Literature DB >> 31784985

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

Erica Casini1, Juliette Richetin1,2, Emanuele Preti1,2, Laura F Bringmann3,4.   

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.
© 2020 Wiley Periodicals, Inc.

Entities:  

Keywords:  dynamic modeling; idiographic; non-stationarity; time-varying autoregressive model

Year:  2020        PMID: 31784985     DOI: 10.1111/jopy.12528

Source DB:  PubMed          Journal:  J Pers        ISSN: 0022-3506


  1 in total

1.  Personality and travel intentions during and after the COVID-19 pandemic: An artificial neural network (ANN) approach.

Authors:  Shalini Talwar; Shalini Srivastava; Mototaka Sakashita; Nazrul Islam; Amandeep Dhir
Journal:  J Bus Res       Date:  2021-12-08
  1 in total

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