Is Good Character All that Counts? A Comparison Between the Predictive Role of Specific Strengths and a General Factor of “Good Character” Using a Bifactor Model

Abstract

Character strengths have been found to consistently predict many positive psychological outcomes, such as well-being, life satisfaction, and mental health, but research on the topic is still at its infancy and some methodological limitations must be overcome to better understand what character strengths are and what is their role. One main issue concerns the structure of character strengths and virtues, which may undermine the credibility and replicability of previous findings. Using two different samples (with 13,439 and 944 participants), we confirm that character strengths can be well described by a bifactor model reflecting the simultaneous existence of a general factor of ‘good character’ and the 24 specific character strengths. We found that the general factor consistently predicts participants’ life satisfaction, mental health, and distress symptoms. In addition, we show that the specific character strengths (with the few exceptions represented by gratitude, hope, and zest) do not predict life satisfaction and mental health above and beyond the general factor. These results highlight the need to better understand what this general factor really represents to finally capture the mechanisms linking character strengths between each other and with external outcomes. Implications for the measurement and interpretation of character strengths and for strength-based interventions are discussed.

20 years of character strengths. A Bibliometric review

“…Finally, the emergence of the measurement/validity cluster during the second wave of research, highlights the challenge of measuring strengths and virtues that was not immediately addressed, but that is now emerging as a key issue in the literature (Feraco et al, 2022(Feraco et al, , 2023Han & McGrath, 2023;McGrath, 2022;McGrath et al, 2020;Miller, 2019;Ruch & Proyer, 2015;Ruch & Stahlmann, 2024).…”

SEMbeddings: how to evaluate model misfit before data collection using large-language models

“…The cosine similarities ranged from .72 to 1, indicating some degree of similarity among all items. This is expected given the typical positive correlation among all VIA-IS-P items (Feraco et al, 2023) and the fact that character strengths are all morally-valued and positive traits (Peterson & Seligman, 2004). To align these values more closely with correlations, we scaled them to range between 0 (indicating the least similar items) and 1 (indicating identical items).…”