Hello Jason, The 21st century approach to percent and count data is to write the model, not search for the 'right test.'
In my experience it is possible for 4th year undergrads and 1st year grad students, with little stats experience, to learn this approach. Statistical analysis based on writing the statistical model can be carried out in almost all stat packages, including SPSS and Minitab. Not to mention SAS and R. Statistically adept readers of Ecolog will recognize problems with zeros when analyzing percent data or count data once one has learned to write the model. These include too many expected values less than zero, or other problems such as zero inflated counts. I trust they will hold off on such problems -- in my view the first and most important step for you is grasping the idea of writing the model that captures your conceptualization of the research question and operating hypotheses, instead of searching for the 'right test.' In the fall term of 2013 a highly motivated grad student with at best a tenuous grasp of algebra learned this approach. If she can learn to write the model, and execute it, and interpret the result, and check the assumptions, then you can. Wishing you the best, David S. http://www.mun.ca/biology/dschneider/ > > On Wed, Feb 12, 2014 at 12:56 AM, Jason Hernandez < > > [email protected]> wrote: > > > >> Some time ago, I inquired about ways to analyze percent cover data, and > >> one of the suggestions was to test for heterogeneity. The snag, however, > >> is that this requires multiplying each cell value by its natural log. My > >> data set has a lot of zero values, which are important to keep; but of > >> course there is no natural log of zero. Is there a way to adjust the > >> analysis to included these zero values? i have not managed to find > >> anything on this. > >> > >> Jason Hernandez
