Analysis of Multiple Dependent Variables by Patrick Dattalo

Analysis of Multiple Dependent Variables

byPatrick Dattalo

Paperback | February 21, 2013

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Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This pocket guide provides a concise, practical, and economical introduction to four procedures forthe analysis of multiple dependent variables: multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). Each procedure will be presented in a way that allows readers to compare and contrast them in terms of (1) appropriate research context; (2) required statistical assumptions, including levels of measurement of variables to be modeled; (3) analytical steps; (4) sample size; and (5) strengths andweaknesses. This invaluable guide facilitates course extensibility in scope and depth by allowing instructors to supplement course content with rigorous statistical procedures. Detailed annotated examples using Stata, SPSS (PASW), SAS, and Amos, together with additional resources, discussion of keyterms, and a companion website, make this an unintimidating guide for producers and consumers of social work research knowledge.

About The Author

Patrick Dattalo, PhD, is Professor of Social Work at Virginia Commonwealth University.
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Details & Specs

Title:Analysis of Multiple Dependent VariablesFormat:PaperbackDimensions:192 pages, 8.5 × 5.5 × 0.68 inPublished:February 21, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199773599

ISBN - 13:9780199773596

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Table of Contents

1. Basic Concepts and Assumptions2. Multivariate Analysis of Variance3. Multivariate Analysis of Covariance4. Multivariate Multiple Regression5. Structural Equation Modeling6. Choosing among Procedures for the Analysis of Multiple Dependent VariablesReferencesIndex