First Publication of Subtests in the Stanford-Binet 5, WAIS-IV, WISC-V, and WPPSI-IV

Gibbons, Aisa; Warne, Russell T.

doi:10.31234/osf.io/9z8xv

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The items on the ICAR test are typical for written intelligence tests. All item types have appeared on intelligence tests for at least 50 years and are well established methods for measuring intelligence in examinees [ 78 ].…”

Section: Methodsmentioning

confidence: 99%

Factor structure of intelligence and divergent thinking subtests: A registered report

2021

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Psychologists have investigated creativity for 70 years, and it is now seen as being an important construct, both scientifically and because of its practical value to society. However, several fundamental unresolved problems persist, including a suitable definition of creativity and the ability of psychometric tests to measure divergent thinking—an important component of creativity—in a way that aligns with theory. It is this latter point that this registered report is designed to address. We propose to administer two divergent thinking tests (the verbal and figural versions of the Torrance Tests of Creative Thinking; TTCT) with an intelligence test (the International Cognitive Ability Resource test; ICAR). We will then subject the subscores from these tests to confirmatory factor analysis to test which of nine theoretically plausible models best fits the data. When this study is completed, we hope to better understand whether the degree to which the TTCT and ICAR measure distinct constructs. This study will be conducted in accordance with all open science practices, including pre-registration, open data and syntax, and open materials (with the exception of copyrighted and confidential test stimuli).

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Section: Methodsmentioning

confidence: 99%

Factor structure of intelligence and divergent thinking subtests: A registered report

2021

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“…A pszichológiában, az oktatásban és segédtudományaikban a 19. század vé gétől használnak teljesítményteszteket a kognitív képességek mérésére (Gibbons & Warne, 2019). A pszichológia egyik legjobban replikálódó ered ménye az a tény, hogy a teljesítménytesztek eredményei gyakorlatilag ki vétel nélkül pozitívan korrelálnak, azaz az egyik teljesítményteszten az át lagnál jobban teljesítő személy az összes többi teszten is az átlagnál jobban teljesít (Spearman, 1904;Warne & Burningham, 2019).…”

Section: Az Intelligenciaunclassified

Kognitív epidemiológia – Az intelligenciaszint prospektív összefüggése a szomatikus és pszichiátriai betegségrizikóval

Peter

2021

Mental

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A kognitív epidemiológia az intelligencia és az egészségi állapot összefüggésének tudo- mánya. A modern, sokszor több százezer fős, teljes populációkon végzett kognitív epide- miológiai vizsgálatok eredményei alapján a magasabb premorbid intelligencia gya- korlatilag valamennyi mentális betegség, illetve pszichiátriai probléma alacsonyabb kockázatával függ össze. A magasabb premorbid intelligencia a halálozás, a szív- és ér- rendszeri betegségek, a metabolikus betegségek, a rossz egészség-magatartás és számos kisebb népegészségügyi jelentőségű betegség előfordulásával is negatívan függ össze; a légzőszervi betegségekkel és a dohányzáshoz nem köthető daganatokkal azonban gyen- ge vagy hiányzik az összefüggés. A mentális betegségekkel való összefüggést nem, a szo- matikus betegségekkel és a mortalitással való összefüggést azonban részben mediálják a felnőttkori szocioökonómiai státusz mutatói. A speciális vizsgálati elrendezések – úgymint ikerkontroll-vizsgálatok, pszeudoexperimentális vizsgálatok, valamint a mendeli ran- domizáció módszerét használó molekuláris genetikai vizsgálatok – eredményei arra utal- nak, hogy az intelligencia és az egészség közötti kapcsolat jelentős részét genetikai ténye- zők közvetítik, de a szomatikus egészségre a magasabb intelligencia következményeként elérhető jobb szocioökonómiai státusz is szerény hatást gyakorol.Cognitive epidemiology is the science of the relationship between intelligence and health. Modern studies of cognitive epidemiology, often with samples of several hundreds of thousands of individuals, have revealed that higher premorbid intelligence is associated with a lower risk of virtually all of mental illnesses and psychiatric problems. Higher premorbid intelligence is also associated negatively with the incidence of mortality, circulatory illness, metabolic illness, poor health behavior and many diseases of lower epidemiological significance, but its relationship to respiratory illness and non-smoking related cancers is weaker or non-existent. Indicators of adult socioeconomic status do not mediate the association between intelligence and mental illness, but they do partially mediate the relationship with somatic illness and mortality. Studies with special designs -twin control studies, pseudo-experimental studies and molecular genetic studies using Mendelian randomization – suggest that the relationship between intelligence and health is heavily mediated by genetic factors, but somatic health may be modestly but causally improved by better social status as a consequence of higher intelligence.

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“…Some of the intelligence quotient tests are [4] : 1) Stanford Binet (SB-V) calculates the capability of reasoning such as abstract, quantitative, verbals, and even working memory [5]. 2) Wechsler Adult Intelligence Scale (WAIS-IV) Mainly comprises two essential parts.…”

Section: Introductionmentioning

confidence: 99%

On the Design of Student Assessment Model Based on Intelligence Quotient Using Machine Learning

Kathirisetty¹,

Jadeja

Garg

et al. 2022

IEEE Access

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The goal of this research is to figure out how to calculate academic achievements and students' cognitive quotients for placement. This study will attempt to forecast students' intelligence quotients or academic grades to measure the IQ of a student in a holistic manner using all kinds of parameters, from students' academic records to input from their professors and even their family background, thus creating a dataset of 9000 instances with all these data. We implemented and trained multiple machine learning algorithms on the data and collected the outcomes to select the best algorithm. Students' quantitative reasoning ability was selected as a parameter that could be assessed by their performance on aptitude tests. Certifications of the student during their bachelor's degree have been considered, which would also give us an idea about the student's critical and logical thinking ability. All the parameters were rated on a scale of 1-10. The driving motivation behind this investigation was to discover what parameters force a student to be placed in a company then the final overall "student score" is calculated to determine a student's intelligence quotient. The final IQ score of the studentgenerated was graded on a scale of 0-3 and a suitable salary package range for the student was estimated giving the company a good idea of the student's capability.

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First Publication of Subtests in the Stanford-Binet 5, WAIS-IV, WISC-V, and WPPSI-IV

Cited by 4 publications

References 6 publications

Factor structure of intelligence and divergent thinking subtests: A registered report

Factor structure of intelligence and divergent thinking subtests: A registered report

Kognitív epidemiológia – Az intelligenciaszint prospektív összefüggése a szomatikus és pszichiátriai betegségrizikóval

On the Design of Student Assessment Model Based on Intelligence Quotient Using Machine Learning

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