Development and Initial Validation of a General Psychological Well-being Scale (GPWS) in an African Context

The hierarchical framework of wellbeing (HiFWB)

Purpose

Several decades of research on wellbeing has resulted in a variety of conceptual models used to measure wellbeing. The historical motivations behind these conceptual models have emphasized their differences to the point of clouding the wellbeing measurement landscape. A synthesis of the wellbeing literature is needed to move the field forward and guide future research.

Methods

In this review, we synthesize literature on the measurement of wellbeing from the past 50 years and present The Hierarchical Framework of Wellbeing (HiFWB) that organizes multiple prior models.

Results

We propose a general factor of wellbeing (i.e., "h" factor) at the top level of the hierarchy analogous to "g" in the intelligence literature and "p" in the psychopathology literature. Building off prior conceptualizations, we define general well-being as "the experience of personally valued fulfillment within one's life." We detail the theoretical rationale and empirical evidence behind four hierarchical levels: general (i.e., "h" factor), lenses (e.g., subjective wellbeing), contents (e.g., affects), and characteristics (e.g., positive affect). Example wellbeing constructs are proposed for each level of HiFWB while emphasizing the hierarchical structure is prioritized above any (arbitrary) list of constructs. We discuss various approaches to distinguishing predictors of wellbeing from wellbeing itself (i.e., preventing tautologies) and how they fit into our framework. Considering the bulk of the empirical evidence comes from Western, Educated, Industrialized, Rich, Democratic (WEIRD) cultures, constraints on generalizability are important. Throughout, we compare and contrast HiFWB to other hierarchical structures in psychological science (e.g., five factor model of personality).

Conclusion

The HiFWB is a flexible, encompassing, evidence-based framework for wellbeing conceptualization and measurement in WEIRD populations.