Applied multivariate statistics for the social sciences / James Stevens.
By: Stevens, James.
Contributor(s): Stevens, James.Material type: BookNew Jersey : Lawrence Erlbaum, 1996Edition: 3rd ed.Description: xvii, 659 p. : ill. ; 23 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780805816716 (pbk.); 0805816712 (pbk. : acid-free paper).Other title: SPSS for Windows supplement for Applied multivariate statistics for the social sciences, third edition [Title of supplement:].Subject(s): Sciences sociales -- Methodes statistiques | Multivariate analyse | Multivariate analysis | Social sciences -- Statistical methods | Social sciences -- Statistical methods | Algebra | Social sciences | Statistics | Statistical analysis | Multivariate analysis | Multivariate analysis | Statistical analysis | Social sciences | Linear models | MANOVA | Variance (Statistical)DDC classification: 519.5/35/0243
|Item type||Current location||Collection||Call number||Status||Date due|
|Books||Prof Juhani Tuovinen's Collection||Non-fiction||519.5/35/0243 (Browse shelf)||Available|
Supplement bound within volume.
Includes bibliographical references (p. 561-572) and indexes.
Ch. 1. Introduction -- Ch. 2. Matrix Algebra -- Ch. 3. Multiple Regression -- Ch. 4. Two Group Multivariate Analysis of Variance -- Ch. 5. K Group Manova: A Priori and Post Hoc Procedures -- Ch. 6. Assumptions in Manova -- Ch. 7. Discriminant Analysis -- Ch. 8. Factorial Analysis of Variance -- Ch. 9. Analysis of Covariance -- Ch. 10. Stepdown Analysis -- Ch. 11. Confirmatory and Exploratory Factor Analysis -- Ch. 12. Canonical Correlation -- Ch. 13. Repeated Measures Analysis -- Ch. 14. Categorical Data Analysis: The Log Linear Model.
This best-selling text is written for those who use, rather than develop statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving results. Helpful narrative and numerous examples enhance understanding and a chapter on matrix algebra serves as a review. Annotated printouts from SPSS and SAS indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use these packages, the author stresses the importance of checking the data, assessing the assumptions, and ensuring adequate sample size by providing guidelines so that the results can be generalized. The book is noted for its extensive applied coverage of MANOVA, its emphasis on statistical power, and numerous exercises including answers to half. [Publisher summary]
Supplement title: SPSS for Windows supplement : for applied multivariate statistics for the social sciences, third edition.