A Comparative Study of the Dimensionality of the Self-Concealment Scale Using Principal Components Analysis and Mokken Scale Analysis

2008 ◽  
Vol 90 (4) ◽  
pp. 323-334 ◽  
Author(s):  
Andreas A. J. Wismeijer ◽  
Klaas Sijtsma ◽  
Marcel A. L. M. van Assen ◽  
Ad J. J. M. Vingerhoets
Keyword(s):  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
The Self ◽  
Scale Analysis ◽  
Mokken Scale Analysis ◽  
Components Analysis

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures DOI: 10.5585/exacta.v8i2.2000

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

Allport-Vernon-Lindzey Study of Values: 1. Factor Structure for a Combined Sample of Male and Female College Students

1970 ◽  
Vol 27 (3) ◽  
pp. 955-958 ◽  
Author(s):  
Rio Sciortino
Keyword(s):  
College Students ◽  
Principal Components Analysis ◽  
Factor Structure ◽  
Principal Components ◽  
The Self ◽  
Female College Students ◽  
Male And Female ◽  
Female College ◽  
Components Analysis

A principal components analysis was performed on the self-ratings (for a combined sample) obtained from the Allport-Vernon-Lindzey Study of Values ( N = 150 combined sample of 102 male and 48 female college students). The obtained principal components were then rotated according to the varimax procedure. The varimax factors obtained were: esthetic, social, and religious.

scholarly journals Low-loss image compression techniques for cutting tool images: a comparative study of compression quality measures

Exacta ◽  
2010 ◽  
Vol 8 (2) ◽  
pp. 225-235
Author(s):  
Fábio Henrique Pereira ◽  
Elesandro Antonio Baptista ◽  
Nivaldo Lemos Coppini ◽  
Rafael Do Espírito-Santo ◽  
Ademir João de Oliveira
Keyword(s):  
Image Compression ◽  
Comparative Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
High Performance ◽  
Cutting Tool ◽  
Low Loss ◽  
Compression Process ◽  
Lifting Technique ◽  
Components Analysis

This work accomplishes a comparative study between two distinct image compression techniques, namely the Lifting technique and the Principal Components Analysis (PCA), in order to determine what of these two approaches is more appropriate for cutting tool wear images analysis. Lifting and Principal Components Analysis were applied in original images of a cutting tool for producing a low resolution version, while keeping the more important details of the image. The low-loss image compression quality provided by these techniques was expressed in terms of the compression factor (ρ), the Mean Square Error (MSE) and the Peak Signal-to-Noise Rate (PSNR) provided by the image compression process. The tests were accomplished using the high-performance language for technical computing MATLAB®, and the results shown that the PCA technique presented the best values of PSNR with low compression rates. However, with high values of compression rates the lifting technique gave the highest PSNR.

scholarly journals Questionnaires for 360-degree assessment of consultant psychiatrists: Development and psychometric properties

2008 ◽  
Vol 193 (2) ◽  
pp. 156-160 ◽  
Author(s):  
Paul Lelliott ◽  
Richard Williams ◽  
Alex Mears ◽  
Manoharan Andiappan ◽  
Helen Owen ◽  
...  
Keyword(s):  
Psychometric Properties ◽  
Principal Components Analysis ◽  
Domain Structure ◽  
Principal Components ◽  
Multisource Feedback ◽  
The Self ◽  
Clinical Judgement ◽  
Technical Proficiency ◽  
Components Analysis ◽  
Large Case

BackgroundExpert clinical judgement combines technical proficiency with humanistic qualities.AimsTo test the psychometric properties of questionnaires to assess the humanistic qualities of working with colleagues and relating to patients using multisource feedback.MethodAnalysis of self-ratings by 347 consultant psychiatrists and ratings by 4422 colleagues and 6657 patients.ResultsMean effectiveness as rated by self, colleagues and patients, was 4.6, 5.0 and 5.2 respectively (where 1=very low and 6=excellent). The instruments are internally consistent (Cronbach's alpha > 0.95). Principal components analysis of the colleague questionnaire yielded seven factors that explain 70.2% of the variance and accord with the domain structure. Colleague and patient ratings correlate with one another (r=0.39, P<0.001) but not with the self-rating. Ratings from 13 colleagues and 25 patients are required to achieve a generalisability coefficient (Eρ2) of 0.75.ConclusionsReliable 360-degree assessment of humane judgement is feasible for psychiatrists who work in large multiprofessional teams and who have large case-loads.

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