Hello all,
I have been searching for some advice on appropriate non-parametric statistics
for the analysis of a dependent variable that fails normality and homogeneity
assumptions under both sqrt and ln transformations.
First I will describe the dataset. The data are from a field sample. I have 4
years of data from the same set of ecological populations. The number of
populations varies year to year. The number of individuals sampled in a
population may have varied within and among years.
Here is a description of the model I would like to implement. Let’s say the
Dependent Variable is # seeds eaten / plant. So, I want to implement
individual plant nested within population (i.e. a mixed model with population
identifier as random variable or SUBJECT(PopID)). YEAR is a categorical
independent variable, Population Size is one continuous independent variable.
Total # Seeds produced / plant is another continuous independent variable. I
would also like to test interactions.
As I said before, I was not successful in transforming my dependent variable
using my standard choices (ln and sqrt). I had found references to using rank
transformed data in an ANOVA / ANCOVA model, but this was rejected by a
reviewer. I am familiar with simple nonparametric tests like Kruskal-Wallis,
but I do not see how to preserve the complex model with such tests.
My first hope is to find a method, generally accepted by ecologists, that is
easily implemented in SPSS. If this is not possible, I can explore more
complicated analyses with the help of my campus math / stats consultant.
Thanks for you advice.
| / \ | Alan B. Griffith, PhD
\ \ ̗ ̖ / / Associate Professor
\ \( )/ / Department of Biological Sciences
\ ( ) / University of Mary Washington
/( )\ (540) 654-1422
/ / ( ) \ \ [email protected]
/ | ¦¦ | \
| |
