scholarly journals Impulsivity in the eye of the beholder: an analysis of teachers' concepts of impulsive and reflective behaviour

1993 ◽  
Vol 7 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Mechteld R. M. Visser ◽  
Edith A. Das‐Smaal ◽  
Jan A. Feij
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Social Behaviour ◽  
Rating Scales ◽  
Teacher Rating ◽  
One Dimension ◽  
Analysis Study ◽  
Components Analysis ◽  
Item Pools

The aim of this research was to investigate teachers' perception of the concept of impulsivity and its relation to the concept of reflectivity. Teacher rating scales for both concepts were constructed by means of the prototype method (Study 1). It appeared that impulsivity refers to social behaviour, whereas reflectivity is more cognitive in character. A Principal Components Analysis (Study 2) showed that the item pools for impulsive and reflective behaviour account for separate components. It is concluded that impulsivity and reflectivity as perceived by teachers are not two extremes of one dimension, but refer to different behavioural domains: social and cognitive.

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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