R² of ANCOVA mostly driven by covariate Based on data from a scenario-based experiment, I am running a $2\times2\times2$ ANCOVA with one continuous covariate (sample size 320). Without including the covariate, the ANOVA model and two of the main effects are significant on the 5% level. However my $R^2$ is only around 3%. 
After including my covariate, the significance of my main effects and the ANCOVA model increases heavily. Moreover the $R^2$ of the model increases to around 25%.
The assumptions for including the covariate are met. How should/can I interpret such a result? Does it mean that the manipulated variables do only have a minor effect on the dependent variable and explain much less than the covariate? If so, is the experiment actually useless as the manipulated variables barely explain variance of the dependent variable?   
 A: When you run the ANOVA you get a unique prediction for each cell. So if you have 320 observations you have about 40 per cell. Since each of them gets the same prediction there is bound to be a lot of residual variability unaccounted for. When you add a covariate which has many values there will be many different predictions which can potentially be closer to the actual values leading to the phenomenon you observed.
A: It's difficult to tell since you are not providing sufficient background information regarding your experiment and the nature of your independent variables (IVs) - especially the nature of the covariate. Ideally provide a snapshot of your dataset, the output of your analysis incl. the code you ran to generate that output.
The reason that the two main IVs came out significant, even without including the covariate, is probably due to your large sample size of 320.
After adding the covariate things change and your $R^2$ goes up, which to me suggests that your covariate explains quite a bit of the variation in your dependent variable (DV). On that note, how did you decide that the assumptions for using the covariate were met?
Generally, I would say that your IVs did not manipulate the outcome in a way you would expected it, i.e. the IVs did not quite support your tested hypothesis (or better, left you with a good amount of unexplained variation).
Is the experiment useless? I don't think so. No experiment is useless since you had an initial hypothesis to test. Now comes the important part and thinking about why the IVs showed a rather weak influence on your DV. Also, it might be worthwhile thinking about why the covariate had such a big influence on your DV? There might be something interesting about this covariate which should be discussed and followed up upon. 
