Dispositional insight: Its relations with HEXACO personality and cognitive ability

Vries, Reinout E. de; Barends, Ard J.; Köck, F.

doi:10.1016/j.paid.2021.110644

Cited by 9 publications

(17 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some data from the firefighter sample (Holtrop et al, 2021) and the student sample (Wood et al, 2022) have been previously reported. The four data sets were also used in conjunction with a preexisting Dutch sample (de Vries et al, 2021) to assess item-level predictive models. The Dutch sample was obtained as part of the previously mentioned requests for data and consisted of 1,330 (75% female; M age = 20.64 years, SD = 2.84) Dutch-speaking university students who completed the official Dutch translation of the HEXACO-208 and a 24-item version of the ICAR.…”

Section: Methodsmentioning

confidence: 99%

Personality and intelligence: A meta-analysis.

Anglim¹,

Dunlop²,

Wee³

et al. 2022

Psychological Bulletin

View full text Add to dashboard Cite

This study provides a comprehensive assessment of the associations of personality and intelligence. It presents a meta-analysis (N = 162,636, k = 272) of domain, facet, and item-level correlations between personality and intelligence (general, fluid, and crystallized) for the major Big Five and HEXACO hierarchical frameworks of personality: NEO Personality Inventory–Revised, Big Five Aspect Scales, Big Five Inventory–2, and HEXACO Personality Inventory–Revised. It provides the first meta-analysis of personality and intelligence to comprehensively examine (a) facet-level correlations for these hierarchical frameworks of personality, (b) item-level correlations, (c) domain- and facet-level predictive models. Age and sex differences in personality and intelligence, and study-level moderators, are also examined. The study was complemented by four of our own unpublished data sets (N = 26,813) which were used to assess the ability of item-level models to provide generalizable prediction. Results showed that openness (ρ = .20) and neuroticism (ρ = −.09) were the strongest Big Five correlates of intelligence and that openness correlated more with crystallized than fluid intelligence. At the facet level, traits related to intellectual engagement and unconventionality were more strongly related to intelligence than other openness facets, and sociability and orderliness were negatively correlated with intelligence. Facets of gregariousness and excitement seeking had stronger negative correlations, and openness to aesthetics, feelings, and values had stronger positive correlations with crystallized than fluid intelligence. Facets explained more than twice the variance of domains. Overall, the results provide the most nuanced and robust evidence to date of the relationship between personality and intelligence.

show abstract

Section: Methodsmentioning

confidence: 99%

Personality and intelligence: A meta-analysis.

Anglim¹,

Dunlop²,

Wee³

et al. 2022

Psychological Bulletin

View full text Add to dashboard Cite

show abstract

“…We collected data from participants at two time points. At t1, respondents completed the self-report HEXACO personality inventory (Lee & Ashton, 2018) and the Dispositional Insight Test (DIT; De Vries et al, 2021). One week later, at t2, participants completed the assessment game Building Docks (Barends et al, 2022).…”

Section: Current Researchmentioning

confidence: 99%

Construct validity of a personality assessment game in a simulated selection situation and the moderating roles of the ability to identify criteria and dispositional insight

Barends

Vries

2022

Int J Selection Assessment

Self Cite

View full text Add to dashboard Cite

There is scant research on the validity of personality assessment games in selection situations. Therefore, in two experimental simulated selection studies, the construct validity of an assessment game developed to assess honesty‐humility was tested. Both studies found no differences between a control condition and a simulated selection condition on honesty‐humility game scores. Moreover, convergent and discriminant validity with self‐reported personality were not affected by the manipulation. We obtained mixed evidence that individual differences in dispositional insight and the ability to identify criteria influenced the validity of the game. As the validity of the personality assessment game was not significantly affected in the simulated selection context, our findings may imply that well‐designed personality assessment games can be used for high‐stakes selection assessments.

show abstract

“…Previous research has found that conscientiousness is negatively correlated with an index of insufficiently effortful response patterns (Huang et al, 2015). Unlike GMA, however, conscientiousness has not been consistently found to be positively related to rater accuracy in judging others (de Kock et al, 2020; de Vries et al, 2021). However, some individual studies have found positive relationships between conscientiousness and the ability to understand human personality and more accurate in estimating their own ability (Mayer et al, 2020).…”

Section: Individual Differences Among Crowd Membersmentioning

confidence: 99%

Can you crowdsource expertise? Comparing expert and crowd‐based scoring keys for three situational judgment tests

Brown

Grossenbacher²,

Martín‐Raugh

et al. 2021

Int J Selection Assessment

View full text Add to dashboard Cite

This study seeks to answer a simple question: Is it possible to develop a scoring key for a situational judgment test (SJT) without a pool of subject matter experts (SMEs)? The SJT method is widely studied and used for selection in both occupational and educational settings (Oswald et al., 2004;Lievens & Sackett, 2012). SJTs are typically designed to measure procedural knowledge about how to behave effectively in a particular job (Motowidlo et al., 2006). Along these lines, SJT items are typically scored using rational keys based on ratings gathered from SMEs (Whetzel et al., 2020). According to the U.S. Office of Personnel Management, SMEs are defined as a "person with bona-fide expert knowledge about what it takes to do a particular job" (Office of Personnel Management, 2016). Their judgments determine how each item will be scored. This poses a challenge when creating a new assessment because test developers typically need an item pool at least twice as large as the desired length of the final test (Hinkin, 1998;Murphy & Davidshofer, 1998). Each of these items needs a scoring key in order to be evaluated during an initial round of testing. This means that SMEs would likely spend most of their time rating items that will not be used in the final version of the test.Researchers and test developers face several challenges when using SMEs. The criteria for determining who is considered a SME can be vague or inconsistent across studies. When SJTs are developed for a specific occupation, SMEs have included job incumbents, supervisors, customers, and even novices (Weekley et al., 2006). In many cases, these SMEs work in prestigious occupations (e.g., experienced physicians or medical professionals, Lievens & Sackett, 2012;Patterson et al., 2009) which can make them expensive and difficult to recruit. For more construct-driven SJTs (e.g., applied social skills), academic researchers or graduate

show abstract

Dispositional insight: Its relations with HEXACO personality and cognitive ability

Cited by 9 publications

References 29 publications

Personality and intelligence: A meta-analysis.

Personality and intelligence: A meta-analysis.

Construct validity of a personality assessment game in a simulated selection situation and the moderating roles of the ability to identify criteria and dispositional insight

Can you crowdsource expertise? Comparing expert and crowd‐based scoring keys for three situational judgment tests

Contact Info

Product

Resources

About