Individual differences in the forms of personality trait trajectories

Abstract

Changes in personality are typically modeled linearly or curvilinearly. It is a simplifying – yet untested – assumption that this model form accurately depicts all person-level trajectories in a sample. Given the complexity of personality development, it seems unlikely that imposing a single model form across all individuals is appropriate. Although typical models can estimate individual trajectories that deviate from the average (i.e., random effects), they do not explicitly test whether people differ in the forms (linear, quadratic, etc.) of their trajectories. This heterogeneity is valuable to uncover, though, as it may imply that different processes are driving change. The current study uses data from four longitudinal datasets (N = 26,469) to empirically test the degree to which people vary in their best-fitting model forms for Big Five personality development. Across datasets, there was substantial heterogeneity in best-fitting model forms. Moreover, the type of form someone had was directly associated with their net and total amount of change across time, and these changes were substantially misquantified when the wrong form was used. Variables such as gender, age, trait levels, and number of waves were also associated with individuals’ types of forms. Lastly, comparisons of best-fitting forms from individual- and sample-level models indicated that consequential discrepancies arise from different levels of analysis (i.e., individual versus sample) and alternative modeling choices (e.g., choice of time metric). Our findings highlight the importance of these individual differences for understanding change processes and suggest that a flexible, person-level approach to understanding personality development is necessary.

Longitudinal within-person variability around personality trajectories.

“…For example, if people have trajectories that are not strictly linear in form and do not necessarily differ from each other in the average magnitude and direction of their changes (Figure 1B), a linear modeling approach would find no meaningful differences between their trajectories nor relative to the average trajectory. Furthermore, if people vary in the magnitude, direction, and form of their trajectory (Figure 1C), which appears to be the norm rather than the exception (Wright & Jackson, 2024a), This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

“…then the complexity of these individuals' development is even more poorly captured. Indeed, it is likely the case that most people are best fit by trajectories that are more complex in form than linear and quadratic (Wright & Jackson, 2024a). Failing to capture this variability could be avoided by fitting a cubic or more flexible model that includes random effects for all slope terms and/or allows people to vary in how well they adhere to the chosen model form; however, these are rarely done in practice.…”

“…In particular, they constrain the degree of person-level deviations around fitted trajectories and assume each person has similar forms of development. This results in many people’s patterns of change being misrepresented (Wright & Jackson, 2024a) and a valuable individual difference parameter that can quantify this degree of within-person variability around trajectories not being modeled. If personality is modeled incorrectly or in too simplistic of a manner, it obscures general trends of development and further hampers the ability to understand why changes occur in the first place (Luhmann et al, 2014; Specht et al, 2014).…”

“…Generally, the most complex form that is fit tends to fit the data the best. For example, a recent meta-analysis favored piecewise linear spline models (Bleidorn et al, 2022); a coordinated data analysis found that, depending on the trait, quadratic forms usually fit best compared to linear (Graham et al, 2020); and another study testing linear, curvilinear, and nonlinear forms across four data sets found that cubic trajectories most often fit the best (Wright & Jackson, 2024a).…”

“…Although some people adhere to the typical assumed linear or possibly quadratic trajectory, others reliably change in different ways, such as those that are cubic or nonlinear in form (Wright & Jackson, 2024a). That is, there are individual differences in the forms that best fit people's trajectories across time-some develop in a monotonic and constant fashion, and others do not.…”