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Fellowship Description

Research program title

Psychosocial Drivers of Optimal Functioning Across Life Transitions: A Substantive-Methodological Synergy of Research and Training

Reference number



Program description

Self-concept is a key broadband driver of successful development, making it critical to achieve a clear understanding of the mechanisms underpinning continuity and change in self-concept trajectories across critical life transitions. The self-equilibrium hypothesis underlines the importance of having a strong core self, which is defined as a positive and developmentally stable self-concept. This proposal investigates this hypothesis across the transition into the workforce, into a new job, as well as across other critical life transitions. We also consider the extent to which this hypothesis generalizes to motivation and commitment. To better elucidate the rich tapestry of processes underpinning self-concept formation and crystallization, we also propose the self-complexity hypothesis, which suggests that the presence of a strong core self-concept needs to be anchored in multiple areas of self-strength itself nurtured by need supporting and thwarting conditions present in one’s environment. Finally, we document developmental outcomes related to retention, performance, psychological well-being and physical health. This proposal capitalizes on advanced longitudinal person-centered analyses (growth mixture analysis, mixture time series analysis) to test these research questions, on already existing data sets, and on the recruitment of two new cohorts of participants (students and new employees) followed over time using online questionnaires.

Academic qualifications required

PhD in Psychology or related discipline, with previous research experience related to the coordination of data collection procedures.

The ideal candidate would have a background in social or organizational psychology, although candidates with a background in developmental or educational psychology will also be considered.

The candidate should be familiar with longitudinal data analyses (e.g., latent curve models) or person-centered analyses (e.g., latent profile analyses, latent transition analyses). Candidates familiar with both (e.g., growth mixture analyses) will be given priority.

Bilingual (English-French) candidates will be given priority.

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