Thisishandledby usingeither

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Unformatted text preview: IVs, so choice of IVs to include needs to be thought about carefully Theoretical Considerations Theoretical Considerations Choice of DVs also needs to be carefully considered, highly correlated DVs severely weaken the power of the analysis. Choice of the order in which DVs are entered in the stepdown analysis has an impact on interpretation, DVs that are causally (in theory) more important need to be given higher priority Generalizability is limited to the population studied Missing data, unequal samples, Missing data, unequal samples, number of subjects and power Missing data needs to be handled in the usual ways Unequal samples cause non­orthogonality and the total sums of squares is less than all of the effects and error added up. This is handled by using either: – Type 3 sums of squares assumes the data was intended to be equal and the lack of balance does not reflect anything meaningful – Type 1 sums of square which weights the samples by size and emphasizes the difference in samples is meaningful Missing data, unequal samples, Missing data, unequal samples, number of subjects and power You need more cases than DVs in every cell of the design and this can become difficult when the design becomes complex If there are more DVs than cases in any cell the cell will become singular and cannot be inverted. If there are only a few cases more than DVs the assumption of equality of covariance matrices is likely to be rejected. Missing data, unequal samples, Missing data, unequal samples, number of subjects and power Plus, with a small cases/DV ratio power is likely to be very small and the chance of finding a significant effect, even when there is one, is very unlikely you can use programs like GANOVA to calculate power in MANO...
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This document was uploaded on 01/16/2014.

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