XI Conference – European Congress of Methodology

Exploring the stability and temporal variability of Consideration of Future Consequences (CCF): A Trait-State-Occasion (TSO) model.

24 Jul 2025, 15:15

15m

Faculty of Social Sciences and Communication. (The Pyramid)/12 - Room (Faculty of Social Sciences and Communication. (The Pyramid))

Oral Presentation Applications/Substantive areas Session 1 : "Development and validation of psychometric instruments"

Speaker

Geraldy Sepúlveda Páez (Complutense University of Madrid)

Abstract

Introduction: Consideration of Future Consequences (CFC) is one of the temporal constructs that has undergone a broad development in psychology in recent years. Although initially conceptualized as a stable individual trait, recent research suggests that it may fluctuate over short periods of time (days or weeks). However, this possible variability has been scarcely explored, and previous literature typically assesses stability using test-retest correlation coefficients, which depend on the interval between measurements (higher in short lapses and lower in long intervals), limiting the understanding of CFC stability. To address these limitations, this study employs a Trait-State-Occasion (TSO) latent variable model, which allows decomposing variability into time-varying and time-invariant sources while also accounting for measurement error. Specifically, we apply this model to analyze the stability of CFC in Spanish workers. Methods: Two independent samples (n₁ = 120; n₂ = 272) were evaluated at four-time points, with measurement intervals of one week in the first sample and two months in the second. We first assessed longitudinal measurement invariance and subsequently fitted TSO models for each data set using the Lavaan package in R Studio. Results: Preliminary results indicate that the TSO model provides a robust modeling framework for modeling intra-individual fluctuations in CFC, particularly in the bimonthly sample, where significant temporal patterns were detected. Conclusions: These results highlight the importance of integrating latent variable models into longitudinal research to improve measurement precision in psychology, minimize the impact of measurement error, and obtain more reliable estimates over time.

Presentation materials

AEMCCO_2025_CFC.pptx