2005-10-31 04:08:11 UTC
techniques over the past few months to try to get a viable method to
use on my dataset, and am still somewhat confused.
I gathered data on about 200 people. My independent variables are
genotype at two genes (each genotype can be considered a binary
variable with two roughly equal sized groupings), gender, and season of
birth (a binary variable separating the year into halves). The
dependent variables of interest are about 15 continuous psychological
scales (based upon past research that derives these scales from factor
analysis of many other individual questions), and about 4 other
continuous variables that I am interested in such as at what age
subjects think they will die at. The dependents are often significantly
correlated with each other.
The independents are expected to have small and perhaps interactive
effects on the dependents. The analysis is meant to be exploratory. I
expect little to none of my "significant" results to hold up to
corrections for multiple testing.
I considered DFA or logistic regression using one of the binary
independent variables as a pseudo-dependent variable. The
pseudo-independent continuous variables would then be ranked as to
which best distinguish between the binary pseudo-dependent. I could
solve the problem of multicollinearity by doing a PCA on the continuous
variables. I decided against this method because its method of flipping
the dependent/independent relationship on its head is dubious, factors
found significant in DFA would have to be deconstructed to understand
what they are saying, and further analysis would need to be done to
evaluate interactions between the binary dependents.
MANOVA seems like a good alternative. It allows interaction effects and
has a fairly straightforward interpretation. However I have some
-For my interpretation I plan to report the results for each
multivariate main effect and the univariate individual effects
regardless of if the main effect is significant. I realize a common
technique is to only proceed to the individual effects if the main
effect is significant. Is my plan acceptable in an exploratory
-The effect of multicollinear dependents on a model is ambiguous. Some
say that correlated dependents are a serious problem
(http://www.matforsk.no/ola/ffmanova.htm), while others present a more
ambiguous case (How the Power of MANOVA Can Both Increase and Decrease
as a Function of the Intercorrelations Among the Dependent Variables.
Cole, David A.1; Maxwell, Scott E.1; Arvey, Richard2; Salas, Eduardo3,
Psychological Bulletin. Vol 115 (3), May 1994, pp. 465-474). Fooling
around with my model so far, I find that changing the number of
independents and dependents in the model changes my P values some, but
not a ton. How much should I be worrying about this assumption?
Having four interacting binary independents with N=200 causes some
major stratification. I've read that no cell in the analysis should
have an N=20, or alternatively that the minimum N of the lowest cell
should not be outnumbered by the number of dependent variables. I may
lower my number of dependents to fit the latter rule if it is correct.
Is MANOVA a good option for my needs? Would I be better off doing one,
two and three way ANOVAs and the nonparametric equivalents
Thanks for any comments you can provide.