Factor selection and structural identification in the interaction ANOVA model.

ABSTRACT: When faced with categorical predictors and a continuous response, the objective of an analysis often consists of two tasks: finding which factors are important and determining which levels of the factors differ significantly from one another. Often times, these tasks are done separately using Analysis of Variance (ANOVA) followed by a post hoc hypothesis testing procedure such as Tukey's Honestly Significant Difference test. When interactions between factors are included in the model the collapsing of levels of a factor becomes a more difficult problem. When testing for differences between two levels of a factor, claiming no difference would refer not only to equality of main effects, but also to equality of each interaction involving those levels. This structure between the main effects a

SUBMITTER: Post JB 

PROVIDER: S-EPMC3772552 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature