Using the MANOVA test in Research
This easy tutorial will show you how to run the One Way MANOVA test in SPSS, and how to interpret the result.
Multivariate analysis of variance (MANOVA) is a statistical test for comparing multivariate means of several groups (Source). On the other hand, if you need more deep information about the formula of the test. Finally, if you need more information, please read this article (Source)
For instance, one-way MANOVA is a parametric test (data are normally distributed) and we use this test when we want to determine whether there are differences between groups (two or more) of the categorical variable on more than one continuous dependent variable. Therefore, we have one independent categorical variable and two or more continuous dependent variables.
Assumptions of the MANOVA Test:
When performing a One-Way MANOVA procedure the following assumptions are required:
- independent categorical variable with two or more groups
- two or more dependent continuous variables
- data are normally distributed
- independence of observations
- sample size (more cases in each group than the number of dependent variables)
- no univariate or multivariate outliers
- homogeneity of variance-covariance matrices
- no multicollinearity
- a linear relationship between each pair of continuous dependent variables for each group of the independent categorical variable
- multivariate normality
An Example: One-Way MANOVA Test
This guide will explain, step by step, how to run a one-way MANOVA Test in SPSS statistical software by using an example.
We wanted to examine whether there is a difference between males and females on English test scores, Math test scores, and History test scores. Therefore, we have one independent categorical variable gender (male and female) and three continuous dependent variables (English test score, Math test score, and History test score).
There is no difference between groups of the categorical independent variable on two or more continuous variables.
There is a difference between groups of the categorical independent variable on two or more continuous variables.