current studies

psychological richness

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

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.

Psychological richness as a meta-quality of well-being:
Evidence across cross-sectional and longitudinal networks

Psychological richness—a life marked by variety, complexity, and perspective change—has been proposed as a potential third dimension of well-being alongside happiness and meaning. Yet it remains unclear whether richness constitutes a distinct type of well-being, a facilitating process that links motivational and evaluative elements, or a broader meta-quality that shapes the organization of the well-being system. Across two studies, we used network analysis to map the structural position of psychological richness within well-being architecture. Study 1 estimated an EBICglasso network in a large cross-sectional sample (N = 489) including basic psychological needs (autonomy, competence, relatedness, novelty), psychological richness (PRL-Q), and evaluative well-being. Richness did not appear as an isolated node or separate community within the network; instead, it occupied a bridging position linking need satisfaction with life evaluation. Study 2 replicated this structure using Wave 1 data from an independent longitudinal sample (N = 442) and then examined descriptive network dynamics across four weekly waves. Across waves, need and well-being nodes showed stable clustering, and Network Comparison Tests indicated structural invariance over a short time. Richness, assessed at Waves 1 and 4, consistently reappeared as an integrative connector within this motivational–evaluative system. These findings suggest that psychological richness functions as an integrative dimension within the well-being system, connecting motivational processes with evaluative judgments of life.

positive, personality, and motivational psychology

What people prioritize matters:
Longitudinal links between well-being priorities and life satisfaction

Subjective well-being reflects people’s circumstances, their values, and the standards used to evaluate their lives. Using three waves of nationally representative UK panel data (Wave 1 N = 9,051; Wave 2 N = 13,513; Wave 3 N = 12,110), this study examined how personal well-being priorities relate to life satisfaction. Personal priorities (e.g., health, peace of mind) were nearly universal, whereas endorsements of material, relational, leisure, spiritual/community, and local-environment priorities varied across people and time. Linear mixed-effects models that separated between-person differences from within-person fluctuations showed that individuals who consistently emphasised priorities involving other people reported higher life satisfaction on average, whereas those who emphasised material concerns reported somewhat lower life satisfaction. The effects of leisure/activities, spiritual/community, and local-environment priorities were not statistically significant once stable individual differences were accounted for. Within-person deviations in priority endorsement were generally small and not reliably associated with corresponding deviations in life satisfaction. These findings suggest that personal priorities primarily reflect relatively stable evaluative orientations rather than short-term drivers of change in life satisfaction.

What matters when?
Age-related reweighting of life domains in life satisfaction

Life satisfaction reflects not only lived circumstances, but the standards used to evaluate them. Using long-running UK panel data with repeated measures of life satisfaction and domain satisfaction (up to 13 annual waves; N ≈ 292,000 person-wave observations from 45,000+ individuals), this study examined whether the evaluative weight of core life domains changes across adulthood. Focusing on health, income, and leisure satisfaction, we tested whether equivalent within-person changes in these domains carry different implications for life satisfaction at different ages. Within–between (REWB) models, cohort-sequential age–period–cohort spline models, dominance analyses, and counterfactual predictions converged on a consistent pattern of age-related reweighting. Leisure satisfaction showed progressively stronger within-person coupling with life satisfaction across adulthood, accounting for an increasing share of explained within-person variance in later life. Income satisfaction predicted within-person change at all ages, but its relative contribution declined steadily with age. Health satisfaction remained a strong and stable predictor, functioning as a background constraint rather than an increasingly dominant evaluative driver. These patterns were not attributable to between-person differences, period effects, or mean-level domain change. Instead, they indicate systematic shifts in how domain information is weighted when individuals evaluate their lives. The findings challenge fixed-weight models of subjective well-being and suggest that life satisfaction is constructed through an evaluative system whose structure changes across the life course.

Well-being as a dynamic and constrained system

This research develops a theoretical framework conceptualising wellbeing as a dynamic system shaped by temporal, energetic, and ecological constraints. Moving beyond additive models of wellbeing, the work examines how experiences, activities, and psychological processes interact over time to produce both gains and costs. Using longitudinal and within-person data, the programme investigates how individuals navigate trade-offs between enjoyment, growth, and sustainability, and how these dynamics vary across contexts and personalities. This work contributes to a more realistic, systems-oriented understanding of wellbeing that emphasises limits, adaptation, and the conditions under which positive functioning can be maintained.

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.

public mental health

Dual-continua mental health across Scottish communities

This project tests the dual-continua model of mental health using large-scale longitudinal survey data from Scotland, examining mental wellbeing and psychological distress as related but distinct dimensions of functioning. Using secondary analysis of national datasets (e.g., Understanding Society), the project applies bivariate growth curve modelling and multilevel modelling to assess how wellbeing and distress co-evolve over time and vary across individuals and communities. By modelling trajectories across multiple waves and embedding individuals within geographic contexts (e.g., data zones), the project evaluates whether the determinants and distributions of wellbeing and distress diverge at the population level. This work contributes to public mental health research by providing a rigorous, data-driven test of the dual-continua framework and informing place-based approaches to mental health promotion and prevention.

Public mental health as a complex system

This programme conceptualises public mental health as a complex, multi-level system shaped by interactions between individuals, communities, and structural conditions. Drawing on systems science and population health frameworks, it moves beyond individual-level models to examine how mental health emerges from dynamic relationships between social infrastructure, inequalities, and institutional processes. Using quantitative modelling, simulation approaches, and policy-oriented analysis, the research tests how population mental health evolves under different system configurations. In particular, it distinguishes between downstream clinical interventions, which act on existing cases, and upstream structural changes, which shape incidence, recovery, and transitions into flourishing. Across modelling and empirical applications, findings show that expanding treatment alone produces limited population-level change under sustained incidence, whereas upstream interventions generate larger and more durable improvements in both mental health and equity. This programme advances a systems-based approach to public mental health by identifying prevention, infrastructure, and collective capability as key levers for improving population outcomes at scale.

Community data spine for population mental health

This project develops a reproducible “community data spine” linking small-area data across the UK (e.g., LSOAs and Data Zones) to create an integrated, place-based dataset for population mental health research. The spine brings together diverse data sources (including socioeconomic indicators, community infrastructure, environmental features, and service access) into a harmonised framework aligned at a common geographic level. Using reproducible data pipelines and open workflows, the project enables scalable linkage, aggregation, and analysis of contextual factors shaping mental health and wellbeing. This infrastructure supports advanced place-based modelling, including multilevel and spatial analyses, to examine how environments influence mental health outcomes across communities. The broader aim is to provide a transparent and extensible data foundation for research, policy, and prevention-focused approaches to population mental health.

well-being meta-science

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.

When brevity met diversity:
Evaluating the TIPI in global well-being research

Large-scale global surveys increasingly include personality measures to test theoretically rich models of well-being, yet little work has evaluated whether commonly used ultra-brief personality inventories are psychometrically adequate for such purposes across cultural contexts. Using two-wave data from the Global Flourishing Survey (GFS; N > 200,000 across 22 countries), this study combines longitudinal modeling with cross-cultural psychometric diagnostics to assess both substantive personality–well-being processes and the methodological viability of the Ten Item Personality Inventory (TIPI) in global research. We employed cross-lagged panel models to examine whether Big Five personality traits predict subsequent well-being indirectly through basic psychological need satisfaction (competence, autonomy, and relatedness), and we assessed how personality traits relate to discrepancies between current and anticipated life evaluations. To evaluate the robustness and interpretability of these associations, we conducted a series of measurement-focused analyses, including country-specific estimation of internal consistency for each TIPI trait, country-level regression of trait–well-being slopes, and meta-regression of these slopes on Hofstede’s individualism–collectivism index. We further tested the sensitivity of all key findings to measurement specification by adjusting for cross-national variability in reliability and by re-estimating models using only positively keyed personality items to remove reverse-wording artefacts. Across analytic approaches, basic psychological need satisfaction exhibited stable and theoretically coherent associations with multiple well-being outcomes over time. In contrast, personality–well-being associations were uniformly small and highly sensitive to measurement specification, with several apparent cross-cultural moderation effects substantially attenuated or altered after accounting for reliability differences across countries. These patterns indicate that variation in measurement precision, rather than substantive psychological differences, can meaningfully shape inferences about personality–well-being relations in global datasets. By integrating longitudinal modeling with systematic psychometric diagnostics, this study demonstrates how ultra-brief personality measures can constrain or distort substantive conclusions in cross-cultural well-being research. The findings highlight the importance of explicitly evaluating reliability and measurement functioning when personality traits are used as predictors in large, diverse surveys and provide methodological guidance for future global studies of personality and flourishing.

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