# Applied Statistics Lesson of the Day – Choosing the Number of Levels for Factors in Experimental Design

January 7, 2014 2 Comments

The experimenter needs to decide the number of levels for each factor in an experiment.

- For a
**qualitative (categorical) factor**, the number of levels may simply be the number of categories for that factor. However, because of cost constraints, an experimenter may choose to drop a certain category. Based on the experimenter’s prior knowledge or hypothesis, the category with the least potential for showing a cause-and-effect relationship between the factor and the response should be dropped. - For a
**quantitative (numeric) factor**, the number of levels should reflect the cause-and-effect relationship between the factor and the response. Again, the experimenter’s prior knowledge or hypothesis is valuable in making this decision.- If the relationship in the chosen range of the factor is hypothesized to be roughly linear, then 2 levels (perhaps the minimum and the maximum) should be sufficient.
- If the relationship in the chosen range of the factor is hypothesized to be roughly quadratic, then 3 levels would be useful. Often, 3 levels are enough.
- If the relationship in the chosen range of the factor is hypothesized to be more complicated than a quadratic relationship, consider using 4 or more levels.

Reblogged this on Psychology & Statistics Tutor:Mentor and commented:

Really great resource for learning more about the Why of stat processes.

Thanks for re-blogging my post, Char! I’m glad that my blog is useful to you!