Using the time‐varying autoregressive model to study dynamic changes in situation perceptions and emotional reactions

Abstract

Objective: Assuming personality to be a system of intra-individual processes emerging over time in interaction with the environment, we propose an idiographic approach to investigate potential changes of intra-individual dynamics in the perception of situations and emotions of individuals varying in personality traits. We compared the semiparametric time-varying autoregressive model (TV-AR) that takes into account the non-stationarity of psychological processes at the individual level, with the standard AR model. Method: We conducted analyses of individual time series to assess intra-individual changes in mean levels and inertia on data from two adolescents who completed measures of personality and indicated their situation perceptions and emotions five times a day for 19 days. Results: For the less honest, emotional, extraverted, and more agreeable adolescent, the TV-AR model detected reliable changes in the intra-individual dynamics of situation perceptions and emotions whereas, for the other individual, the standard AR model was more preferred, given the lack of changes in the intra-individual dynamics. Conclusions: Psychological processes dynamics in situation perception and emotions may vary from person to person depending on their personality. This work constitutes a first step in demonstrating that an idiographic approach has advantages in identifying changes in individuals' perceptions and reactions to situations.

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“…Individuals may also differ in their response to the crisis. Past studies have noted that reactions to situations vary from individual to individual ( Casini et al, 2020 ). Some become overwhelmed by the slightest changes in their routines, while others exhibit a tremendous amount of resilience even during times of great uncertainty and turmoil ( Casini et al, 2020 ).…”

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“…Other extensions from the time series literature that could be considered are heteroscedastic models or (fractionally) integrated models (Hamaker & Dolan, 2009). Additionally, there have been several studies that looked at nonstationarity due to parameters that change over time (see, e.g., Albers & Bringmann, 2020;Bringmann et al, 2017;Casini et al, 2020). Another way in which models can be made more flexible is by allowing cycles and dynamics at different timescales (e.g., at daily, weekly, and monthly timescales) to be combined by including harmonic waves with different periods as well as complex seasonality in the model (see, e.g., Hyndman & Athanasopoulos, 2021, Chapters 12.1 & 13.1).…”