Constructing Stories of Self-Growth: How Individual Differences in Patterns of Autobiographical Reasoning Relate to Well-Being in Midlife


  • This research was supported by the Foley Center for the Study of Lives and by a MIDUS Pilot Grant from the National Institutes of Health/National Institute on Aging, Grant P01AG020166 (Integrative Pathways to Health and Illness). This research used interview transcripts from the Social Responsibility in Midlife 1995 data set (made accessible in 2002, machine-readable data files), originally collected by A. Colby and made available through the archive of the Henry A. Murray Research Center of the Radcliffe Institute for Advanced Study, Harvard University, Cambridge, Massachusetts (producer and distributor). We thank Anne Colby and Mary Anne Machado for their contributions to this article.

concerning this article should be addressed to Jennifer Pals Lilgendahl, Haverford College, 370 Lancaster Ave., Haverford, PA, 19041. Email:


ABSTRACT Although growth has been a central focus in narrative research, few studies have examined growth comprehensively, as a story that emerges across the interpretation of many events. In this study, we examined how individual differences in autobiographical reasoning (AR) about self-growth relate to traits and well-being in a national sample of midlife adults (N= 88) who ranged in age from 34 to 68. Two patterns of growth-related AR were identified: (1) positive processing, defined as the average tendency to interpret events positively (vs. negatively), and (2) differentiated processing, defined as the extent to which past events are interpreted as causing a variety of forms of self-growth. Results showed that positive processing was negatively related to neuroticism and predicted well-being even after controlling for the average valence of past events. Additionally, differentiated processing of negative events but not positive events was positively related to openness and predictive of well-being. Finally, growth-related AR patterns independently predicted well-being beyond the effects of traits and demographic factors.