An S Factor Analysis on the Provinces of Vietnam: Relationships with Cognitive Ability, Ethnicity, and Latitude

2018 ◽  
Vol 58 (4) ◽  
pp. 562-579
Author(s):  
Emil O. W. Kirkegaard ◽  
Bryan J. Pesta
Keyword(s):  
Factor Analysis ◽  
Cognitive Ability ◽  
S Factor

SEX DIFFERENCES IN SELF-RATED ABILITY

2002 ◽  
Vol 30 (2) ◽  
pp. 185-193 ◽  
Author(s):  
Adrian Furnham
Keyword(s):  
Factor Analysis ◽  
Sex Differences ◽  
Cognitive Ability ◽  
Sex Difference ◽  
Right Wing ◽  
Political Beliefs ◽  
General Aptitude Test ◽  
Two Factors ◽  
Ability Factor ◽  
Iq Scores

Two hundred and one adults completed two questionnaires: the first estimating their scores and that of their partner on nine scales from the General Aptitude Test Battery (GATB); the second on beliefs about IQ tests. There was overall no sex difference between participants on self- and partner- ratings though there was an expected difference on numerical ability. Factor analysis revealed two factors labeled cognitive ability and dexterity. Regressing the eight specific abilities onto the overall score showed five abilities with significant beta weights (particularly verbal and numerical abilities) accounting for nearly three-quarters of the variance. Regressing seven demographic factors onto the overall score showed the participants' wealth, education and political beliefs to be the best predictors: richer, longer educated, more right-wing people thought they had higher IQ scores.

scholarly journals Psychometric Network Analysis of the Hungarian WAIS

2019 ◽  
Vol 7 (3) ◽  
pp. 21 ◽  
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Factor Analysis ◽  
Network Analysis ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Secondary Data ◽  
Network Models ◽  
Higher Order ◽  
Underlying Structure ◽  
Adult Intelligence Scale

The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed.

A Study of Language Disabilities in Cerebral Palsied Children

1965 ◽  
Vol 8 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Patricia Myers
Keyword(s):  
Factor Analysis ◽  
Discriminant Analysis ◽  
Cognitive Ability ◽  
Analysis Of Variance ◽  
Normal Group ◽  
The Other ◽  
Normal Children ◽  
Language Disabilities ◽  
Language Variables ◽  
Representational Level

Sixty-eight spastic, 24 athetoid, and 32 normal children were administered the Illinois Test of Psycholinguistic Abilities to determine whether this instrument would distinguish among the groups. Non-language variables, such as age, cognitive ability, and social experience were controlled, and the data were subjected to factor analysis, analysis of variance, and discriminant analysis. Significant differences were found, favoring the spastic group on ITPA tests at the automatic-sequential level of language and the athetoid group on ITPA tests at the representational level. The scores for the normal group were uniformly higher than the scores of the cerebral palsied groups and separated the normal group from the other two groups.

Factor Analysis of the Gordon Test of Visual Imagery Control

1974 ◽  
Vol 38 (3) ◽  
pp. 945-946 ◽  
Author(s):  
R. Ashton ◽  
K. White
Keyword(s):  
Factor Analysis ◽  
Cognitive Ability ◽  
Visual Imagery ◽  
Factorial Structure ◽  
Simple Measure

Gordon's Test of Visual Imagery Control was administered to 1562 Ss and, when the responses were factored, this test did not have a simple factorial structure. Thus, it cannot be assumed that S's score on the test represents a simple measure of a unimodal cognitive ability of imagery control.

scholarly journals Dimensionality and Determinants of Self-Reported Cognitive Failures

2018 ◽  
Vol 11 (1) ◽  
pp. 9-18 ◽  
Author(s):  
William C. Tirre
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Ability ◽  
Need For Cognition ◽  
Personality Factors ◽  
Big Five Personality ◽  
Ability Test ◽  
Reasoning Test ◽  
Cognitive Failures ◽  
Confirmatory Factor

This research examined the dimensionality and the correlates of self-reported cognitive failures.  The first goal was to determine what factors, in addition to a general one, are needed to explain self-reported cognitive failures.  To explore this issue, both Rasch measurement and confirmatory factor analysis were employed.  The second goal was to determine if cognitive failures might be predicted with personality factors, general cognitive ability, and the need for cognition. A sample of 552 USAF airmen responded to the Broadbent Cognitive Failures Questionnaire (CFQ), a Big-Five personality inventory, the Abstract Reasoning Test, the Speeded Cognitive Ability Test, and the Need for Cognition survey.  Both Rasch modeling and confirmatory factor analysis indicated that a single factor dominated CFQ responses.  Regression analysis showed that CFQ responses were predicted well by personality factors (R = .60).

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