We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

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It has been argued that facets do not represent the bottom of the personality hierarchy-even more specific personality characteristics, nuances, could be useful for describing and understanding individuals and their differences. Combining 2 samples of German twins, we assessed the consensual validity (correlations across different observers), rank-order stability, and heritability of nuances. Personality nuances were operationalized as the 240 items of the Revised NEO Personality Inventory (NEO-PI-R). Their attributes were examined by analyzing item residuals, controlling for the variance of the facet the item had been assigned to and all other facets. Most nuances demonstrated significant (p < .0002) cross-method agreement and rank-order stability. A substantial proportion of them (48% in self-reports, 20% in informant ratings, and 50% in combined ratings) demonstrated a significant (p < .0002) component of additive genetic variance, whereas evidence for environmental influences shared by twins was modest. Applying a procedure to estimate stability and heritability of true scores of item residuals yielded estimates comparable with those of higher-order personality traits, with median estimates of rank-order stability and heritability being .77 and .52, respectively. Few nuances demonstrated robust associations with age and gender, but many showed incremental, conceptually meaningful, and replicable (across methods and/or samples) predictive validity for a range of interest domains and body mass index. We argue that these narrow personality characteristics constitute a valid level of the personality hierarchy. They may be especially useful for providing a deep and contextualized description of the individual, but also for the prediction of specific outcomes. (PsycINFO Database Record

Much of personality research attempts to identify causal links between personality traits and various types of outcomes. I argue that causal interpretations require traits to be seen as existentially and holistically real and the associations to be independent of specific ways of operationalizing the traits. Among other things, this means that, to the extents that causality is to be ascribed to such holistic traits, items and facets of those traits should be similarly associated with specific outcomes, except for variability in the degrees to which they reflect the traits (i.e. factor loadings). I argue that, before drawing causal inferences about personality trait–outcome associations, the presence of this condition should be routinely tested by, for example, systematically comparing the outcome associations of individual items or facets, or sampling different indicators for measuring the same purported traits. Existing evidence suggests that observed associations between personality traits and outcomes at least sometimes depend on which particular items or facets have been included in trait operationalizations, calling trait‐level causal interpretations into question. However, this has rarely been considered in the literature. I argue that when outcome associations are specific to facets, they should not be generalized to traits. Furthermore, when the associations are specific to particular items, they should not even be generalized to facets. Copyright © 2016 European Association of Personality Psychology

Personality-outcome associations, typically represented using the Big Five personality domains, are ubiquitous, but often weak and possibly driven by the constituents of these domains. We hypothesized that representing the associations using personality questionnaire items (as markers for personality nuances) could increase prediction strength. Using the National Child Development Study (N = 8719), we predicted 40 diverse outcomes from both the Big Five domains and their 50 items. Models were trained (using penalized regression) and applied for prediction in independent sample partitions (with 100 permutations). Item models tended to out-predict Big Five models (explaining on average 30% more variance), regardless of outcomes' independently rated breadth versus behavioural specificity. Moreover, the predictive power of Big Five domains per se was at least partly inflated by the unique variance of their constituent items, especially for generally more predictable outcomes. Removing the Big Five variance from items marginally reduced their predictive power. These findings are consistent with the possibility that the associations of personality with outcomes often pertain to (potentially large numbers of) specific behavioural, cognitive, affective, and motivational characteristics represented by single questionnaire items rather than to the broader (underlying) traits that these items are ostensibly indicators of. This may also have implications for personality-based interventions.

We argue that it is useful to distinguish between three key goals of personality science – description, prediction and explanation – and that attaining them often requires different priorities and methodological approaches. We put forward specific recommendations such as publishing findings with minimum a priori aggregation and exploring the limits of predictive models without being constrained by parsimony and intuitiveness but instead maximising out-of-sample predictive accuracy. We argue that naturally-occurring variance in many decontextualized and multi-determined constructs that interest personality scientists may not have individual causes, at least as this term is generally understood and in ways that are human-interpretable, never mind intervenable. If so, useful explanations are narratives that summarize many pieces of descriptive findings rather than models that target individual cause-effect associations. By meticulously studying specific and contextualized behaviours, thoughts, feelings and goals, however, individual causes of variance may ultimately be identifiable, although such causal explanations will likely be far more complex, phenomenon-specific and person-specific than anticipated thus far. Progress in all three areas – description, prediction, and explanation – requires higher-dimensional models than the currently-dominant “Big Few” and supplementing subjective trait-ratings with alternative sources of information such as informant-reports and behavioural measurements. Developing a new generation of psychometric tools thus provides many immediate research opportunities.

Personality traits, especially Openness, are associated with dietary patterns in older age. The pattern of findings may indicate that, in older people, dietary habits may be less related to how controlled they are and more related to their levels of openness and emotional and social adjustment. Policy implications are discussed.

With climate change and its consequences believed to be among the most vital challenges for humanity and the Earth’s ecosystem, it is important to understand why individuals do or do not adopt proenvironmental attitudes and behaviors. Personality traits are well suited for this purpose. Because no recent work has systematically combined the accumulating evidence on this topic, we aimed to meta-analyze the associations of the Big Five and HEXACO personality domains with proenvironmental attitudes and behaviors. A meta-analysis of 38 sources ( N = 44,993) implicated openness and honesty-humility as the strongest correlates of proenvironmental attitudes ( r = .22 and .20) and behaviors ( r = .21 and .25). Agreeableness, conscientiousness, and, to a lesser extent, extraversion were also associated with proenvironmental attitudes ( r = .15, .12, and .09) and behaviors ( r = .10, .11, and .10). Heterogeneity among effect sizes was partly explained by samples’ gender ratio, age, and country of origin and by the personality model. P-curve analyses, funnel plots, and Egger’s tests indicated significant but sporadic and small publication bias. As a validity test, the meta-analytic associations collectively provided substantial predictive accuracy for proenvironmental attitudes ( r = .44–.45) and behaviors ( r = .28–.43) in independent holdout samples.