scholarly journals Non-Cognitive Ability, Test Scores, and Teacher Quality: Evidence from 9th Grade Teachers in North Carolina

2012 ◽  
Author(s):  
C. Kirabo Jackson
2010 ◽  
Author(s):  
Jonas W. B. Lang ◽  
Martin Kersting ◽  
Ute R. Hulsheger

2020 ◽  
Vol 14 ◽  
Author(s):  
Yi Ming Li ◽  
Jian Li ◽  
Hong Zou ◽  
Shengnan Wei

Abstract A culture- and age-appropriate instrument for measuring emotion regulation ability is needed for the research and practice of Chinese adolescents’ emotion regulation. This study developed and validated a situational judgment test of emotion regulation ability for Chinese youth (STER-CY). Three samples were recruited, and approximately 4380 5th- to 11th-grade students (but no 9th-grade students) participated in the study. Researchers collected emotional situations and responses based on the life of indigenous samples and examined the reliability and validity of the test scores. The results showed that Cronbach’s alpha and test–retest correlations provided evidence for the reliability of the test scores. Exploratory and confirmatory factor analysis supported unidimensionality. Construct validity was further verified by convergent and discriminant validity. Criteria-related validity was confirmed by the correlations between this test and some outcome variables related to emotion regulation. It was also found that girls scored higher on this test than boys did and that emotion regulation ability significantly increased from 5th to 7th grade, but it did not improve from 7th to 11th grade. Considered together, these findings showed that the STER-CY is a psychometrically sound measure of emotion regulation ability and can be used in future research and practice.


2018 ◽  
Author(s):  
Roxanne Connelly ◽  
Vernon Gayle

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.


2018 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Michael D. Nunez ◽  
Dirk Hagemann ◽  
Joachim Vandekerckhove

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.


2021 ◽  
Author(s):  
Matt Brown ◽  
Michael Grossenbacher ◽  
Zachary Warman

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


Author(s):  
Ian J. Deary

‘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.


2018 ◽  
Author(s):  
Anna-Lena Schubert ◽  
Michael D. Nunez ◽  
Dirk Hagemann ◽  
Joachim Vandekerckhove

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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