Using the Two-Way MANOVA test in Research
This easy tutorial will show you how to run the Two Way MANOVA test in SPSS, and how to interpret the result.
Two-way multivariate analysis of variance (MANOVA) deals with testing the effects of the two grouping variables, usually called factors, on the measured observations as well as interaction effects between the factors. In addition, It is the direct multivariate analog of two-way univariate ANOVA and is able to deal with possible correlations between the variables under consideration. (Source)
On the other hand, two-way MANOVA is a parametric test. So, we use two-way MANOVA when we want to determine whether there is an interaction between the two independent categorical variables on the two or more continuous dependent variables. Therefore, we have two independent categorical variable and two or more continuous dependent variables.
Assumptions of the Two-Way MANOVA Test
When performing a Two-Way MANOVA procedure the following assumptions are required
- two independent categorical variables 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: Two-Way MANOVA Test
This guide will explain, step by step, how to run the Two-way MANOVA test in SPSS statistical software by using an example.
We want to examine whether there is an interaction effect of gender and training on English test scores, Math test scores, and History test scores. Therefore, we have two independent categorical variables: Gender (male and female) and training (1 or 2 or 3 months) and three continuous dependent variables (English test score, Math test score, and History test score).
There is no effect of interaction between the two independent categorical variables on the two or more continuous dependent variables.
There is an effect of interaction between the two independent categorical variables on the two or more continuous dependent variables.
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