again I am fighting with stata to do the right analysis. My data is a panel
design, 6 measuring times, 4 groups to compare, n=75.
I try to test whether 4 different groups differ in terms of means over time
regarding a variable (motivation). In other words wether motivation differs
between the 4 groups and wether there is a change over time (motivation is
decreasing f.e.). First I did a nice graph to see the differences but now I
would like to test them using manova.
I did a manova (for repeated measures)
manova motiva_1 motiva_2 motiva_3 motiva_4 motiva_5 motiva_6 = group
Then I checked, which of the 4 groups differ significantly.
manovatest, test(c) ytransform(m)
manovatest, test(c1) ytransform(m)
manovatest, test(c2) ytransform(m)
manovatest, test(c3) ytransform(m)
manovatest, test(c4) ytransform(m)
manovatest, test(c5) ytransform(m)
manovatest, test(c6) ytransform(m)
This worked out perfectly.
1.) However, now I would like to see, wether there is a over-all time-effect
(wether motivation is decreasing or increasing over time) for all groups
together and for each group. This is very easy in SPSS but I did not figure
it out in Stata even though I read different manuals and books.
2.) Secondly I would like to see wether there is a significant difference
between two groups at a certain time-point (f.e. group 2 and 4 at time 3) .
I could do this by using a simple anova but maybe I can integrate it in the
manova-model as well?
3.) Thirdly I would like to know wether a decrease for one group differ from
a certain increase of another group between two time-points (f.e. wether the
decrease of group 4 between time 2 and time 3 differes significantly from
the increase of group 1 at the same time period.
Could you please tell me, how to write the matrix for it. I just did not
understand it by reading the manual or other books. Question 1 is the most
important. I know I could do Question 2 and 3 by using a form of
panelanalysis, but since I am working with psychologist I would rather go
for anova with repeated measures.
Thanks a lot,
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