Familial and personal characteristics profiles predict bias in academic competence and impostorism self-evaluations

ABSTRACT

The goal of this longitudinal study was to explore the familial and personal characteristics that are potentially shared by two related phenomena observed in students: negative bias in self-evaluation of academic competence and impostorism. Specifically, this study aimed to examine whether the same set of characteristics (parental overprotection, conditional parental support, test anxiety, concern over mistakes and self-esteem) could be combined to predict both phenomena in high school students. To do so, these characteristics were first measured in 648 7^th and 8^th graders. In the three following years, students’ negative bias and impostorism were assessed. A latent profile analysis revealed a two-class model of characteristics. One group presented a “negative” pattern characterized by high parental overprotection, conditional parental support, test anxiety and concern over mistakes, and low self-esteem. Another group presented a “positive” pattern: students’ scores on all familial and personal variables were more favorable. As hypothesized, membership in the “negative” profile predicted negative bias and impostorism at T2, T3, and T4. This highlights the principle of multifinality and suggests that the two phenomena might be distinct aspects of a single broader issue such as a tendency toward a biased interpretation of information about one’s own competence, or self-protection.

KEYWORDS:

• Self-evaluation bias
• impostorism
• developmental antecedents
• latent profile analysis

Acknowledgments

The authors would like to thank the schools and parents who agreed to collaborate, and especially the students who participated in this study year after year.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. In their Monte Carlo simulation study aiming to determine which fit indexes are the most efficient in establishing the optimal number of profiles in finite mixture modeling, Nylund et al. (2007) showed that the BIC performed better than the AIC and the SSBIC as information criteria, while the performance of the BLRT exceeded that of the LMR-LRT as a likelihood ratio test. For this reason, in the current study, the values of the BIC and the BLRT were preferred over those of other indexes to determine the optimal number of latent profiles.

Additional information

Funding

This work was supported by the Social Sciences and Humanities Research Council of Canada [435-2013-0969].