to stay as formal and full members of the values hierarchy that matters not only for values and development but also for personality science as a whole.
Supplemental Material
sj-docx-1-psp-10.1177_01461672241312570 – Supplemental material for Human Values Across the Lifespan: Age-Graded Differences at Three Hierarchical Levels and What We Can Learn From Them
Supplemental material, sj-docx-1-psp-10.1177_01461672241312570 for Human Values Across the Lifespan: Age-Graded Differences at Three Hierarchical Levels and What We Can Learn From Them by Andrés Gvirtz, Matteo Montecchi, Amy Selby and Friedrich M. Götz in Personality and Social Psychology Bulletin
Footnotes
Declaration of Conflicting Interests
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