Analysis of Variance (ANOVA) is a hypothesis testing procedure that tests whether two means are significantly different from each other. One-Way ANOVA tests the relationship between a numeric variable and a categorical variable.
This article describes how to go create a One-Way ANOVA Table as shown below. The table below shows the pairwise comparison of Total Spend grouped by Household description.
- A numeric variable to be used as a dependent variable.
- A categorical variable to be used as a predictor.
- In the Anything menu select Advanced Analysis > Analysis of Variance > One-Way ANOVA.
- In the object inspector go to the Inputs tab.
- In the Output menu select the numeric variable to be predicted by the predictor variables.
- Select the categorical predictor variable from the Predictor list.
- In the Compare menu select the contrasts to be performed.
- To mean The post hoc testing compares the mean of each category to the overall average (ie, the grand mean).
- To first The post hoc testing compares the mean of each category to the mean of the first category.
- Pairwise The post hoc testing compares the mean of each pair of categories.
- OPTIONAL: Select multiple comparison Correction used when calculating p-values. Note: The Correction calculations take into account the settings in Compare. Tukey Range correction is used by default.
- OPTIONAL: To compute standard errors that are robust to violations of the assumption of constant variance (ie, heteroscedasticity) select Robust standard errors.
- OPTIONAL: Set the Alternative hypothesis to be used in computing the p-values in the post hoc tests. You can choose between Two sided (default), Greater or Less.
- OPTIONAL: If the output returns an error due to missing data, go to the Missing Data menu and select Exclude Cases with Missing Data.
- OPTIONAL: Select Variable names to display Variable names in the output instead of labels.