Hi all,
Just wanted to ask If I test how IVs A,B,C,D, E, F predict depended variable Y and given that: .
A is point in time (before or after manipulation).
B is the group to which participants belong (control or experiment).
C and D are two measures of well being.
E and F are sex and age.
Y is measure for resilience.
Now, here is where it become a bit complex, I assume that after manipulation there would be increase in scores of C and D for the experiment group but not for the control group. Also, C and D scores will positively predict Y scores for both control and experiment groups. Of importance, I assume that C and D will predict the same or higher level of the variance in Y scores and that Y scores therefore would be higher for the experiment group after the manipulation (no change in the control). No special predictions for sex and age, they are very much covariates.
So, what analysis should I use?
Chi-Square test should do, I think. Is it possible to share snippet of your data? I can suggest better then.
My data is on ratio scale, not nominal. I was thinking more on something like time series regression but I'm not certain. Repeated measures ANOVA doesn't fit here in my opinion.
Times series by using a software such as SPSS is helpful in this case.
My suggestion is to predict value of Y by using only C and D variables. (Parameter A can be define as time parameter. For example: Y(t) = C(t) + D(t)
You need to predict for control group and experiment group separately.
The same for parameters E and F. For E, you have only MALE and FEMALE, so it's easy to deal with it. But for age, depends on the variety of ages, you can divide them into groups. For example: 0-10, 10-20, and so on.
Thus, by using your data, you can obtain many information. Don't limit yourself in just one global formula to predict Y based on all mentioned parameters.
Good luck
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