A case of extreme simplicity of the core matrix in three-mode principal components analysis

Psychometrika ◽  
1998 ◽  
Vol 63 (3) ◽  
pp. 255-261 ◽  
Author(s):  
Takashi Murakami ◽  
Jos M. F. Ten Berge ◽  
Henk A. L. Kiers
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
The Core ◽  
Core Matrix ◽  
Components Analysis

Associations among the Subtests: A Short-Form CART

2016 ◽  
Author(s):  
Keith E. Stanovich ◽  
Richard F. West ◽  
Maggie E. Toplak
Keyword(s):  
Cognitive Ability ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Scientific Reasoning ◽  
Large Scale ◽  
Short Form ◽  
Statistical Reasoning ◽  
Rational Thinking ◽  
The Core ◽  
Components Analysis

Chapter 12 describes a large-scale study of the short-form version of the CART. The short-form is composed of 11 of the 20 subtests and can be completed in less than two hours by most subjects. The short-form CART includes both the Probabilistic and Statistical Reasoning and the Scientific Reasoning subtests, as both are at the core of most definitions of rational thinking. All four subtests that directly tap the avoidance of miserly processing are included in the short form. The Probabilistic Numeracy subtest is included in the short-form CART because it is statistically quite potent for the amount of time that it takes. All four subtests assessing contaminated mindware are included in the short-form. Chapter 12 reports the results of a study of short-form performance involving 372 subjects. Reliabilities of all the subtests are reported, as well as correlations with cognitive ability and the Actively Open-Minded Thinking scale. Correlations among all the subtests are reported as well as a principal components analysis of the subtests.

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.

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