Introduction

Ideally, measuring overconfidence (specifically, overestimation) requires an objective measure of ability to contrast with self-estimates. Administering such parallel tests can costly. Furthermore, if overconfidence is measured as a difference, it remains confounded by the reference ability and self-estimates. Overclaiming is an efficient, unobtrusive technique that compares claiming familiarity of genuine items (Reals) against claiming familiarity with fake items (Foils).

Objectives

To explore how the overclaiming technique distinguishes between respondents’ actual knowledge and their perceived knowledge as predictors of academic performance.

Methodology

Undergraduate students were given both a vocabulary test and then an overclaiming measure of vocabulary, as well as other knowledge tests which included confidence ratings for general knowledge items. Their overall grades in an introductory psychology course were also collected (not self-report). Overconfidence was calculated as the difference between a respondent’s standardized confidence ratings and their standardized correct knowledge score.

Results

Vocabulary ability measured by Overclaiming (Reals-claiming minus Foils-claiming) strongly correlated with a conventional vocabulary ability measure (r(163) = .77***, CI­­_.95 = [.69, .82]), and moderately with course grade, r(163) = .37***, CI­­_.95 = [.23, .50]. Overconfidence was necessarily confounded by confidence and general knowledge measures (e.g. r = .55), yet negatively predicted course grade, r(162) = -.30***, CI­­_.95 = [-.43, -.15]. Reals-claiming captured confidence (r(162) = .33***, CI­­_.95 = [.19, .46]) and Foils-claiming captured overconfidence, r(162) = .24**, CI­­_.95 = [.08, .37]. Regression models showed no overlap between the two associations.

Conclusions

Assessing overconfidence via combined ability and reported confidence is onerous and yields confounded measures which can’t be combined in predictive regression models. Our vocabulary overclaiming technique, despite covering a different knowledge area, captures general confidence and overconfidence in a way that can be combined for predicting academic outcomes.

NOTE: * p < .05, ** p < .01, *** p < .001