Implicative Statistical Analysis and Principal Components Analysis in Recording Students’ Attitudes to Electronics and Electrical Construction Subjects

2011 ◽  
Vol 7 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Sofia D. Anastasiadou ◽  
Lazaros Anastasiadis ◽  
Ioannis Vandikas ◽  
Theoxaris Angeletos
Keyword(s):  
Statistical Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis ◽  
Students Attitudes ◽  
Electronics And Electrical

Comparison of multivariate pattering methods in group/cluster identification regarding the science of educational research: Implicative Statistical Analysis vs. L’ Analysee Factorielle des Correspondances

2019 ◽  
Vol 6 (1) ◽  
pp. 238-245
Author(s):  
Sofia D Anastasiadou
Keyword(s):  
Statistical Analysis ◽  
Principal Components Analysis ◽  
Educational Research ◽  
Principal Components ◽  
Quantitative Research ◽  
Self Control ◽  
Clustering Methods ◽  
Technological Education ◽  
Cluster Identification ◽  
Components Analysis

Nowadays, there is a substantial improvement and utilisation of pattering methods in the science of educational research, a comparison of multivariate methods in group/cluster identification in the scientific domain of quantitative research has not been carried out. This study analyses two different statistical techniques: i.e., the principal components analysis (PCA) and the implicative statistical analysis (ASI). A survey was carried out using a structured questionnaire for a sample of 135 nurses which studied in the School of Pedagogical and Technological Education in order to be qualified in respect The study focuses on the presentation of the two main types of clustering methods, της ASI and L’ Analysee Factorielle des Correspondances (AFC). AFC’s results made it evident that Emotionality, Self-control, Sociability, General items of EI constructs are shaped attitudes and reveal the latent dimension of respondents psychological attributes related to EI conceptual constructs. Keywords: L’ Analysee Factorielle des Correspondances, principal components analysis, implicative statistical analysis, research.

scholarly journals Statistical analysis: a practical guide for psychiatrists

2016 ◽  
Vol 22 (4) ◽  
pp. 251-259 ◽  
Author(s):  
Vasilis Nikolaou
Keyword(s):  
Statistical Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Statistical Tests ◽  
Measures Of Association ◽  
Practical Guide ◽  
Components Analysis ◽  
Using Data

SummaryThis article is a practical guide for psychiatrists who want to apply basic and straightforward statistics in their research. It describes ways of summarising data and provides an overview of statistical tests for comparing patients' characteristics. Measures of association such as correlation and regression are also explained, along with principal components analysis, a method for reducing the dimensionality of data. Explanations are clarified using data from the published studies.

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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