The theoretical limit to the number of factors included in an ANOVA is primarily governed by the sample size and the degrees of freedom available. Each additional factor requires additional degrees of freedom for the model, which can lead to overfitting if the sample size is not sufficiently large. Practically, as the number of factors increases, the complexity of the model grows, making it harder to interpret and potentially leading to issues like multicollinearity. Therefore, while there is no strict upper limit, the number of factors should be balanced with the sample size and the research objectives.
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