Hi,
In my experiment, I have two factors but one of them just has one level since it's not the main factor I don't want to have more levels.
I was wondering that with this situation should I use factorial design for data analysis or CRD is viable here?
Best,
No, is not mandatory. You can use for differents factors, differents levels. In this case, the process is unbalanced.
If something only has 1 level, then it's not something you put in the factorial design experiment.
If you have 2 factors and one doesn't change level, you have a simple ANOVA experiment. The one level factor isn't part of the design.
The simplest factorial design involves two factors each at two levels. The levels of this factors are not mandatory numerical. For example, to use the catalyzer in a reaction or not to use…You should just assign low and high levels. The high level corresponds to using the catalyzer. The low level is no catalyzer… In a similar way you can fit your factor..
If a factor has just one level, it is constant, and consequently completely confounded with the inrecept of the model. Technically, excluding the intercept from the model, you could obtain an estimate for this factor, but the interpretation of this is then the same as that of an intercept. Summarizing, taking a constant factor into a model doesn't make much sense.
You have two factors , the fist has many levels and the other only one level , if you performe analysis with simple experiment CRD the results includ only the combinations between the two factors and you can not compar the main effects of the factors , but in case of factorial you get the main effects of the two factors and the combinations between the factors , and this represent the best .
Good Luck
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