The Effect of Sample Size on the Stability of Principal Components Analysis of Truss-Based Fish Morphometrics

2009 ◽  
Vol 138 (3) ◽  
pp. 487-496 ◽  
Author(s):  
Patrick M. Kocovsky ◽  
Jean V. Adams ◽  
Charles R. Bronte
Keyword(s):  
Sample Size ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis ◽  
The Stability

Multivariate Analysis of the California Test of Mental Maturity, Primary Forms

1976 ◽  
Vol 38 (2) ◽  
pp. 487-493 ◽  
Author(s):  
Peter F. Merenda ◽  
Harry S. Novack ◽  
Elisa Bonaventura
Keyword(s):  
Multivariate Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Canonical Variate ◽  
General Factor ◽  
Canonical Correlations ◽  
Mental Maturity ◽  
Components Analysis ◽  
The Stability ◽  
Test Retest Reliability

The test-retest reliability of the profile yielded by the four subscores of the California Test of Mental Maturity was determined. Subjects were 716 pupils in Grades K, 1, and 2 who were tested twice within 8 mo. Canonical correlational analysis gave four statistically significant interprofile correlations, each based on a successively weaker canonical variate. These canonical correlations ranged from a high of .688 to a low of .167, raising some doubts regarding the stability of the test profile. The test-retest correlations between individual subscores ranged between .43 and .56, further attesting to the questionable reliability of these measures, at least for the lower levels of the test. Principal components analysis suggests the existence of a single general factor which, however, accounts for only a little more than half the total variance yielded by the battery.

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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