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/9780199773596.003.0003
- Subject:
- Social Work, Research and Evaluation
Analysis of covariance (ANCOVA) assesses group differences on a dependent variable (DV) after the effects of one or more covariates are statistically removed. By utilizing the relationship between ...
More
Analysis of covariance (ANCOVA) assesses group differences on a dependent variable (DV) after the effects of one or more covariates are statistically removed. By utilizing the relationship between the covariate(s) and the DV, ANCOVA can increase the power of an analysis. MANCOVA is an extension of ANCOVA to relationships where a linear combination of DVs is adjusted for differences on one or more covariates. The adjusted linear combination of DVs is the combination that would be obtained if all participants had the same scores on the covariates. That is, MANCOVA is similar to MANOVA, but allows a researcher to control for the effects of supplementary continuous IVs, termed covariates. In an experimental design, covariates are usually the variables not controlled by the experimenter, but still affect the DVs. Consequently, although not as effective as random assignment, including covariates may reduce both systematic and within-group error by equalizing groups being compared on important characteristics. This chapter discusses the assumptions of MANCOVA, sample size requirements, and strengths and limitations of MANCOVA. An annotated example is also provided.Less
Analysis of covariance (ANCOVA) assesses group differences on a dependent variable (DV) after the effects of one or more covariates are statistically removed. By utilizing the relationship between the covariate(s) and the DV, ANCOVA can increase the power of an analysis. MANCOVA is an extension of ANCOVA to relationships where a linear combination of DVs is adjusted for differences on one or more covariates. The adjusted linear combination of DVs is the combination that would be obtained if all participants had the same scores on the covariates. That is, MANCOVA is similar to MANOVA, but allows a researcher to control for the effects of supplementary continuous IVs, termed covariates. In an experimental design, covariates are usually the variables not controlled by the experimenter, but still affect the DVs. Consequently, although not as effective as random assignment, including covariates may reduce both systematic and within-group error by equalizing groups being compared on important characteristics. This chapter discusses the assumptions of MANCOVA, sample size requirements, and strengths and limitations of MANCOVA. An annotated example is also provided.
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/9780199773596.003.0006
- Subject:
- Social Work, Research and Evaluation
This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and ...
More
This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). It offers suggestions to guide their differential use. It also compares and contrasts MANOVA and MANCOVA versus MMR, MANOVA and MANCOVA versus SEM, and MMR versus SEM.Less
This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). It offers suggestions to guide their differential use. It also compares and contrasts MANOVA and MANCOVA versus MMR, MANOVA and MANCOVA versus SEM, and MMR versus SEM.
