levels of confidence predicted higher school grades after controlling for age, gender, intelligence, school fees and
parent/C0child family dynamics ( Kleitman et al., 2010 ). That is, teachers who were naı ¨ve to research objectives
assigned higher grades to more confident children as compared with children who were less confident in their
performance.
Three large-scale studies carried out in Singapore, o ne of the best performing countries in PISA surveys,
found similar results. In the Stankov, Lee et al. (2012) study, the raw correlation b etween Mathematics Exam
scores at the end of the year and confidence was .55, which was higher than the correlation with mathematics
anxiety ( /C0.39), self-concept (.25) and self-efficacy (.16). In the Morony et al. (2013) study, mathematics
achievement scores correlated .60 with confidence, /C0.27 with anxiety, .35 with self-concept and .45 with self-
efficacy, and a very similar pattern of correlation s was obtained in every country. Finally, in another
Singaporean sample ( N5600) the correlation between confidence and achievement was .68, whereas self-
efficacy (.41), self-concept (.30) and anxiety ( /C0.33) were all lower in size (Stankov et al., 2014). There can be
no doubts that confidence is the best known non-cognit ive predictor of academic achievement in education.
Furthermore, in all three studies, regression and SEM analyses have shown that confidence captured most of
the predictive variance of the other three self-beliefs co nstructs. This suggests that, in many instances, captur-
ing online confidence levels ‘absolves the researcher f rom employing separate scales of Self-efficacy, Self-
concept and Anxiety’ ( Kleitman et al., 2013 ). In our studies with University students and adults, we
employed measures of personality, thinking dispositions and social attitudes in addition to measures of confi-
dence. In all instances, confidence proved to be the best non-cognitive predictor of cognitive performance
(Kleitman, 2008; Crawford & Stankov, 1996 ).
In contrast, there is limited evidence for the predi ctive validity of Confidence for different types of mal-
adjusted behavior .Want and Kleitman (2006) reported a discrepancy between confidence and accuracy
levels for people suffering from imposterism feelings (intense feelings of phoniness experienced by some
individuals who have achieved a certain level of success; Clance & Imes, 1978 ), such that confidence rat-
ings, but not accuracy, shared a negative relationshi p with detrimental self-evaluations. In other words,
people high on imposterism feelings showed a pronounced ‘gap’ between their confidence and actual per-
formance levels.
Confidence levels are also central to many real-life decision-making processes ( Bruine de Bruin, Parker &
Fischhoff 2007; DeMarree, Petty, & Brinol 2007; Koriat & Goldsmith, 1996; Slovic, Fischhoff & Lichtenstein 1977 ).
For example, Jackson and Kleitman (2014) found confidence levels to be a strong incremental predictor of
decision-making tendencies in their novel Medical Decision-making Test (MDMT) using an undergraduate psy-
chology sample ( N5193). In this test participants diagnosed patients with fictitious, yet supposedly fatal, ill-
nesses and indicate their confidence in the accuracy of each diagnosis. For each patient, participants decide
whether to administer a treatment matching their diagnosis (direct) or request a blood test to make an accurate
diagnosis. Based on Koriat and Goldsmith’s (1996) model, the MDMT captured individual differences in the way
people make decisions based on their own levels of confidence. This allowed for the assessment of five novel
individual decision-making tendencies : optimal (patients cured outright); re alistic (patients cured outright or
tested appropriately); hesitant (patients risking death due to unnecessary testing); incompetent (patients dying
due to incorrect diagnosis and treatment); and congruen t (proportion of patients tr eated). Confidence was a
strong incremental predictor of these t endencies, after taking diagnostic acc uracy, intelligence, personality, cog-
nitive styles, gender, and age into account. In support of this finding, Parker, De Bruin, Yoong, and Willis
(2012) reported that confidence judgments measured with fou r different tests of financial knowledge, financial
sophistication, a hypothetical investment task and a gene r a lk n o w l e d g et e s ta l lp r e d i c t e da c t u a lf i n a n c i a lr e t i r e -
ment planning behavior after accounting for test accuracy , age, gender, whether participants had a Bachelor’s
degree, and income (aged 18 to 88 years; N5491). These findings implied that confidence judgments are valid
measures of the confidence construct that, in turn, shar es a meaningful relationship with habitual decision-
making tendencies.
Results and Comments
Much of the work employing confidence ratings to asse ss online judgments of accuracy has been carried out
with cognitive tests. In our empirical work, we have employed virtually all types of cognitive tests used in
studies of fluid (Gf) and crystallized (Gc) intelligence (see Carroll, 1993 ). These included measures of higher
mental processes, such as memory, creative and critical thinking, and perceptual tests from visual, auditory,
tactile, kinesthetic and movement/sport, olfactory and gustatory modalities. Perceptual tests, like the Line
Length test ( Kleitman & Stankov, 2001; Stankov, Pallier et al., 2012 ) can be used in studies of developmental179 ONLINE PERFORMANCE BASED MEASURES
II. EMOTIONAL DISPOSITIONS