Factorial ANOVA

I am not sure if what i am about to ask would make complete sense so let me start from the beginning.

I'm doing research to measure five different psychometeric traits in two groups of students. I also want to see how these five traits interact with one another.

For some reason while gethering information i was under the impression i would be using a 2 X 5 facotrial ANOVA, but just realised that this does not seem to make sense as i do not know what my Dependent variable would be. For my study, one of the five traits, Befallen Injustice(BI), is the dependent variable. Am i missing something? Should i not be using an ANOVA but a simple independent t- test instead and a correlation to see the interaction between traits. I feel i would be missing out on valuable information by doing this.

Is there anyone who could suggest a way of picking up on all important information?
What isn't clear from your question is what is the research question you want to test, in terms of the 5 traits and the two groups.

For example: You mention BI, one of the traits, as the DV. Do you want to test if the two groups differ on BI? If the other four traits affect or are associated with BI values? Or if the two groups differ in how much the other four traits affect BI?

Or just how the 5 traits coincide within each of the two groups?

Or something entirely different? A lot of people really have trouble getting specific at this point, but it's really crucial in deciding the right test.

Hi Karen, thanks for that..

Specificly i hope to find how the 4 traits affect or are associated with the Dependent Variable in each of the two groups. By doing this will i be able to compare each of the two groups? For example

One of my traits is Depression.. Say i find and association between DI and Depression, will i also be able to say that group A had higher Depression than group B?

What i was almost about to do was use multiple regression with 4 IVs to the 1 DV, i am just wondering how to incorporate the two groups into this equation.