Vocabulary Size as a Verbal Intelligence Index: Application of the Express Methods of an Estimating Vocabulary Size

Maslennikova, Ekaterina; Feklicheva, Inna; Esipenko, Elena; Sharafieva, K.R.; Ismatullina, Victoria; Golovin, G.V.; Miklashevskiy, A.A.; Chipeeva, Nadezda; Soldatova, E.L.

doi:10.14529/psy170306

Cited by 3 publications

(2 citation statements)

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“…Passive vocabulary volume was assessed with the short version of ‘My Vocab’ test and was used in a current study as a proxy to verbal ability (correlations between it and other measures of verbal ability were shown to range from 0.39 to 0.55; (Maslennikova et al, 2017). The ‘My Vocab’ (Golovin, 2015) is similar in design to the Mill Hill Vocabulary Scale (Raven, 1958).…”

Section: Methodsmentioning

confidence: 99%

This is the way: Network perspective on targets for spatial ability development programmes

Likhanov

Maslennikova

Costantini

et al. 2022

Brit J of Edu Psychol

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Background: Spatial ability (SA) was shown to be important for success in different fields, including STEM. Recent research suggested that SA is a unitary construct, rather than a set of related skills. However, it is not clear how individual differences in different facets of SA emerge, and how they relate to variance in general cognitive ability. Aims: The aim of the present study was threefold: 1) to examine the structure of SA testing nine theoretical models; 2) to explore the relation between 16 different facets of SA with general cognitive ability; and 3) to identify central facet(s) within the network of SA -with most links and/or strongest links to other facets. Sample: The study participants were 958 university students from Russia. Methods: The study used a comprehensive battery of 16 SA tests and a verbal ability measure. Results: Results supported previous research, suggesting moderate overlap between all SA facets. Factor analysis suggested several potential structures, with similar fit indices for five different theoretically driven models, including split into small-and large scale; partially independent manipulation, visualization and navigation facets. Confirmatory factor analysis, mediation and network analyses showed spatialThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Methodsmentioning

confidence: 99%

This is the way: Network perspective on targets for spatial ability development programmes

Likhanov

Maslennikova

Costantini

et al. 2022

Brit J of Edu Psychol

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“…Verbal cognitive abilities were estimated using a short version of the vocabulary test “My Vocab” [ 45 ] and the verbal scales of the Universal Intellectual Test [ 46 ].…”

Section: Methodsmentioning

confidence: 99%

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity

et al. 2021

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The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.

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Emotional, Cognitive and Social Factors Relating to the Efficiency of Solving Spatial Tasks

Esipenko¹,

Beloplotova²,

Полякова³

2020

Pedagogical Review

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Пространственные способности могут предсказывать успешность человека в областях, связанных с наукой и математикой. С целью изучения влияния эмоциональных, когнитивных и социальных факторов на решение четырех разных типов пространственных задач впервые было проведено исследование, куда вошли юноши и девушки естественно-научного и гуманитарного направлений обучения. В результате выяснилось, что невербальный интеллект вносит значимый вклад во все анализируемые пространственные задачи; кроме того, рабочая память и направление обучения вносят значимый вклад в задания, оценивающие отношение паттернов и пространственную визуализацию. Другие факторы не оказали такого влияния. Полученные результаты имеют теоретическую и практическую значимость. Psychologists are interested in the study of spatial and general cognitive abilities. Known that they can predict success in science, technology, engineering and mathematics (STEM) areas. Nevertheless, a comprehensive study of the influence of various factors on spatial abilities was not held. To examine the factors that can be associated with solving spatial problems, the study was conducted on a Russian-language sample, which included 161 young men and women from different study majors (STEM and non-STEM). Studied factors were emotional, cognitive and social. A feature of the study is that a comprehensive examination of factors was carried out in the study of spatial tasks such as “Mechanical reasoning and spatial relations”, “Paper folding”, “Pattern assembly”, “Shape rotation”. As a result of the regression analysis (with the inclusion of all the studied factors), it turned out that for each spatial problem the presented models were significant. All models have the same percentage of explained variance are approximately 20%. Non-verbal intelligence makes a great contribution to “Mechanical reasoning and spatial relations” and “Shape rotation”, and also for non-verbal intelligence, working memory, and the direction of learning make a great contribution to “Pattern assembly” and “Paper folding”. Emotional and social factors were not significant. Findings will enable a better understanding of what contributes to spatial abilities.

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Vocabulary Size as a Verbal Intelligence Index: Application of the Express Methods of an Estimating Vocabulary Size

Cited by 3 publications

References 0 publications

This is the way: Network perspective on targets for spatial ability development programmes

This is the way: Network perspective on targets for spatial ability development programmes

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity

Emotional, Cognitive and Social Factors Relating to the Efficiency of Solving Spatial Tasks

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