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In this talk, we consider various Bayes factor approaches for the hypothesis testing problem in analysis-of-variance (ANOVA) designs. We firstly reparameterize the ANOVA model with constraints for uniqueness into a classical linear regression model without constraints. We then adopt Zellners g-prior for the regression coefficients and place a hyper-g prior for g, providing a mixture of g-priors. We propose an explicit closed-form expression for Bayes factor without integral representation. Specifically, we investigate the consistency of Bayes factors based on mixture g-priors when the model dimension grows with the sample size. The proposed results generalize some existing ones for the one-way/two-way ANOVA models and can directly be applied to higher-order factorial models. Applications to two real-data sets are presented to compare the performances between the proposed and previous Bayesian procedures in the literature.