Towards an Explanatory Personality Psychology: Integrating Personality Structure, Personality Process, and Personality Development
Introduction
Personality psychology aims to describe, predict, and explain individual differences in thoughts, feelings, and behaviors.
While personality psychology has made progress in description and prediction, explanation requires integrating structural, developmental, and process-oriented perspectives.
This paper summarizes a theoretical framework published in the European Journal of Personality (Baumert et al., 2017a) and highlights challenges for future research.
Key terms (personality, behavior, trait, state, structure, explanation, development) are defined as in Baumert et al. (2017a) to avoid ambiguity.
The focus is on the relation between structure, processes, and development of personality, with less attention to biological, genetic, and evolutionary factors.
Personality is defined as an individual's characteristic patterns of thought, emotion, and behavior, along with the psychological mechanisms behind those patterns (Funder, 2004).
These patterns must be relatively stable over time and across contexts and distinguish the individual from others.
The focus is on stable interindividual differences, but intraindividual differences are also of interest if they are systematically reoccurring or if there is enduring intraindividual change (development).
Behavior is defined narrowly as observable utterances, movements, or lack thereof, to avoid circularity in explanations.
Description and Prediction: Structural Approach to Personality
Personality researchers have invested efforts in describing interindividual differences in thoughts, feelings, and behaviors.
They investigated relations among traits and states.
Traits are quantitative dimensions describing stable interindividual differences in coherent behaviors, thoughts, and feelings, including temperament, ability, attitude, value, belief, motive, and emotion (Baumert et al., 2017a).
Latent state-trait theory (Steyer, Schmitt, & Eid, 1999) highlights that any variable can capture trait- and state-variance to different degrees.
A structural approach identifies patterns of population-level covariation of interindividual differences.
Structure refers to the manner in which traits or states are organized with respect to each other (Baumert et al., 2017a).
Hierarchical models group traits into clusters based on covariation, with lower-level descriptions nested within higher levels.
Examples include the Five Factor Model (FFM), HEXACO model, and Big Two.
Factorial traits are broad clusters of correlated thoughts, feelings, and behaviors derived through factor analytic approaches.
Structural models offer parsimony and potential exhaustiveness, enhancing comparability across studies and enabling economical applications.
Factorial traits have facilitated progress in describing developmental patterns, estimating heritability, identifying molecular genetic correlates, and describing clinical syndromes.
Factorial traits successfully predict relevant life outcomes, such as health, educational/vocational success, and social relationships.
Explanation: Process-Oriented Approaches
Explanation involves articulating a causal or functional relation that brings about a phenomenon (Baumert et al., 2017a).
Can Factorial Traits Explain Individual Differences?
Some researchers use factorial traits to explain individual differences in behavior.
For example, agreeableness in promoting cooperative behaviors, or personality determining physical activity.
However, an explanation requires conceptual independence between the explanans (cause) and the explanandum (behavior).
Using factorial traits (defined by their indicators) to explain thoughts, feelings, and behaviors that cluster below them would be circular (Blum, Baumert, & Schmitt, 2018; Cervone, 1999; Mischel, 1968).
Factorial traits can reveal shared underlying causes of correlated thoughts, feelings, and behaviors.
McCrae and Costa (2008) suggest traits are basic tendencies causing behavior but not affected by psychological processes.
This understanding assumes covariation among indicators is due to common causes (Borsboom, Mellenbergh, & van Heerden, 2003; Reichenbach, 1956).
Correspondence means correlated behaviors are caused by common or overlapping processes, not involved in shaping uncorrelated behaviors (Baumert et al., 2017a).
Correspondence vs. Emergence
Covariation among indicators can reflect common causes, but different causal patterns could also give rise to observed clusters (Borsboom et al., 2003; Read et al., 2010).
Direct causal relations among indicators can occur (Costantini & Perugini, 2016a, 2018; Cramer et al., 2012).
The network perspective conceives personality as a network of interacting thoughts, feelings, motivations, and behaviors (Borsboom & Cramer, 2013; Costantini et al., 2015; Cramer et al., 2012).
Factorial traits are seen as emergent from interactions within the personality network, without assuming unitary causal entities.
For example, Costantini and Perugini (2016b) found that the correlation between industriousness and impulse-control (facets of conscientiousness) becomes negligible when controlling for self-control and future orientation.
Self-control and future orientation could be causal mechanisms explaining the clustering of facets into conscientiousness.
Covariation among indicators of factorial traits could be explained by direct causal relations or shared causes of other indicators.
Uncorrelated behaviors might still share causal influences.
Counteracting mechanisms can result in uncorrelated behavioral tendencies (Wood, Gardner, & Harms, 2015).
For example, politeness (agreeableness) and assertiveness (extraversion) might both depend on favorable perceptions of others and desire for power, but with opposite effects.
Reinforcement sensitivity theory (Corr, 2004) proposes neuroticism and extraversion both share reward and punishment sensitivity as causal underpinnings.
Punishment sensitivity has a positive influence on indicators of neuroticism, and a negative influence on indicators of extraversion.
Weak emergence implies properties at a superordinate level can be deduced from subordinate levels, even with complex causal mechanisms (Chalmers, 2006).
Weakly emergent phenomena are characterized by causal dependence, causal autonomy, and downward causation (Costantini & Perugini, 2018).
Causal dependence: Properties of factorial traits depend causally on underlying behaviors, thoughts, and feelings.
Causal autonomy: Emergent phenomena acquire properties independent of the specific causal events that generated them.
Downward causation: Emergent phenomena influence the subordinate level.
For example, a traffic jam (emergent phenomenon) influences drivers' behaviors.
A person's level of agreeableness influences their helping behaviors, regardless of current reinforcement levels.
In cases of emergence, there is no simple mapping between structure and processes; rather, the interplay between processes should be the focus.
Process-Oriented Approaches to Explanation
Structural models suggest groups of phenomena with common causes, but lower levels of organization that influence behavior need to be identified (Baumert et al., 2017a).
Process-oriented approaches articulate intraindividual relations among processes and between internal processes and overt behavior.
Process-oriented theories describe systematic interindividual differences in how intraindividual processes unfold, which could be causal for interindividual differences in behavior.
Social cognitive, emotion, and motivation/self-regulatory theories specify aspects of information processing, expectations, evaluations, goal-setting, and self-monitoring.
Research links factorial traits to social cognitive, affective, and motivational operations.
For example, anger proneness is linked to hostile thoughts, while agreeableness involves controlling such thoughts.
Neuroticism combines negative appraisals and the link between appraisals and negative emotions (Tong, 2010).
Extraversion might not be connected to positive affect from sociable behavior itself, but more to initiating the behavior (Srivastava, Angelo, & Vallereux, 2008).
The role of factorial traits is interpreted differently among researchers.
Some interpret traits as an aggregate description of processes and their causal relations (Costantini & Perugini, 2016a; Fleeson & Jayawickreme, 2015).
Others assign traits roles conceptually distinct from the processes, using them as placeholders for underlying causes (Bolger & Schilling, 1991; Baumert et al., 2011).
If trait labels are used as placeholders, it is critical to make explicit what they represent.
Commonly, traits are assessed by global self-reports, which may overlap in content with processes or behaviors being explained.
Gaining from Integrating Process-Oriented and Structural Approaches
Structural approaches are valuable for description and prediction but limited for explanation.
Process-oriented approaches are needed to reveal underlying causes of behavior.
Knowledge of individual differences can guide process-oriented approaches.
Identification of factorial traits can guide hypotheses on clusters of phenomena sharing common causes.
Process-oriented research should compare effects of candidate processes across domains to reveal generalizable causal mechanisms.
Observed patterns of covariation constrain models of microscopic processes (Baumert et al., 2017a).
Comparisons between domains can reveal counteracting mechanisms.
Factorial trait labels can be useful for parsimonious communication if understood as brief labels for underlying explanations.
Explanation of Change and Development
Understanding causal mechanisms of behavior is necessary for explaining development.
Causal processes, in transaction with situational circumstances, give rise to trait levels and enduring changes in them (Baumert et al., 2017a).
Development Caused by Changes in Processes
Descriptive knowledge has accumulated on patterns of enduring change in individual trait levels.
The core question is what causes a change in trait levels.
Changes in behavioral tendencies can be explained by changes in how causal processes unfold or changes in elements that feed into processes.
Changes in causal processes or elements must be retained or perpetuated across time to speak of development (Baumert et al., 2017a; VandenBos, 2006).
This can be due to single shocks or repeated experiences.
For example, contingencies between behaviors and social rewards/punishments might differ between environments, resulting in behavioral change (Bleidorn et al., 2018).
Process, Structure, and Development are Inherently Related
Developmental, process-oriented, and structural perspectives should be considered in concert.
Comparing different structures can provide working hypotheses on processes to identify (Shiner & DeYoung, 2013).
Cattell's (1966) data box exemplifies the interconnection between process, structure, and development.
A multidimensional data space includes persons, behaviors, and time points.
Cells can be aggregated and compared in different ways to observe different patterns and structures (Baltes, Reese, & Nesselroade, 1977; Cattell, 1952; Molenaar & Campbell, 2009).
Different structures do not necessarily imply different causes.
Interpreting Differences in Structures
Differences between inter- and intraindividual structures are often discussed.
Comparisons between inter- and intraindividual structures have confounded differences in time scale.
For example, inter- and intraindividual correlations among positive and negative affect differ (Epstein, 1983).
Intraindividual correlations are negative at short time scales, while interindividual correlations are closer to zero (Watson, Wiese, Vaidya, & Tellegen, 1999).
Structure and time-scale can be unconfounded by correlating positive affect and negative affect interindividually at any single occasion or intraindividually across longer time scales.
Intraindividual processes and interindividual differences act in similar ways but give rise to different structures at different time scales.
Different Processes at Different Time Scales
Observing different structures at different time scales points toward timely dynamics without implying different mechanisms.
However, different mechanisms can come into play at different time scales (Mund, Hagemeyer, & Neyer, 2017).
Repetition of processes across time might change their functionality.
Habit formation is an example where a situational constellation activates a goal representation, which becomes more independent of the individual's goal pursuit over time (Wood & Rünger, 2016).
Investigating causal processes of development requires examining those processes that cause behavior at both short and longer time scales.
Trait structures can be emergent from interactions of causal processes.
Changes in behavior might depend on interplays of counteracting processes at the same or different time scales.
Counteracting processes can be responsible for individual deviations from normative trajectories.
Social cognitive learning processes, self-regulatory processes, and self-concept formation interact at different time scales.
Linking short-term and long-term processes can explain why behavior remains stable or changes across time.
Implications for Personality Research
The theoretical framework integrating personality structure, process, and development has several important implications.
Disaggregation
Broad factorial traits are useful for description and prediction, but their aggregation may conceal causal mechanisms.
Items of typical personality measures collapse different processes and behaviors, obscuring relations among those elements.
Network analytic approaches disaggregate analyses to investigate micro-level associations.
Measurement instruments designed to capture factorial traits might not be sufficient for disentangling relevant mechanisms.
Disaggregation is needed at a conceptual level to determine which processes to target as causal mechanisms.
Process-oriented frameworks provide sensible accounts of units of analyses.
It is critical to describe interindividual differences in intraindividual dynamics of states in interaction with situational features (Wrzus, Quintus, & Baumert, 2018).
Methodologically, this requires repeated momentary assessments under systematic variation of situational constellations.
Relying on global reports is insufficient for fine-grained analyses.
Investigating Processes Together
Most research linking factorial traits and personality processes has targeted processes in isolation.
Candidate causal processes should be compared across behavioral domains.
This will allow determining whether causal mechanisms generalize, are parallel, or are specifically relevant in certain domains.
It is necessary to test empirically whether uncorrelated behaviors share common causes.
Interindividual differences in cognitive, affective, and motivational processes should be investigated simultaneously.
Patterns of interactions and transactions between these processes can be explored.
Investigating processes in concert will serve to identify patterns of counteracting mechanisms.
Further Level of Explanation
Cognitive, affective and motivational processes can be related to behavioral effects, allowing for functional-cognitive analysis (De Houwer, 2011; Hughes, De Houwer, & Perugini, 2016; Perugini, Costantini, Hughes, & De Houwer, 2016).
Distinguishing between behavioral and cognitive levels separates the phenomenon to be explained from the cognitive constructs used to explain it.
It identifies what aspects of the environment need to be manipulated to influence behavior and mediating mental processes.
For example, manipulating the environment can increase the likelihood of being on time, which is functional to achieving a desired outcome.
If successful and repeated, corresponding cognitive and self-regulatory processes will change in enduring ways.
Personality Change
There has been a growing interest in personality trait change (Roberts & Mroczek, 2008).
Traits can change in enduring ways in a relatively brief time span and because of experimental manipulations (Roberts et al., 2017).
Volitional personality change is possible with minimal interventions (Hudson & Fraley, 2015, 2016; Robinson, Noftle, Guo, Asadi, & Zhang, 2015).
Evidence of personality change is straightforward from an emergent perspective such as the one we have sketched here.
From an emergent perspective, stability is the achievement of a relative equilibrium over time; under conditions that support change, the trait will change as well.
Linking Time-Scales
It is key to understand the processes that cause concrete behavior in concrete situations and how individuals differ in how these processes unfold.
Research must link descriptions of processes and behaviors at different time scales.
We need information on moment-to-moment dynamics of cognition, affect, and motivation and their causal effects on behavior.
We need knowledge about recurrences of interindividual differences and changes that occur across time in processes or in elements feeding into them.
Process knowledge at different time scales should be linked to macro-level descriptions of changes in factorial traits.
The question of which time scales are adequate is critical and difficult and will differ between phenomena.
Insufficient timely resolution represents a major challenge.
Multi-Method Assessment
Personality is not equal to self-concept, and its assessment is not bound to self-report.
Integrating personality structure, process, and development requires explaining concrete behavior, which lends itself to multi-method assessment.
This includes observation, tests, and informant reports (e.g. Back & Egloff, 2009; Kurzius & Borkenau, 2015).
Self-report might be suitable for assessing candidate causal psychological processes, yet additional means should be used to infer those processes.
Experimental Designs
Personality research has predominantly relied on correlational designs.
As an explanatory science, the focus shifts to causal mechanisms.
Extending the methodological repertoire with quasi-experimental and experimental designs is desirable.
Observing consequences of systematic situational variations can speak to potential processing tendencies.
Replicability
The discussed implications require complex studies, with multi-method assessments under systematic variation of situational constellations.
Collaborative efforts are needed to reach adequate sample sizes (Finnigan & Vazire, 2017).
Large-scale studies provide opportunities for manifold analyses, so preregistration of a priori hypotheses is important.
Concluding Comments
This contribution has presented an integrative approach to personality structure, processes, and development.
Abandoning the simple assumption of correspondence requires moving toward a more complex set of studies.
Properly appreciating the distinction between different levels of analysis requires theoretical developments.
Acknowledging the complexity involved in conceptualizing and studying personality by trying to adopt an integrative approach implies relaxing some more or less hidden assumptions.
Introduction
Personality psychology seeks to comprehensively describe, accurately predict, and thoroughly explain individual differences in thoughts, feelings, and behaviors. It aims to understand why people differ.
While personality psychology has achieved notable progress in description and prediction through structural models, providing explanations necessitates the integration of structural, developmental, and process-oriented perspectives.
This paper presents a summary of a theoretical framework initially published in the European Journal of Personality (Baumert et al., 2017a). It highlights critical challenges that warrant attention in future research endeavors within the field.
To ensure clarity and avoid ambiguity, key terms such as personality, behavior, trait, state, structure, explanation, and development are defined precisely as they are in Baumert et al. (2017a).
The primary focus is on the intricate relationships between the structure, processes, and development of personality. Biological, genetic, and evolutionary factors receive comparatively less attention.
Personality is defined as an individual's consistent patterns of thought, emotion, and behavior, incorporating the psychological mechanisms that underlie these patterns (Funder, 2004). These patterns exhibit relative stability over time and across various contexts, distinguishing the individual from others.
The emphasis is placed on stable interindividual differences. However, intraindividual differences are also of interest, especially if they systematically reoccur or signify enduring intraindividual change (development).
Behavior is specifically defined as observable utterances, movements, or their absence. This narrow definition is intended to prevent circularity in explanations.
Description and Prediction: Structural Approach to Personality
Personality researchers have dedicated considerable effort to describing interindividual differences in thoughts, feelings, and behaviors, seeking to categorize and quantify the ways individuals vary.
They have extensively investigated the relationships among traits and states, aiming to understand how these aspects of personality relate to one another.
Traits are conceptualized as quantitative dimensions that describe stable interindividual differences in coherent behaviors, thoughts, and feelings. This includes a broad range of characteristics such as temperament, abilities, attitudes, values, beliefs, motives, and emotions (Baumert et al., 2017a).
Latent state-trait theory (Steyer, Schmitt, & Eid, 1999) emphasizes that any variable can capture trait variance and state variance to varying degrees, highlighting the dynamic nature of personality.
A structural approach is employed to identify patterns of population-level covariation of interindividual differences, aiming to map out the organization of personality traits and states.
Structure, in this context, refers to the specific manner in which traits or states are organized in relation to each other (Baumert et al., 2017a), providing a framework for understanding personality organization.
Hierarchical models are used to group traits into clusters based on their covariation. Lower-level descriptions are nested within higher levels, allowing for a multi-layered understanding of personality.
Prominent examples of hierarchical models include the Five Factor Model (FFM), the HEXACO model, and the Big Two, which offer comprehensive classifications of personality traits.
Factorial traits are broad clusters of correlated thoughts, feelings, and behaviors derived through factor analytic approaches. They represent higher-order dimensions that capture the essence of personality.
Structural models offer parsimony and potential exhaustiveness, enhancing comparability across studies and enabling economical applications in various fields.
Factorial traits have played a crucial role in facilitating progress in describing developmental patterns, estimating heritability, identifying molecular genetic correlates, and describing clinical syndromes. They provide a valuable framework for understanding the complexities of human behavior.
Factorial traits have demonstrated the ability to successfully predict relevant life outcomes, including health, educational/vocational success, and social relationships, underscoring their practical significance.
Explanation: Process-Oriented Approaches
Explanation, in the context of personality psychology, involves articulating a causal or functional relation that brings about a particular phenomenon (Baumert et al., 2017a). It seeks to uncover the underlying mechanisms that drive behavior.
Can Factorial Traits Explain Individual Differences?
Some researchers attempt to utilize factorial traits to explain individual differences in behavior, positing that traits directly influence actions.
For example, it might be suggested that agreeableness promotes cooperative behaviors, or that personality traits determine levels of physical activity. However, this approach encounters challenges.
An explanation necessitates conceptual independence between the explanans (cause) and the explanandum (behavior). If the cause and effect are not distinct, the explanation becomes circular.
Employing factorial traits (defined by their indicators) to explain thoughts, feelings, and behaviors that cluster below them would be a circular argument (Blum, Baumert, & Schmitt, 2018; Cervone, 1999; Mischel, 1968). It's akin to explaining a phenomenon using the phenomenon itself.
Factorial traits have the capacity to reveal shared underlying causes of correlated thoughts, feelings, and behaviors, shedding light on the common origins of different aspects of personality.
McCrae and Costa (2008) propose that traits are basic tendencies that cause behavior but are not influenced by psychological processes. This perspective emphasizes the causal role of traits in shaping behavior.
This understanding assumes that covariation among indicators is due to common causes (Borsboom, Mellenbergh, & van Heerden, 2003; Reichenbach, 1956), implying that traits are the fundamental drivers of behavior.
Correspondence implies that correlated behaviors are caused by common or overlapping processes, which are not involved in shaping uncorrelated behaviors (Baumert et al., 2017a). This perspective highlights the specificity of trait influences.
Correspondence vs. Emergence
Covariation among indicators can indeed reflect common causes, but it is crucial to recognize that different causal patterns could also give rise to observed clusters (Borsboom et al., 2003; Read et al., 2010). The relationship between traits and behaviors is not always straightforward.
Direct causal relations among indicators can occur (Costantini & Perugini, 2016a, 2018; Cramer et al., 2012), suggesting that behaviors can influence one another in complex ways.
The network perspective conceives personality as a network of interacting thoughts, feelings, motivations, and behaviors (Borsboom & Cramer, 2013; Costantini et al., 2015; Cramer et al., 2012). This perspective offers a dynamic view of personality as an interconnected system.
Factorial traits are viewed as emergent from interactions within the personality network, without the assumption of unitary causal entities. Traits are the result of complex interactions rather than the starting point.
For example, Costantini and Perugini (2016b) discovered that the correlation between industriousness and impulse-control (facets of conscientiousness) becomes negligible when controlling for self-control and future orientation. This suggests that these facets are influenced by other factors.
Self-control and future orientation may serve as causal mechanisms, explaining the clustering of facets into conscientiousness. These factors provide insights into the underlying dynamics of personality.
Covariation among indicators of factorial traits could be explained by direct causal relations or shared causes of other indicators, highlighting the complexity of trait relationships.
Uncorrelated behaviors might still share causal influences, suggesting that even seemingly unrelated actions can have common origins.
Counteracting mechanisms can result in uncorrelated behavioral tendencies (Wood, Gardner, & Harms, 2015). Personality is not always straightforward; opposing forces can shape behavior.
For instance, politeness (agreeableness) and assertiveness (extraversion) might both depend on favorable perceptions of others and a desire for power, but with opposite effects. These traits can be influenced by similar motivations but manifest differently.
Reinforcement sensitivity theory (Corr, 2004) posits that neuroticism and extraversion both share reward and punishment sensitivity as causal underpinnings. Different traits can have common roots.
Punishment sensitivity has a positive influence on indicators of neuroticism and a negative influence on indicators of extraversion, illustrating how the same factor can have contrasting effects on different traits.
Weak emergence implies that properties at a superordinate level can be deduced from subordinate levels, even in the presence of complex causal mechanisms (Chalmers, 2006). Higher-level traits can be understood by examining their underlying components.
Weakly emergent phenomena are characterized by causal dependence, causal autonomy, and downward causation (Costantini & Perugini, 2018).
Causal dependence means that the properties of factorial traits depend causally on underlying behaviors, thoughts, and feelings. Traits are shaped by these fundamental elements.
Causal autonomy implies that emergent phenomena acquire properties independent of the specific causal events that generated them. Traits can develop their own characteristics over time.
Downward causation suggests that emergent phenomena influence the subordinate level. Higher-level traits can influence behaviors, thoughts, and feelings.
For example, a traffic jam (an emergent phenomenon) influences drivers' behaviors, demonstrating how emergent phenomena can shape individual actions.
Similarly, a person's level of agreeableness influences their helping behaviors, regardless of current reinforcement levels, illustrating how traits impact behavior.
In cases of emergence, there is no simple mapping between structure and processes; the focus shifts to the interplay between processes, emphasizing that the dynamics between different elements are key to understanding personality.
Process-Oriented Approaches to Explanation
Structural models offer valuable insights into groups of phenomena with common causes, but identifying the lower levels of organization that influence behavior is crucial, according to Baumert et al. (2017a).
Process-oriented approaches articulate intraindividual relations among processes and between internal processes and overt behavior, providing a more granular understanding of behavior.
Process-oriented theories describe systematic interindividual differences in how intraindividual processes unfold, which could explain interindividual differences in behavior. These theories delve into the mechanisms that drive personality.
Social cognitive, emotion, and motivation/self-regulatory theories specify aspects of information processing, expectations, evaluations, goal-setting, and self-monitoring. They offer frameworks for understanding the cognitive and emotional processes that influence behavior.
Research connects factorial traits to social cognitive, affective, and motivational operations, suggesting that traits are linked to these internal processes.
For example, anger proneness is linked to hostile thoughts, while agreeableness involves controlling such thoughts, illustrating the connection between traits and cognitive processes.
Neuroticism combines negative appraisals and the link between appraisals and negative emotions (Tong, 2010), indicating that neuroticism involves both cognitive and emotional components.
Extraversion might not be connected to positive affect from sociable behavior itself but more to initiating the behavior (Srivastava, Angelo, & Vallereux, 2008), suggesting that extraversion is associated with initiating social interactions.
Researchers have varying interpretations of the role of factorial traits.
Some interpret traits as an aggregate description of processes and their causal relations (Costantini & Perugini, 2016a; Fleeson & Jayawickreme, 2015), viewing traits as summaries of underlying processes.
Others assign traits roles conceptually distinct from the processes, using them as placeholders for underlying causes (Bolger & Schilling, 1991; Baumert et al., 2011), considering traits as indicators of the root causes of behavior.
If trait labels are used as placeholders, it is essential to make explicit what they represent to avoid ambiguity.
Traits are commonly assessed using global self-reports, which may overlap in content with processes or behaviors being explained, highlighting the need for careful interpretation.
Gaining from Integrating Process-Oriented and Structural Approaches
Structural approaches are valuable for description and prediction but have limitations when it comes to explanation. They provide a broad overview but lack the depth to explain why behaviors occur.
Process-oriented approaches are necessary to uncover the underlying causes of behavior, providing a more detailed understanding of the mechanisms at play.
Knowledge of individual differences can guide process-oriented approaches, helping researchers focus on the most relevant processes for specific traits.
The identification of factorial traits can guide hypotheses about clusters of phenomena sharing common causes, streamlining research efforts.
Process-oriented research should compare the effects of candidate processes across domains to identify generalizable causal mechanisms. This approach allows for a more comprehensive understanding of personality.
Observed patterns of covariation constrain models of microscopic processes, ensuring that these models align with empirical data (Baumert et al., 2017a).
Comparisons between domains can reveal counteracting mechanisms, shedding light on the complex interplay of factors that influence behavior.
Factorial trait labels can be useful for parsimonious communication if understood as brief labels for underlying explanations. They provide a concise way to refer to complex phenomena.
Explanation of Change and Development
Understanding the causal mechanisms of behavior is necessary for explaining development, as emphasized by Baumert et al. (2017a).
Causal processes, interacting with situational circumstances, give rise to trait levels and enduring changes in them. This highlights the dynamic nature of personality development.
Development Caused by Changes in Processes
Descriptive knowledge has accumulated regarding patterns of enduring change in individual trait levels, providing insights into how traits evolve over time.
The central question is: What causes a change in trait levels? Exploring this question is crucial for understanding personality development.
Changes in behavioral tendencies can be explained by alterations in how causal processes unfold or changes in elements that feed into those processes. This perspective emphasizes the importance of understanding the underlying mechanisms of change.
Changes in causal processes or elements must be retained or perpetuated across time to be considered development (Baumert et al., 2017a; VandenBos, 2006). This requirement ensures that the changes are enduring and meaningful.
These changes can result from single shocks or repeated experiences, highlighting the role of both significant events and cumulative effects in shaping personality.
For example, contingencies between behaviors and social rewards/punishments might differ between environments, leading to behavioral change (Bleidorn et al., 2018). Social interactions play a significant role in shaping personality.
Process, Structure, and Development are Inherently Related
Developmental, process-oriented, and structural perspectives should be considered in conjunction to provide a comprehensive understanding of personality. These different perspectives are interconnected and mutually informative.
Comparing different structures can generate working hypotheses on processes to identify (Shiner & DeYoung, 2013), facilitating the exploration of underlying mechanisms.
Cattell's (1966) data box exemplifies the interconnection between process, structure, and development by providing a framework for analyzing data from multiple perspectives.
This multidimensional data space includes persons, behaviors, and time points, allowing for a comprehensive analysis of personality data.
Cells can be aggregated and compared in various ways to observe different patterns and structures (Baltes, Reese, & Nesselroade, 1977; Cattell, 1952; Molenaar & Campbell, 2009). Different analytical approaches can reveal different insights.
It is important to note that different structures do not necessarily imply different causes; different patterns can arise from the same underlying mechanisms.
Interpreting Differences in Structures
Discussions often revolve around differences between inter- and intraindividual structures, but these comparisons can be complex.
Comparisons between inter- and intraindividual structures have sometimes confounded differences in time scale, leading to misinterpretations.
For example, inter- and intraindividual correlations among positive and negative affect differ (Epstein, 1983). These correlations vary depending on the level of analysis.
Intraindividual correlations are negative at short time scales, while interindividual correlations are closer to zero (Watson, Wiese, Vaidya, & Tellegen, 1999). This highlights the dynamic nature of emotions over time.
Structure and time-scale can be unconfounded by correlating positive affect and negative affect interindividually at any single occasion or intraindividually across longer time scales. This approach provides a more accurate understanding of emotional dynamics.
Intraindividual processes and interindividual differences act similarly but give rise to different structures at different time scales, emphasizing the importance of considering both individual and group dynamics.
Different Processes at Different Time Scales
Observing different structures at different time scales points to timely dynamics without necessarily implying different mechanisms, suggesting that patterns can change over time without fundamental alterations in underlying processes.
However, different mechanisms can come into play at different time scales (Mund, Hagemeyer, & Neyer, 2017). Different factors can influence behavior at different points in time.
Repetition of processes across time might alter their functionality, highlighting the potential for processes to evolve with repeated activation.
Habit formation exemplifies this, where a situational constellation activates a goal representation, which becomes more independent of the individual's goal pursuit over time (Wood & Rünger, 2016). Habits display a shift in the relationship between goals and actions.
Investigating causal processes of development requires examining those processes that cause behavior at both short and longer time scales, underscoring the need to consider both immediate and long-term influences.
Trait structures can emerge from the interactions of causal processes, highlighting the dynamic nature of personality.
Changes in behavior may hinge on the interplay of counteracting processes at the same or different time scales. These competing influences can shape behavior in complex ways.
Counteracting processes can account for individual deviations from normative trajectories, explaining why individuals may diverge from typical patterns of development.
Social cognitive learning processes, self-regulatory processes, and self-concept formation interact across different time scales, demonstrating the interplay of various factors in shaping personality.
Linking short-term and long-term processes can elucidate why behavior remains stable or changes across time. This approach provides insights into the continuity and change in personality.
Implications for Personality Research
This theoretical framework, which integrates personality structure, process, and development, has significant implications for future research in the field.
Disaggregation
Broad factorial traits are valuable for description and prediction, but their aggregation can obscure the underlying causal mechanisms, potentially masking important insights.
Items found in typical personality measures collapse different processes and behaviors, hindering the understanding of the relations among these elements.
Network analytic approaches emphasize disaggregation to examine micro-level associations, enabling a more detailed understanding of personality dynamics.
Measurement instruments designed to capture factorial traits may not be sufficient for disentangling the relevant mechanisms, necessitating the development of more refined measurement tools.
Disaggregation is essential at a conceptual level to identify the processes that should be targeted as causal mechanisms, providing a clearer focus for research.
Process-oriented frameworks provide sensible accounts of units of analyses, offering a more structured approach to understanding personality.
It is vital to describe interindividual differences in intraindividual dynamics of states in interaction with situational features (Wrzus, Quintus, & Baumert, 2018). Consideration of situational context is essential for a comprehensive understanding.
Methodologically, this necessitates repeated momentary assessments under systematic variation of situational constellations, requiring more intensive data collection methods.
Relying on global reports is inadequate for fine-grained analyses, underlining the need for more precise measurement techniques.
Investigating Processes Together
The majority of research linking factorial traits and personality processes has focused on processes in isolation, neglecting the potential interactions and relationships between them.
Candidate causal processes should be compared across behavioral domains to determine whether causal mechanisms generalize, act in parallel, or are specifically relevant in certain domains. This comparative approach provides a more comprehensive understanding.
It is essential to test empirically whether uncorrelated behaviors share common causes, challenging the assumption that traits are the sole drivers of behavior.
Interindividual differences in cognitive, affective, and motivational processes should be investigated simultaneously to fully understand the complex interplay of these factors.
Exploring patterns of interactions and transactions between these processes can offer insights into the dynamics of personality.
Investigating processes in concert is crucial to identify patterns of counteracting mechanisms, allowing for a more nuanced understanding of personality.
Further Level of Explanation
Cognitive, affective, and motivational processes can be related to behavioral effects through functional-cognitive analysis (De Houwer, 2011; Hughes, De Houwer, & Perugini, 2016; Perugini, Costantini, Hughes, & De Houwer, 2016), providing a framework for understanding the link between internal processes and external behaviors.
Distinguishing between behavioral and cognitive levels separates the phenomenon being explained from the cognitive constructs used to explain it, enhancing the clarity of explanations.
This approach identifies what aspects of the environment need to be manipulated to influence behavior and mediating mental processes, offering insights into potential interventions.
For example, manipulating the environment can increase the likelihood of being on time, which is functional for achieving a desired outcome, demonstrating how environmental changes can influence behavior.
If successful and repeated, corresponding cognitive and self-regulatory processes will undergo enduring changes, leading to lasting impacts on personality.
Personality Change
There has been a growing interest in the topic of personality trait change (Roberts & Mroczek, 2008), highlighting the potential for individuals to evolve over time.
Traits can undergo enduring changes in a relatively short time span and as a result of experimental manipulations (Roberts et al., 2017), indicating the malleability of personality.
Volitional personality change is possible with minimal interventions (Hudson & Fraley, 2015, 2016; Robinson, Noftle, Guo, Asadi, & Zhang, 2015), underscoring the potential for individuals to intentionally shape their personalities.
Evidence of personality change is straightforward from an emergent perspective, such as the one we have sketched here, suggesting that personality is a dynamic and evolving phenomenon.
From an emergent perspective, stability represents the achievement of a relative equilibrium over time; under conditions that support change, the trait will change as well, highlighting the context-dependent nature of personality.
Linking Time-Scales
It is essential to understand the processes that elicit concrete behavior in specific situations and how individuals vary in the ways these processes unfold, as emphasized throughout this discussion.
Research must connect descriptions of processes and behaviors across different time scales to develop a more comprehensive understanding of personality.
We require information on the moment-to-moment dynamics of cognition, affect, and motivation and their causal effects on behavior, necessitating more intensive and granular data collection.
We need knowledge about the recurrences of interindividual differences and changes that occur across time in processes or in elements that feed into them, requiring longitudinal studies.
Process knowledge at different time scales should be linked to macro-level descriptions of changes in factorial traits, providing a bridge between micro and macro perspectives on personality.
Determining the appropriate time scales is critical, yet challenging, and will vary depending on the phenomena being examined. The time scales must align with the processes being investigated.
Insufficient timely resolution poses a significant challenge, highlighting the need for methodologies that can capture the dynamics of personality at appropriate time scales.
Multi-Method Assessment
Personality should not be equated with self-concept, and its assessment should not be limited to self-report measures, emphasizing the importance of adopting a broader approach.
Integrating personality structure, process, and development demands the explanation of concrete behavior, which naturally lends itself to multi-method assessment strategies.
These strategies should incorporate observation, tests, and informant reports (e.g., Back & Egloff, 2009; Kurzius & Borkenau, 2015) to provide a more comprehensive understanding of personality.
While self-report might be suitable for assessing candidate causal psychological processes, additional methods should be used to infer those processes, enhancing the validity of research.
Experimental Designs
Traditionally, personality research has primarily relied on correlational designs, limiting the ability to draw causal inferences.
As an explanatory science, the focus shifts to causal mechanisms, necessitating a greater emphasis on methodologies that can establish causation.
Extending the methodological repertoire to include quasi-experimental and experimental designs is a desirable goal, enhancing the rigor of personality research.
Observing the consequences of systematic situational variations can offer insights into potential processing tendencies, facilitating a more thorough understanding of personality processes.
Replicability
The implications discussed here necessitate complex studies involving multi-method assessments conducted under systematic variation of situational constellations, which can be resource-intensive.
Collaborative endeavors are essential to gather adequate sample sizes (Finnigan & Vazire, 2017), ensuring the reliability and generalizability of findings.
Large-scale studies offer valuable opportunities for diverse analyses, underscoring the importance of preregistering a priori hypotheses to reduce bias and enhance transparency.
Concluding Comments
In this contribution, we have presented an integrative approach to understanding personality structure, processes, and development, offering a holistic perspective on the field.
Abandoning the simplistic assumption of correspondence requires a shift toward more intricate and comprehensive studies that explore the complexities of personality.
Fully appreciating the distinction between different levels of analysis requires ongoing theoretical developments, necessitating continued efforts to refine our understanding of personality.
Acknowledging the complexity involved in conceptualizing and studying personality by adopting an