Characterizing the psychologically rich life:
Personality traits, daily activities, and goal pursuit

The idea of a ‘good life’ has traditionally been thought of as one filled with happy, pleasurable moments of comfort (hedonic), or one filled with meaningful dedication to personally valued goals (eudaimonic). Moving beyond the eudaimonic–hedonic divide to conceptualizing well-being, a new pathway has been proposed: well-being via pursuing a psychologically rich life. The psychologically rich life is characterized by a variety of interesting and perspective-changing experiences. The purpose of this study is to identify the individual and contextual factors that characterize the psychologically rich life in an everyday context. We first completed a cross-sectional quantitative pilot study is to understand how the personality traits, positive psychological functioning, and daily activities characteristic of those who pursue a life of richness differ from those who pursue a life of pleasure, meaning, or engagement. These findings informed our hypothesis-driven longitudinal study (4-wave weekly surveys). Data analysis is now underway while we collect qualitative data through life story interviews. 

Studies

• Psychological Richness as a Meta-Quality of Well-Being: Evidence Across Cross-Sectional and Longitudinal Networks

• The Brief Psychologically Rich Life Questionnaire

• Narrative Identity and Psychologically Rich Lives

• Is Psychological Richness a Matter of Balancing Novelty?

Methods

Longitudinal modelling, structural equation modelling, network analysis, experience sampling, mixed methods, and natural language processing.

https://osf.io/xz8y5/overview

Configurational science of well-being meta-theory

Traditional approaches often conceptualise well-being as the sum of individual components, such as happiness, meaning, or psychological need satisfaction. This research programme takes a different perspective by viewing well-being as an emergent property of how experiences, motivations, behaviours, and life circumstances fit together across time. Rather than asking which single factors matter most, this work examines how different configurations of psychological processes become sustainable—or unsustainable—under real-world conditions. This meta-theoretical programme provides an overarching framework that integrates several linked theories which investigate how people balance enjoyable and effortful experiences, recover from periods of high demand, develop flexible repertoires of well-being, and navigate environmental constraints over time

Studies

• Beyond Additive Models: Well-Being as a Dynamic Cost–Recovery System

• The Cost of Fulfilment: A Dynamic Model of Well-Being

• Toward a Configurational Science of Well-Being

• Well-Being as a Temporally Constrained, Energetic, and Ecologically Embedded System

• The Well-Being Complexity Framework: Understanding Well-being Repertoires Across the Life Course

• Resource–Constraint Alignment: A Configurational Perspective on Sustainable Well-Being

Methods

Conceptual theory development, longitudinal modelling, multilevel modelling, structural equation modelling, intensive longitudinal methods, and psychometric network analysis.

NHL Career Trajectories and Outcomes (1979-2025) Database

This project develops a longitudinal database of player careers in the National Hockey League, integrating draft data, performance metrics, team histories, and career endpoints. The database is designed to support advanced quantitative modelling of career trajectories in elite sport, including growth curve models, event-history analyses, and team-level time series approaches. We use these data to examine how performance evolves over time, how players and teams respond to shocks (e.g., losses, injuries, transitions), and how short-term dynamics accumulate into long-term career outcomes. The project provides a scalable framework for studying development, resilience, and performance sustainability in high-performance systems.

https://osf.io/jvcnx/overview 

Understanding how communities shape mental health and well-being

Mental health is shaped not only by individual characteristics but also by the communities in which people live. This programme investigates how neighbourhood environments, socioeconomic inequalities, and community resources influence mental health and well-being across the life course. By combining nationally representative surveys with systems approaches, the research aims to identify the contextual factors that promote flourishing, reduce inequalities, and support healthier communities.

Current work uses datasets including Understanding Society, the Scottish Health Survey, and the People and Nature Survey for England to examine how neighbourhood characteristics, natural environments, and community resources relate to psychological distress and positive mental health. Alongside empirical analyses, the programme develops systems-based approaches to public mental health, investigating how prevention, community infrastructure, and mental health services interact to shape population outcomes over time.

Studies

• Nature Contact and Well-Being: Independent Associations of Visit Frequency, Nature Diversity, Access, and Loneliness

• Public Mental Health as a Complex System: From Individual Behaviour Change to Collective Capability and Community Infrastructure

• From Mental Illness Treatment to Population Mental Health: A Bridge Too Far

• Population Mental Health, Inequality, and Service Capacity: A Policy Simulation Study in Scotland 

Methods

Multilevel modelling, longitudinal analysis, spatial analysis, systems modelling, and policy simulation.

UK Wellbeing Infrastructure Spine (UK-WIS)

The UK Wellbeing Infrastructure Spine (UK-WIS) is an open, reproducible small-area data infrastructure designed to support place-based health and well-being research across Great Britain. The project addresses a common challenge in neighbourhood research: relevant contextual data are often fragmented across multiple organisations, use different geographic frameworks, and require substantial processing before they can be linked to survey or administrative datasets.

UK-WIS integrates publicly available datasets describing transport infrastructure, greenspace, cultural infrastructure, libraries, and other neighbourhood assets within a harmonised framework of 41,729 small-area units covering England, Scotland, and Wales. Built using a fully scripted and reproducible R pipeline, the resource provides researchers with a transparent and extensible platform for linking neighbourhood characteristics to individual-level survey data and administrative records. Beyond supporting my own research, the long-term goal is to create an openly documented infrastructure that reduces duplication of effort, improves reproducibility, and accelerates place-based mental health and well-being research across the UK.

Methods

Administrative data linkage, spatial data science, geographic information systems (GIS), reproducible programming, open science, and research infrastructure development.

https://osf.io/dhbts/overview 

Programming well-being: Building reproducible and automated tools for longitudinal and cross-national survey research in R

This project develops and evaluates the Tayside Coding Club (TCC), a structured training and mentoring programme designed to build quantitative and reproducible research skills in wellbeing science. Alongside this, the project develops an open-source R toolkit for analysing longitudinal and cross-national wellbeing survey data, automating core tasks such as scale scoring, weighting, and visualisation. Using real-world datasets (e.g., UKHLS and the Global Flourishing Study), the project integrates tool development with hands-on training, enabling participants to engage directly with applied data workflows. The evaluation component examines how structured coding mentorship supports skill development, reproducibility practices, and research capacity within early-career researchers. Together, the project provides a scalable model for combining open science infrastructure with quantitative training in population wellbeing research.

https://osf.io/2yhj7/overview