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

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.

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

1. Psych n Stats Tutor says:

Reblogged this on Psychology & Statistics Tutor:Mentor and commented:
Really great resource for learning more about the Why of stat processes.