Cognitive Ability Testing and Adverse Impact in Selection: Meta-analytic Evidence of Reductions in Black-White Differences in Ability Test Scores Over Time

2011 ◽  
Author(s):  
Stephen A. Woods ◽  
Claire Hardy ◽  
Yves R. F. Guillaume
Keyword(s):  
Cognitive Ability ◽  
Test Scores ◽  
Adverse Impact ◽  
Ability Test ◽  
Over Time ◽  
Ability Testing

Range shrinkage of cognitive ability test scores in applicant pools

2010 ◽  
Author(s):  
Jonas W. B. Lang ◽  
Martin Kersting ◽  
Ute R. Hulsheger
Keyword(s):  
Cognitive Ability ◽  
Test Scores ◽  
Ability Test

scholarly journals An investigation of Social Class Inequalities in General Cognitive Ability in Two British Birth Cohorts

2018 ◽  
Author(s):  
Roxanne Connelly ◽  
Vernon Gayle
Keyword(s):  
Social Class ◽  
Cognitive Ability ◽  
Test Scores ◽  
Large Scale ◽  
Parental Education ◽  
General Cognitive Ability ◽  
Birth Cohorts ◽  
Ability Test ◽  
British Cohort Study ◽  
Nationally Representative

The ‘Flynn effect’ describes the substantial and long-standing increase in average cognitive ability test scores, which has been observed in numerous psychological studies. Flynn makes an appeal for researchers to move beyond psychology’s standard disciplinary boundaries and to consider sociological contexts, in order to develop a more comprehensive understanding of cognitive inequalities. In this article we respond to this appeal and investigate social class inequalities in general cognitive ability test scores over time. We analyse data from the National Child Development Study (1958) and the British Cohort Study (1970). These two British birth cohorts are suitable nationally representative large-scale data resources for studying inequalities in general cognitive ability.We observe a large parental social class effect, net of parental education and gender in both cohorts. The overall finding is that large social class divisions in cognitive ability can be observed when children are still at primary school, and similar patterns are observed in each cohort. Notably, pupils with fathers at the lower end of the class structure are at a distinct disadvantage. This is a disturbing finding and it is especially important because cognitive ability is known to influence individuals later in the lifecourse.

scholarly journals Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

2018 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Michael D. Nunez ◽  
Dirk Hagemann ◽  
Joachim Vandekerckhove
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Processing Speed ◽  
Cognitive Abilities ◽  
Test Scores ◽  
Cognitive Modeling ◽  
Higher Order ◽  
Neural Processing ◽  
Ability Test ◽  
Evidence Accumulation

AbstractPrevious research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

scholarly journals Investigating the Conflicting Findings for Mobile Device Effects on Cognitive Test Scores: Evidence for Selection Bias in an Operational, Applicant Sample

2021 ◽  
Author(s):  
Matt Brown ◽  
Michael Grossenbacher ◽  
Zachary Warman
Keyword(s):  
Propensity Score ◽  
Educational Attainment ◽  
Cognitive Ability ◽  
Mobile Devices ◽  
Selection Bias ◽  
Mobile Device ◽  
Test Scores ◽  
Cognitive Test ◽  
Ability Test ◽  
Post Stratification

Past studies have reported inconsistent results regarding the effect of mobile devices on cognitive ability test scores. We investigate selection bias as a potential explanation for cognitive ability test score differences between applicants using mobile or non-mobile devices. The likelihood of using a mobile device was predicted by educational attainment (R = .71) and O*NET codes (R = .84), both of which are also related to cognitive ability. Controlling for selection bias using propensity score weights reduced the standardized mean difference in test scores from d = 0.58 to d = 0.25 in a sample of 76,948 job applicants. The mobile device effect was further minimized when weighting using post-stratification (d = 0.10). This suggest that contradictory findings in past studies on mobile device effects are likely explained by selection bias in non-experimental studies. In practice, applicants with greater educational attainment were less likely to complete pre-hire assessments with a mobile device and tend to score higher on cognitive tests. Mobile use was also more common among applicants for lower complexity jobs which tend to attractapplicant pools with lower cognitive test scores on average. Therefore, it is important to control for demographic and occupational differences between mobile and non-mobile test takers when analyzing operational data. Propensity score weighting and post-stratification are useful for reducing the impact of selection bias in real-world, observational data. We also strongly recommend the use of random assignment in order to prevent selection bias in future research and test development

9. Is intelligence increasing generation after generation?

2020 ◽  
pp. 113-125
Author(s):  
Ian J. Deary
Keyword(s):  
Cognitive Ability ◽  
20Th Century ◽  
Test Scores ◽  
Intelligence Tests ◽  
Ability Test ◽  
Flynn Effect ◽  
First Response ◽  
Successive Generations ◽  
Mental Tests

‘Is intelligence increasing generation after generation?’ discusses the ‘Flynn effect’ of rising IQ. James Flynn noticed that tables of norms for intelligence tests had to be changed every several years. Newer generations were scoring too well on the tests, by comparison with people who were their age some years before. The first response to the Flynn effect suggests that it is real, marking an actual improvement in brain power in successive generations across the 20th century. The second response states that people are not more intelligent, but have become more familiar with the mental tests’ materials. Something in the environment or culture of many countries across the 20th century has led to cognitive ability test scores increasing substantially.

scholarly journals Individual differences in cortical processing speed predict cognitive abilities: A model-based cognitive neuroscience account

2018 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Michael D. Nunez ◽  
Dirk Hagemann ◽  
Joachim Vandekerckhove
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Processing Speed ◽  
Cognitive Abilities ◽  
Test Scores ◽  
Cognitive Modeling ◽  
Higher Order ◽  
Neural Processing ◽  
Ability Test ◽  
Evidence Accumulation

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks and cognitive ability test scores. However, only a negligible part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

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