Jason, I’m going to have to heartily agree here. My first thought, when you ask how to “analyze” your data, is that this is a fairly broad term, and the answer will depend entirely on what your question is. I would encourage you to think in terms of parameter estimations. At the end, you will have a number (or several) that describes your results. A G statistic (or t, or Chi-sq, or a p-value, for that matter) is not very informative. A slope of a relationship between two variables (or a difference between two means, or a probability of some event occurring), on the other hand, is quite informative. So think in terms of a somewhat skeptical audience, who, if told, “there is a difference,” will immediately respond, “but how large of a difference?” Being able to answer that question should guide you in producing the most appropriate model.
Cheers Matt Talluto On Feb 12, 2014, at 21:41, David Schneider <[email protected]> wrote: > 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
