PRINCIPAL COMPONENT ANALYSIS (PCA) DAN APLIKASINYA DENGAN SPSS
PCA (Principal Component Analysis ) are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largel...
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| Format: | Article |
| Language: | English |
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Andalas University
2009-03-01
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| Series: | JKMA: (Jurnal Kesehatan Masyarakat Andalas) (Andalas Journal of Public Health) |
| Online Access: | http://jurnal.fkm.unand.ac.id/index.php/jkma/article/view/68 |
| Summary: | PCA (Principal Component Analysis ) are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result. |
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| ISSN: | 1978-3833 2442-6725 |