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Analysis of Multiple Dependent Variables$
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Patrick Dattalo

Print publication date: 2013

Print ISBN-13: 9780199773596

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199773596.001.0001

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Multivariate Multiple Regression

Multivariate Multiple Regression

Chapter:
(p.87) 4 Multivariate Multiple Regression
Source:
Analysis of Multiple Dependent Variables
Author(s):

Patrick Dattalo

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199773596.003.0004

Multivariate multiple regression (MMR) is used to model the linear relationship between more than one independent variable (IV) and more than one dependent variable (DV). MMR is multiple because there is more than one IV. MMR is multivariate because there is more than one DV. This chapter begins with an introduction to building and refining linear regression models. The remaining discussion focuses on the MMR procedure, and is organized as follows: estimating multivariate regression parameters; testing the omnibus hypothesis; assessing overall model fit; testing composite hypotheses; model validation; sample size requirements; strengths and limitations of MMR; annotated example; reporting the results of a MMR analysis; results of the annotated example; and additional examples from the applied research literature. MMR is demonstrated with Stata. References to resources for users of SPSS and SAS also are provided.

Keywords:   MMR, independent variables, dependent variables, statistical analysis, Stata

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