How to Create a One-Way MANOVA Table

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

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 Inputs 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.