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In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. The examples highlight some of the properties and limitations of principal component analysis. This is part of a continuing project that produces annotated computer output for principal component analysis. The complete project will involve processing four examples on SAS/PRINCOMP, BMDP/4M, SPSS-X/FACTOR, GENSTAT/PCP, and SYSTAT/FACTOR. We show here the results from SPSS-X/FACTOR, Release 2.2.
In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two correlated variables to form the X-variates for the principal component analysis. The examples highlight some of the properties and limitations of principal component analysis. This is part of a continuing project that produces annotated computer output for principal component analysis. The complete project will involve processing four examples on SAS/PRINCOMP, BMDP/4M, SPSS-X/FACTOR, GENSTAT / PCP, and SYSTAT / FACTOR. We show here the results from SYSTAT/FACTOR, Version 3. (Author).
In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. The examples highlight some of the properties and limitations of the principal component analysis. This is part of a continuing project that produces annotated computer output for principal component analysis. The complete project will involve processing four examples on SAS/PRINCOMP, BMDP/4M, SPSS-X/FACTOR, GENSTAT/PCP, and SYSTAT/FACTOR. We show here the results from BMDP/4M, Version 85.
In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. This is part of a continuing project that produces annotated computer output for principal components analysis. The complete project will involve processing four examples on SAS/PRINCOMP, BMDP/4M, SPSS-X/FACTOR, GENSTAT/PCP, and SYSTAT/FACTOR. We show here the results from GENSTAT/PCP, Version 4.04.
Seminar paper from the year 2011 in the subject Economics - Statistics and Methods, grade: 1,0, University of applied sciences, Munich, course: Research Methods, language: English, abstract: In this paper the historical and theoretical background of the factor analysis is briefly explained. Principal Component Analysis (PCA) and Principal Axis Factoring (PAF) are applied to a data set which has been generated in the scope of the evaluation of the implementation of Company X’s corporate Strategy XX. The results clearly indicate that structural parts of the data collection instrument could be reproduced by the empirical data. The primary factors resulting from an orthogonal respectively oblique rotation are comparable but also show slight differences. Latent constructs like “Trust”, “Job Satisfaction” “Disengagement” and “Pessimism” are indicated by the results. Secondary factors indicate a negative relationship between disengagement and leadership respectively transparency concerning the corporate strategy and job satisfaction. Also aspects of “state negativity” can be identified. This means that a general pessimistic attitude is related to a more pessimistic view on realized customer focus. The application of more elaborated methods would be needed to identify causal relationships.
SPSS is enormously powerful – and challenging to learn. This popular handbook lets students get hands-on with the statistical procedures they need. Full colour screen shots, step-by-step guidance and examples with annotated outputs help students learn. For students of psychology, marketing and research in any discipline. An essential practical guide to using the latest version of IBM SPSS Statistics. New, print versions of this book come with bonus online study tools on the CourseMate Express platform Learn more about the online tools cengage.com.au/learning-solutions