Principal components, analysis of variance and data structure

1972 ◽  
Vol 26 (3) ◽  
pp. 119-129 ◽  
Author(s):  
John Mandel
Psihologija ◽  
2015 ◽  
Vol 48 (1) ◽  
pp. 61-78 ◽  
Author(s):  
Slobodan Markovic ◽  
Djordje Alfirevic

The purpose of the present study was to compare the structure of experience of architectural expressiveness of architects and non-architects. Twenty architects and twenty non-architects rated twenty photographs of architectural objects on thirty expressiveness scales. Principal components analysis revealed four factors for both groups of participants: Aggressiveness, Regularity, Color and Aesthetics. In a cluster analysis two clusters of architectural objects were obtained: Choleric (high Aggressiveness and Color) and Phlegmatic (low Aggressiveness and Color, and high Regularity). All objects were highly rated on Aesthetics. Analysis of variance has shown that architects rated both clusters as less aggressive than non-architects. Also, experts rated the Phlegmatic cluster as more aesthetic, while nonexperts rated the Choleric cluster as more aesthetic. These results supported the Processing Fluency model: compared to non-architects, architects processed the expressive information of minimalistic objects (Phlegmatic cluster) with ease, which led towards positive hedonic reactions and higher.


1976 ◽  
Vol 1 (4) ◽  
pp. 285-312 ◽  
Author(s):  
Howard Wainer

It is noted that the usual estimators that are optimal under a Gaussian assumption are very vulnerable to the effects of outliers. A survey of robust alternatives to the mean, standard deviation, product moment correlation, t-test, and analysis of variance is offered. Robust methods of factor analysis, principal components analysis and multivariate analysis of variance are also surveyed, as are schemes for outlier detection.


2021 ◽  
Vol 85 (3) ◽  
pp. 197-209
Author(s):  
Serdar Yedier ◽  
Derya Bostanci

This study was conducted to discriminate five Scorpaena species and populations of each species according to morphometric characters. A total of 1865 fish specimens were collected from the eight locations in the four Turkish seas: Antalya, Balıkesir, Çanakkale, Hatay, İzmir, Marmara Ereğlisi, Ordu and Şile. In the study, 26 morphometric traits were measured for intra- and interspecific discrimination of five Scorpaena species. The data were subjected to analysis of variance, principal components analysis (PCA) and canonical discriminant analysis. As results of the PCA, 10 traits for S. maderensis and S. scrofa, 12 traits for S. elongata and 13 traits for S. notata and S. porcus were found to be important for intraspcific discrimination. The overall classification scores of intraspecific discrimination were determined as 94.6% for S. elongata, 90.5% for S. maderensis, 96.7% for S. notata, 96.5% for S. porcus and 92.2% for S. scrofa. The PCA indicated that 13 morphometric measurements among the 26 traits are important in the interspecific discrimination of five Scorpaena species. The cross-validated canonical discriminant analysis was correctly classified as 97.4% at the Scorpaena species level. The discrimination of correctly classified species ranged from 94.8% to 100%. Finally, we demonstrated that the morphometric characters examined in the present study can be used successfully in the intra- and interspecific discrimination of Scorpaena species from different habitats.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

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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