Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

Abstract

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 maximizing out–of–sample predictive accuracy. We argue that naturally occurring variance in many decontextualized and multidetermined 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. © 2020 European Association of Personality Psychology

On definition and description in psychopathology: Reply to Widiger et al. (2023).

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We reply to Wright et al's (2023) commentary and suggestion that personality trait models would be the preferred way to reconfigure the personality disorders (PDs). Though we agree that personality trait models are powerful descriptive tools, we highlight that they lack definitional or explanatory power, and that is why they have not been able to define or distinguish what PDs are (Hopwood, 2018;Mõttus et al, 2020;Pincus, 2011). Scientific models must do more than describe; they must define.

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Distinguishing four types of Person × Situation interactions: An integrative framework and empirical examination.

“…This finding can then be partly explained by revealing the situation variables or person variables involved in the interactions (P × S spec and P spec × S), although the notion of variation in slopes across persons (P × S spec ) or situations (P spec × S) still remains relatively descriptive. In contrast, P spec × S spec is potentially most informative for explanatory research goals (Mõttus et al, 2020). 4 Finally, nonsubstantive factors such as response styles and careless responding are more likely to artificially lead to P × S and P × S spec variance, whereas they would only affect P spec × S and P spec × S spec if they were correlated with the specific person variables.…”

Deep lexical hypothesis: Identifying personality structure in natural language.

“…Understanding the comprehensive structure of psychological individual differences offers the potential to develop models that can describe, predict, and explain the ways that important life outcomes are (and are not) shaped by personality (Mõttus et al, 2020). Indeed, the utility of models of between-person differences is substantial, as evidenced by the influence of the Big Few models of personality over the last 30 years (Ozer & Benet-Martínez, 2006; Roberts et al, 2007; Soto, 2019).…”