Multivariate GLM is the extended form of GLM, and it deals with more than one dependent variable and one or more independent variables. Multivariate GLM involves MANOVA and MANCOVA, which are the extended forms of ANOVA and ANCOVA, for multiple parameters.
MANOVA in multivariate GLM is used to observe the main and the interaction effects of categorical variables on which multiple interval natured dependent variables depend. MANOVA in multivariate GLM is used to test the null hypothesis. In other words, it is used to test that the vectors of means on multiple dependent variables are equal across the groups. In SPSS, MANOVA in multivariate GLM is done by selecting “General Linear Model” from the analyze menu, and then selecting the “Multivariate” option from the General Linear Model. MANOVA in multivariate GLM is appropriate when there are two or more dependent variables that are correlated.
MANCOVA in multivariate GLM is an extension of ANCOVA. MANCOVA in multivariate GLM involves examining the influence of uncontrolled independent variables, while examining the differences in the mean values of more than one dependent variable related to the effect of controlled independent variables. MANCOVA in multivariate GLM is similar to MANOVA in multivariate GLM, except that the interval independents are added with covariates. These covariates in MANCOVA in multivariate GLM serve as control variables for the independent factors. In SPSS, MANCOVA in multivariate GLM is done by selecting “General Linear Model” from the analyze menu, and then selecting the “Multivariate” option from the General Linear Model.
MANCOVA in multivariate GLM is a kind of ‘what if analysis’ in which the researcher analyzes what the results would be if all the cases scored equally on the covariates, such that the factors over and beyond the covariates are diminished.
There is a term called Step down MANOVA in multivariate GLM which can also be called Roy-Bargman Stepdown F test. Step down MANOVA in multivariate GLM is used to perform a significance test of the main effects in order to prevent the inflation of Type I errors.
Basically, MANOVA in multivariate GLM is a two-step procedure which involves the significance test and the post hoc test.
There are certain significance tests in MANOVA in multivariate GLM. These are the Hotelling’s T square test, the Wilk’s lambda U test, and the Pillai’s trace test.
There are certain assumptions of Multivariate GLM. These assumptions are as follows:
- The independent variables in multivariate GLM are categorical in nature.
- The dependent variables in multivariate GLM are continuous and interval in nature.
- The covariate variables in multivariate GLM are assumed to be measured without error.
- The residuals in multivariate GLM are randomly distributed.
- There should be no outliers in multivariate GLM as MANCOVA is highly sensitive to outliers in the covariates.
MANOVA and MANCOVA Resources
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