Applied multivariate statistics for the social sciences / by James P. Stevens

By:

Stevens, James P

Material type: Text

TextLanguage: English Publication details: Routledge, 2009. New York:Edition: 5thDescription: xii, 651ISBN:

9780805859034

Subject(s):

DDC classification:

519.535 STE-A

Contents:

1. Introduction -- 2. Matrix Algebra -- 3. Multiple Regression -- 4. Two-Group Multivariate Analysis of Variance -- 5. K-Group MANOVA: A Priori and Post Hoc Procedures -- 6. Assumptions in MANOVA -- 7. Discriminant Analysis -- 8. Factorial Analysis of Variance -- 9. Analysis of Covariance -- 10. Stepdown Analysis -- 11. Exploratory and Confirmatory Factor Analysis -- 12. Canonical Correlation -- 13. Repeated Measures Analysis -- 14. Categorical Data Analysis: The Log Linear Model -- 15. Hierarchical Linear Modeling -- 16. Structural Equation Modeling.

Summary: Suitable for those who use, rather than develop statistical methods, this text focuses on a conceptual understanding of the material rather than on proving results. It 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.

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Text/Reserve Book	Library, SPAB G-1	Non Fiction	519.535 STE-A (Browse shelf(Opens below))	Available		009100

Total holds: 0

1. Introduction --
2. Matrix Algebra --
3. Multiple Regression --
4. Two-Group Multivariate Analysis of Variance --
5. K-Group MANOVA: A Priori and Post Hoc Procedures --
6. Assumptions in MANOVA --
7. Discriminant Analysis --
8. Factorial Analysis of Variance --
9. Analysis of Covariance --
10. Stepdown Analysis --
11. Exploratory and Confirmatory Factor Analysis --
12. Canonical Correlation --
13. Repeated Measures Analysis --
14. Categorical Data Analysis: The Log Linear Model --
15. Hierarchical Linear Modeling --
16. Structural Equation Modeling.

Suitable for those who use, rather than develop statistical methods, this text focuses on a conceptual understanding of the material rather than on proving results. It 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.

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