Patrick Dattalo
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199773596
- eISBN:
- 9780199332564
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199773596.001.0001
- Subject:
- Social Work, Research and Evaluation
Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This ...
More
Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This book provides an introduction to four procedures for the 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 is presented in a way that allows readers to compare and contrast them in terms of appropriate research context; required statistical assumptions, including levels of measurement of variables to be modeled; analytical steps; sample size; and strengths and weaknesses. This book facilitates course extensibility in scope and depth by allowing instructors to supplement course content with rigorous statistical procedures. The book provides detailed annotated examples using Stata, SPSS (PASW), SAS, and Amos.Less
Multivariate procedures allow social workers and other human services researchers to analyze complex, multidimensional social problems and interventions in ways that minimize oversimplification. This book provides an introduction to four procedures for the 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 is presented in a way that allows readers to compare and contrast them in terms of appropriate research context; required statistical assumptions, including levels of measurement of variables to be modeled; analytical steps; sample size; and strengths and weaknesses. This book facilitates course extensibility in scope and depth by allowing instructors to supplement course content with rigorous statistical procedures. The book provides detailed annotated examples using Stata, SPSS (PASW), SAS, and Amos.
Patrick Dattalo
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199773596
- eISBN:
- 9780199332564
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199773596.003.0001
- Subject:
- Social Work, Research and Evaluation
This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ...
More
This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ways that minimize oversimplification. Examples of multivariate statistical procedures to predict and describe relationships include multivariate multiple regression (MMR), multivariate analysis of variance (MANOVA), and multivariate analysis of covariance (MANCOVA). Structural equation modeling (SEM) may be used for data simplification and reduction, description, and prediction. The discussion then turns to the rationale for multivariate analysis followed by a description of the organization and contents of this book.Less
This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ways that minimize oversimplification. Examples of multivariate statistical procedures to predict and describe relationships include multivariate multiple regression (MMR), multivariate analysis of variance (MANOVA), and multivariate analysis of covariance (MANCOVA). Structural equation modeling (SEM) may be used for data simplification and reduction, description, and prediction. The discussion then turns to the rationale for multivariate analysis followed by a description of the organization and contents of this book.
