Thank you so much for your help! I’ve done mixed ANOVA in SPSS, in which you can add the time point DVs into a ‘time’ IV - can’t find that when conducting MANOVA, so I’ll continue to investigate. I hope that gives you enough information to get you started! That will help you answer your questions about comparing states. For more details on that, read my post about using post hoc tests with ANOVA. You’ll need to consider using a post hoc test to control the family-wise error rate. Something to consider.Īnother point is that you’ll be comparing many groups. However, if you average several Likert items together (or add them), then you have stronger grounds for considering it to be a regular continuous variables.

I’m don’t whether the same details apply to ANOVA, which you’d need to use because you have more groups. Note that the discussion is in context of using t-tests. For more information, read my post about analyzing Likert data. However, there is some evidence that the regular parameteric tests are ok to use with them. Many analyst consider averages to be inappropriate for this type of data. One difficult aspect is the fact that you’re using Likert scale data, which are ordinal data. I don’t have specific answer for you but can at least raise several issues and provide some information. This is a tricky problem for several reasons. Instead, if you perform one MANOVA test, the error rate equals the significance level.

Limits the joint error rate: When you perform a series of ANOVA tests because you have multiple dependent variables, the joint probability of rejecting a true null hypothesis increases with each additional test.

As the example in this post shows, ANOVA tests with a single dependent variable can fail completely to detect these patterns.

Assess patterns between multiple dependent variables: The factors in the model can affect the relationship between dependent variables instead of influencing a single dependent variable.

Greater statistical power: When the dependent variables are correlated, MANOVA can identify effects that are smaller than those that regular ANOVA can find.

The correlation structure between the dependent variables provides additional information to the model which gives MANOVA the following enhanced capabilities:

Use multivariate ANOVA when your dependent variables are correlated. We can conclude that there is an association between teaching method and the relationship between the dependent variables. Even though the one-way ANOVA results and graphs seem to indicate that there is nothing of interest, MANOVA produces statistically significant results-as signified by the minuscule P-values.

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