Integrating top-down and bottom-up structural models of subjective well-being: A longitudinal investigation.

Feist, Gregory J., Bodner, Todd E., Jacobs, John F., Miles, Marilyn·1995·Journal of Personality and Social Psychology

Abstract

Although there have been many recent advances in the literature on subjective well-being (SWB), the field historically has suffered from two shortcomings: little theoretical progress and lack of quasiexperimental or longitudinal design (E. Diener, 1984). Causal influences therefore have been difficult to determine. After collecting data over four time periods with 160 Ss we compared how well two alternative models of SWB (bottom-up and top-down models) fit the data. Variables of interest in both models were physical health, daily hassles, world assumptions, and constructive thinking. Results showed that both models provided good fit to the data, with neither model providing a closer fit than the other, which suggests that the field would benefit from devoting more time to examining how general dispositions toward happiness color perceptions of life's experiences. Results implicate bidirectional causal models of SWB and its personality and situational influences

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How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Integrating top-down and bottom-up structural models of subjective well-being: A longitudinal investigation. https://app.cochat.ai/papers/10.1037//0022-3514.68.1.138
The paper
Feist, G. J., Bodner, T. E., Jacobs, J. F., & Miles, M. (1995). Integrating top-down and bottom-up structural models of subjective well-being: A longitudinal investigation. Journal of Personality and Social Psychology. https://doi.org/10.1037//0022-3514.68.1.138

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.