What type of data does mixed ANOVA handle effectively?

Mixed ANOVA is designed to analyze data that includes both categorical independent variables and continuous dependent variables. This makes it particularly useful in scenarios where researchers want to compare group means across different conditions while accounting for variations within subjects.

The categorical variables can represent different groups or conditions, such as treatment types or demographic categories, while the continuous variable reflects the outcome measure, which could be scores on a psychological assessment or other numerical data. This combination allows researchers to explore interactions between the independent variables and how these influence the dependent variable, making mixed ANOVA a versatile tool in statistical analysis.

In contrast, focusing solely on categorical data would limit the analysis, and relying only on continuous data would not utilize the strengths of mixed ANOVA in exploring group differences across categories. Additionally, ordinal data does not fit well within the mixed ANOVA framework since it does not fully meet the assumptions required for parametric testing needed when using means. Thus, the ability of mixed ANOVA to handle a mix of both categorical and continuous data is what makes it the correct answer.