| Description |
1 online resource (96 pages) : illustrations |
| Series |
Sage university papers series. Quantitative applications in the social sciences ; no. 07-069 |
|
Quantitative applications in the social sciences ; no. 07-069
|
| Contents |
1. Introduction -- Example -- 2. Basic concepts of principal components analysis -- 3. geometrical properties of principal components -- Example -- 4. Decomposition properties of principal components -- Decomposition of the variables -- Spectral decomposition of the correlation or covariance matrix -- 5. Principal components of patterned correlation matrices -- Example -- 6. Rotation of principal components -- Example -- 7. Using principal components to select a subset of variables -- Example -- 8. Principal components versus factor analysis -- Example -- Factor rotation -- 9. Uses of principal components in regression analysis -- Regression on principal components -- Principal components regression -- Example -- 10. Using principal components to detect outlying and influential observations -- 11. Use of principal components in cluster analysis -- 12. Use of principal components analysis in conjunction with other multivariate analysis procedures -- Use of principal components in discriminant analysis -- Example -- Use of principal components in canonical correlation analysis -- 13. Other techniques related to principal components -- Principal coordinate analysis -- Correspondence analysis |
| Summary |
Principal components analysis offers researchers a 'feel' for analysing particular sets of multidimensional data. It is particularly useful in coping with multicolinearity in regression analysis, a persistent problem in behavioral and social science data sets |
| Bibliography |
Includes bibliographical references (pages 94-95) |
| Notes |
Print version record |
| Subject |
Principal components analysis.
|
| Form |
Electronic book
|
| ISBN |
058516360X (electronic bk.) |
|
9780585163604 (electronic bk.) |
|
9781412985475 (ebook) |
|
1412985471 (ebook) |
|
