Dear All, I have a question about including covariates in the ANOVA analysis.
We grew corn seedlings in about 32 field plots and then applied 4 different treatments to study their responses (plot is the experiment unit). However, we noticed quite big variation of seedling healthiness from plot to plot BEFORE the treatments were applied. So we scored the healthiness from 1 to 5 (least healthy to most healthy) and planned to include this as a covariate in the model. During data analysis, I noticed that the healthiness was confounded with treatments, with some treatments applied to most of the healthy plots, and other treatment applied to most of the not healthy plots (we could not control that because treatment to each plots was pre-determined). As a result, the analysis on some of the variables show some strange patterns, especially when the healthiness covariate was significant in the model. For one variable, for example, the least-square mean estimates of the four treatments were A=B=C<D if covariate was NOT included, but became A=B=D<C if covariates was included in the model. I acknowledge that covariates serve their important role in controlling factors that were not imposed by the treatment. However, I am just wondering when the covariate is confounded with treatment, and had significant affect on the results, can we argue that the covariate could be excluded from the model? Have you ever have to deal with this similar situation before? Any thoughts will be appreciated. Thanks. Jing Luo
