Analysis of Variance (ANOVA) is a hypothesis testing procedure that tests whether two means are significantly different from each other. One-Way MANOVA tests the relationship between a set of numeric variables and a single categorical variable.

This article describes how to go create a one-way MANOVA table as shown below in which age is used as the predictor variable and attitudes towards different brands as outcome variables.

## Requirements

- Familiarity with the
*Structure*and*Value Attributes*of Variable Sets. - At least one
**Categorical**variable to use as an**Outcome**variable. - A
**Numeric**,**Date**,**Money**,**Categorical,**or**Ordered Categorical**variable to use as a**Predictor**.

## Method

- From the
**Anything ()**menu select**Advanced Analysis > Analysis of Variance > One-Way MANOVA**. - In the
**object inspector**go to the**Data**tab. - In the
**Outcomes**menu select your outcomes variable(s). - Select the predictor variable from the
**Predictor**list. - OPTIONAL: To compute standard errors that are robust to violations of the assumption of constant variance (i.e., heteroscedasticity) select
**Robust standard errors**. See Robust Standard Errors for more information. - OPTIONAL: If the output returns an error due to missing data, go to the
**Missing Data**menu and select**Exclude Cases with Missing Data**or select**Use Partial Data**. See Missing Data Options for more information. - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select
**Categorical as Binary**to convert categorical outcome variables to binary variables. Otherwise, they are represented as sequential integers (i.e., 1 for the first category, 2 for the second, etc.).*Numeric - Multi*variables are treated according to their numeric values and not converted to binary.

### Technical details

- Tests of individual means are two-sided and comparing to the Grand Mean (i.e., "To mean"). See How to Create a One-Way ANOVA Table for more information as well as more options for post hoc testing.
- By modifying the R code so that
`Pillai = TRUE`,*Pillai's Trace*and*F*-tests can be computed for the overall and row null hypotheses, and Tukey's Range test is used to test within rows; Pilla's trace is not valid where the data is weighted.

## Next

How to Create a One-Way ANOVA Table

How to Create a Table of Means

The Magic Trick that Highlights Interesting Results on Any Table