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Related Experiment Video

Updated: Jun 12, 2025

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Connecting the dots: using a network approach to study the wellbeing spectrum.

Anne Landvreugd1,2, Margot P van de Weijer1,2, Dirk H M Pelt1,2

  • 1Department of Biological Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Current Psychology (New Brunswick, N.J.)
|September 26, 2024
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Summary

The psychometric network approach reveals distinct positive and negative wellbeing clusters. Key indicators like life satisfaction, neuroticism, and depression symptoms are most central to understanding overall wellbeing.

Keywords:
DepressionFlourishingHappinessNetworkPersonalityWellbeing

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Area of Science:

  • Positive psychology
  • Psychometrics
  • Network analysis

Background:

  • Defining wellbeing in positive psychology presents challenges.
  • Existing models may not fully capture the complex structure of wellbeing.
  • The psychometric network approach offers a novel method to study construct structure.

Purpose of the Study:

  • To investigate the structure of wellbeing using the psychometric network approach.
  • To identify the most central components of the wellbeing construct.
  • To examine the interconnectedness of positive and negative wellbeing indicators.

Main Methods:

  • Utilized the psychometric network approach with Dutch adults from the Netherlands Twin Register.
  • Employed Mixed Graphical Models and LASSO regularization for network estimation.
  • Validated network structure and accuracy using trimming and estimation samples.

Main Results:

  • Identified a network with distinct positive (life satisfaction, happiness, flourishing) and negative (depressive symptoms, loneliness, neuroticism) clusters.
  • Pinpointed four central nodes: one life satisfaction item, one neuroticism item, and two depression items.
  • Demonstrated interconnectedness between positive and negative wellbeing phenotypes, challenging strictly delineated domains.

Conclusions:

  • The network approach effectively visualizes the complex, interconnected nature of wellbeing.
  • Central nodes identified can serve as efficient indicators for future wellbeing research.
  • Results support a holistic view of wellbeing, rejecting rigidly separated domains.