Zyron Arago An ANOVA requires a continuous (interval or ratio) level of measurement for the dependent variable. The independent variables for an ANOVA must be categorical (nominal or ordinal) and contain more than two levels (groups). If this definition matches your data, you may use it. The same applies to your second query.

Whether or not you should use one-way repeated measures ANOVA instead of multivariate analysis depends on the specific research question you are trying to answer and the structure of your data.

One-way repeated measures ANOVA is appropriate when you have a single independent variable (e.g. time, treatment, etc.) and multiple dependent variables (e.g. 9 variables you mentioned), and you want to test for differences in the mean scores across the levels of the independent variable. This type of analysis can help you to determine if there are significant differences in the mean scores of your dependent variables over time (if time is your independent variable) or across groups (if you have different groups of participants).

Multivariate analysis, on the other hand, can be useful when you have multiple independent variables and multiple dependent variables and you want to investigate the relationships between them. Techniques such as principal component analysis (PCA) and partial least squares regression (PLS) can be used to analyze patterns in the data and identify the underlying structure of the variables.

So, if you are only interested in knowing the mean score across the levels of the independent variable and how it changes over time, One-way repeated measures ANOVA is appropriate. However, if you want to investigate the relationship between the independent and dependent variables, multivariate analysis would be more appropriate.

It's also important to note that ANOVA assumes that the data is normally distributed, If this assumption is not met, it might not be appropriate to use ANOVA and other non-parametric alternatives like Friedman's test should be considered.