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Validity of Self-Evaluation of Ability: A Review and Meta-Analysis

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Paul Alex Mabe at Augusta University
Paul Alex Mabe
Stephen G. West
Stephen G. West
Stephen G. West
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Abstract

Reviews 55 studies in which self-evaluations of ability were compared with measures of performance to show a low mean validity coefficient (mean r = .29) with high variability ( SD = .25). A meta-analysis by the procedures of J. E. Hunter et al (1982) calculated sample-size weighted estimates of –- r and SDr and estimated the appropriate adjustments of these values for sampling error and unreliability. Among person variables, high intelligence, high achievement status, and internal locus of control were associated with more accurate evaluations. Much of the variability in the validity coefficients ( R = .64) could be accounted for by 9 specific conditions of measurement, notably (a) the rater's expectation that the self-evaluation would be compared with criterion measures, (b) the rater's previous experience with self-evaluation, (c) instructions guaranteeing anonymity of the self-evaluation, and (d) self-evaluation instructions emphasizing comparison with others. It is hypothesized that conditions increasing self-awareness would increase the validity of self-evaluation. (84 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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... While one could argue that no-one knows their abilities better than the respective people themselves, previous evidence suggests otherwise, with correlations between SAI and psychometric intelligence ranging from r = −0.25 to r = 0.85 (Freund and Kasten 2012). Previous meta-analytic accounts revealed a moderate correlation between both variables (Freund and Kasten 2012;Mabe and West 1982;Zell and Krizan 2014). However, self-assessment accuracy was theorized to be influenced by various aspects related to characteristics of SAI measurement, participants, and test conditions (Freund and Kasten 2012;Zell and Krizan 2014). ...
... While many of these potentially moderating variables have previously been tested (e.g., Freund and Kasten 2012;Mabe and West 1982;Zell and Krizan 2014), their interactions with different analytical approaches in primary or meta-analytical studies have so far not been systematically examined. Study outcomes may vary considerably depending on which variables are analyzed and how these analyses are carried out (Voracek et al. 2019). ...
... To assess the generality of our findings, we used specification curve and combinatorial meta-analyses in our multiverse approach to assess the influence of various analytical choices in terms of which data are analyzed and how this is done. While many potentially moderating variables have previously been tested (e.g., Freund and Kasten 2012;Mabe and West 1982), their interactions with different analytical approaches in primary or meta-analytical studies have so far not been systematically examined. There are many (reasonable) ways in which primary or meta-analytical studies can be designed. ...
Mirror, Mirror on the Wall: A Meta-Analysis on the Validity of Self-Assessed Intelligence through the Lens of the Multiverse
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  • Aug 2024
Examining the relationship between self-assessed intelligence (SAI) and psychometric intelligence (IQ) is essential for understanding how people's self-evaluations reflect their actual intelligence. Various factors, such as SAI measurement methods, participant characteristics, and testing conditions have been hypothesized to moderate the SAI-IQ link, yet the generality of this association remains unclear. Here, we provide evidence for SAI and IQ associations based on 278 effect sizes from 115 independent samples (N = 36,833) using a multi-level meta-analysis, revealing a moderate positive correlation (r = 0.30; 95% CI [0.27, 0.33]). Multiverse analyses demonstrated remarkable stability of this effect, with most summary effect specifications yielding significant positive correlations (96%), averaging r = 0.32. Notably, ability domain and sample type emerged as significant moderators, with numerical ability showing stronger correlations compared to general cognitive, verbal, and spatial abilities. Importantly, our study found that correlations in student samples were significantly higher than those in general samples. Our findings show a moderate positive association of SAI with IQ, unaffected by participant sex, publication year, administration order, neuroticism, and self-assessment method, yet significantly moderated by ability domain and sample type. Our results illustrate the importance of feedback in educational settings to help students accurately assess their cognitive abilities.
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... This decision is motivated by the fact that substantially different correlations with other variables were identified in meta-analyses examining subjective adaptive performance ratings and objective adaptive performance scores (Christian et al., 2017;Stasielowicz, 2019Stasielowicz, , 2020. Furthermore, including objective performance measures addresses the problem of limited validity of self-report measures (Heidemeier & Moser, 2009;Mabe & West, 1982), which studies examining the relationship 7 between focus on opportunities and performance are usually based on. Thus, the present study contributes to solving the problem of the limited utility of findings for organizational and educational processes. ...
... While people's positive perceptions of their occupational future (e.g., opportunities, perceived remaining time) have been linked to better task and contextual performance , the findings are usually based on self-reported performance, which has limited validity (Heidemeier & Moser, 2009;Mabe & West, 1982). Furthermore, research examining the relationship between perceived occupational opportunities and specific performance facets was scarce. ...
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... Another factor was that educators rated their perceived preknowledge lower than actual pre-knowledge measured (M = 2.4, SD = 0.6 compared to M = 2.8, SD = 1.2). Considering this, and the tendency for people to overestimate their ability, it suggests that it is likely that the pre-knowledge questions used in this study were aimed too low [40,41]. Another factor to consider is the differences in perceived versus actual post-knowledge scores (M = 3.2, SD = 1.4 compared to M = 4, SD = 0.8), suggesting either educators substantially overestimated their knowledge, the questions asked did not capture the breadth of knowledge obtained, or a combination of both. ...
... Another factor to consider is the differences in perceived versus actual post-knowledge scores (M = 3.2, SD = 1.4 compared to M = 4, SD = 0.8), suggesting either educators substantially overestimated their knowledge, the questions asked did not capture the breadth of knowledge obtained, or a combination of both. Given only six pre-knowledge and six post-knowledge questions were asked, and that people tend to overestimate their ability, a combination of both is likely [40,41]. ...
Supporting the Mental Health Needs of Neurodivergent Students: Outcomes of a Mental Health Literacy Program for Primary School Educators
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... However, psychological research has shown that personal assessments of one's own level of ability are generally flawed (e.g., Kruger and Dunning 1999), with most studies reporting low correlations between estimated and actual ability (Mabe and West 1982). The problematic nature of self-ratings are recognized in nursing (e.g., Kajander-Unkuri et al. 2015;Liang et al. 2021), and in other healthcare disciplines (e.g., physicians (Davis et al. 2006), psychiatry (Sowden et al. 2017;pharmacy (Gabbard and Romanelli 2021)). ...
Nurses’ models of spiritual care: Predictors of spiritual care competence
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Objectives Previous studies have shown that nurses’ spiritual care competence is related to characteristics of personal spirituality, training adequacy, and comfort, confidence, and frequency of provision of spiritual care. However, these studies assumed that all participants understood spiritual care in the same way, and used self-ratings of spiritual care competence, which are problematic. Our previous study found that spiritual care was understood in 4 qualitatively different ways that can be arranged in order of competence. This study aimed to re-examine the relationships between nurse characteristics and spiritual care competence, using spiritual care understanding as a proxy for competence. Methods Data was collected from a convenience sample of nurses who completed an anonymous, online survey. The survey provided qualitative data about what spiritual care means for them. The survey also provided quantitative data regarding nurse characteristics. This study created sub-groups of nurses based on their understanding of spiritual care, and used the quantitative data to construct a profile of nurse characteristics for each sub-group. Kruskal–Wallis statistical tests determined whether nurse characteristics differed across the 4 sub-groups. Results Spiritual care competence was not related to confidence or comfort in providing spiritual care. Relationships with spirituality, training adequacy, and frequency of provision of spiritual care were not linear; i.e., higher competence did not always correspond with higher scores of these characteristics. Significance of results The results raise concerns about the construct validity of using comfort and confidence as estimates of spiritual care competence. That the relationships between competence and spirituality, training adequacy, and frequency of spiritual care provision was not as linear as portrayed in extant literature, suggests that outcomes of training may depend on the type of spiritual care understanding subscribed to by training participants. The findings offer insights about how nurses could achieve high levels of spiritual care performance.
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... Second, our data stemmed mainly from participants' self-evaluations. Although self-evaluation is widely used in psychology and management research, self-reports can be influenced by individual subjectivity biases, like social-approval deviation [61]. To enhance the objectivity and accuracy of this research, future researchers might consider co-facilitating other assessments, like peer evaluations. ...
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... The relatively recent epidemic of meta-analysis studies (see, for example, Andrews, et a1., 1980;Kazrin, et al , 1979;Kulik, et al , , 1980;Mabe and West, 1982;Resenthal and Rubin, 1978;and Smith and Glass, 1977) is providing the educational researcher with a much-needed quantitative and qualitative synthesis of research findings as well as a multitude of measures of effectiveness of treatments (Glass, 1976;Glass, McGraw and Smith 1981;Hedges, 1982;Hunter, Rosenthal and Rubin 1982; Kraemer and Andrews, 1982;and Schmidt and Jackson, 1982 And only because tradition has such a firm grip on them have they bothered to mention the probability of a Type I Error (a) and then in an implicit form like, "p <.05". ...
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A recent meta-analysis on ability grouping by Kulik and Kulik (1982) was used as an example to demonstrate how a researcher might plan for adequate sample sizes and power in future research. Data sufficient to estimate minimum sample sizes and power were gleaned from the meta-analysis study or obtained from its authors. Distributions of harmonic mean sample size and estimated power were displayed and ranges of estimates for each were presented using effect sizes from the meta-analysis with fixed alpha. Although effect sizes were relatively symmetric about .10, power and harmonic mean sample sizes were quite skewed. Recommendations for major professors, re- searchers and journal editors were made to assist in the evaluation and planning of research.
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... This finding aligns with previous findings in the fields of psychology (e.g. Dunning, 2011;Mabe and West, 1982) and educational psychology (e.g. Magnus and Peresetsky, 2018;Schl€ osser et al., 2013). ...
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... A meta-analysis by Mabe and West (1982) of 55 studies on the self-evaluation of people's abilities or performance in various fields found that the average correlation between the two was 0.29 (standard deviation, 0.25). When Zell and Krizan (2014) synthesized 22 meta-analyses that measured the correlation between the self-evaluation of ability and actual performance, they found a mean correlation of 0.29 (standard deviation, 0.11). ...
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... This could condition the ability to generalize the results, and, as mentioned above, it would be appropriate to increase the sample with participants from different contexts. In addition to the sampling limitation, there is the limitation of any survey or self-report research (Karpen, 2018;Mabe & West, 1982), even with validated instruments. ...
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