Experimental Designs Week 4 Solution

Solutions and explanations for experimental design concepts from Week 4.

Anna Wilson
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Experimental Designs Week 4 Solution
Experimental Designs
2. Explain the difference between multiple independent variables and multiple levels of independent
variables. Which is better?
Answer:
The general purpose of multivariate analysis of variance (MANOVA) is to determine
whether multiple levels of independent variables on their own or in combination with
one another have an effect on the dependent variables. MANOVA requires that the
dependent variables meet parametric requirements.
MANOVA is used under the same circumstances as ANOVA but when
there are multiple dependent variables as well as independent variables within the model
which the researcher wishes to test. MANOVA is also considered a valid alternative to
the repeated measures ANOVA when sphericity is violated.
Like an ANOVA, MANOVA examines the degree of variance within the
independent variables and determines whether it is smaller than the degree of variance
between the independent variables. If the within subjects variance is smaller than the betw-
een subjects variance it means the independent variable has had a significant effect on the
dependent variables. There are two main differences between MANOVAs and ANOVAs.
The first is that MANOVAs are able to take into account multiple independent and multiple
dependent variables within the same model, permitting greater complexity. Secondly,rather
than using the F value as the indicator of significance a number of multivariate measures.
MANOVAs the independent variables relevant to each main effect are weighted to give
them priority in the calculations performed. In interactions the independent variables are
equally weighted to determine whether or not they have an additive effect in terms of the
combined variance they account for in the dependent variable/s.
The main effects of the independent variables and of the interactions are examined with
all else held constant. The effect of each of the independent variables is tested separately.
Any multiple interactions are tested separately from one another and from any significant
main effects. Assuming there are equal sample sizes both in the main effects and the inter-
actions, each test performed will be independent of the next or previous calculation (exce-
pt for the error term which is calculated across the independent variables).
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Subject
Statistics