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Psy524 Lecture 10 MANOVA1

# Ifyouhavesmallerunbalanceddesignsthanthe

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Unformatted text preview: VA designs or you can estimate it by picking the DV with the smallest effect expected and calculate power on that variable in a univariate method Missing data, unequal samples, Missing data, unequal samples, number of subjects and power Power in MANOVA also depends on the relationships among the DVs. – Power is highest when the pooled within cell correlation is high and negative. If the pooled within correlation is positive, zero or moderately negative the power is much less Multivariate normality Multivariate normality assumes that the means of the various DVs in each cell and all linear combinations of them are normally distributed. Difficult to show explicitly Multivariate normality Multivariate normality In univariate tests robustness against violation of the assumption is assured when the degrees of freedom for error is 20 or more and equal samples If there is at least 20 cases in the smallest cell the test is robust to violations of multivariate normality even when there is unequal n. If you have smaller unbalanced designs than the assumption is assessed on the basis of researcher judgment. Absence of outliers Absence of outliers univariate and multivariate outliers need to be assessed in every cell of the design Linearity Linearity MANOVA and MANCOVA assume linear relationships between all DVs, all CVs and all DV/CV pairs Linearity Linearity Deviations from linearity reduce the power of the test because: – the linear combination of DVs does not maximize the difference between the groups – the CVs do not maximally adjust the error. Homogeneity of regression Homogeneity of regression no IV by CV interaction Reliability of CVs and DVs Reliability of CVs and DVs reliability of CVs discussed previously. In the stepdown procedure in order for proper interpretation of the DVs as CVs the DVs should also...
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