Using factorial MANOVA in psychology assignment

Considering your I/O psychology research interests, identify two IVs (each with 2 categories) and three DVs that are measured on continuous scales. Think of DV measures that probably are moderately correlated with each other because they are measuring different components of the same or similar concepts (e.g., three different measures of academic performance). Given these variables and design, what information would a factorial MANOVA provide you?  What more would you want to know if you get significant results in the factorial MANOVA? Why would this be significant

to your research? (Research support is not required for this question.)

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I have provided and example of how the questions needs to be answered.

IV : Gender  IV 1 = Male Veteran and IV 2 = Female Veteran in the following 2 groups

Category  1 – level of experience (entry, mid, experienced)

Category  2 – college degree (none, Bachelors, Masters)

DV is:  Hiring data for:

– Veteran hiring rate and

– number of months taken to get hired

– average salary range received

________________________________________________________________________

What information would a factorial MANOVA provide you?

A factorial MANOVA design can look at the relationships between the main effects of each of the IVs, and their interaction with the DV.  With the Factorial MANOVA the interaction effect is how the combinations of levels of the IVs influence the dependent variables.  The MANOVA can identify main effect as well as interaction effects between the IV’s and the DV.  The main effect would tell us if there is a difference between our IV and all of DV’s.  Since we have two IV’s with two levels each the factorial MANOVA can

examine effects across all other variables and evaluate significant effects.  The interaction effect can exist between factors such as there an effect between gender with college

degree and level of experience effect hiring rate or any of the other DV’s; determining significant effects on all of the dimensions.   Thus we can identify the relationships with the most significant influence.  This helps us to answer if the IV has a significant impact on the DV, how does the IV relate to the DVs and what are the separate relationships for each variable and the power of the effect on each.

What more would you want to know if you getsignificant results in the factorial MANOVA?

A significant result can be seen in a factorial MANOVA in through the separate F-values generated for the main effect and the interaction effect between IV we are using such as

with the Wilks or Pillai’s trace criterion to evaluate the significance level.  When we look at the significance level and find it to be statistically significant we reject our null  hypothesis since the DV was significantly affected.  We can also look at the partial eta square and see if the effect had a large or small effect on the variable.  In my example I could have a significant effect between the IV level of experience and the DV salary range with a small effect, as where I may have a statistically significant effect on IV with

college degree and salary range with a large effect size.  I would then see that both effected the DV but that college degree has more influence on salary then does level of

experience.

Why would this be significant to your research?

When there are several similar (correlated) DV’s the MANOVA allows me to analyze single variables without running multiple individual test and causing type I errors.  It also allows me to investigate how the IV’s influence each of DV singularly and collectively.  However, additional Post Hoc test need to be run for this level of comparison. Understanding how each variable differs from each other and how they may overlap in their influence  we can see to what degree they correlate and how much one may affect the other or how the combination effects it.

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