## What is a Definitive Screening Design?

A **Definitive Screening Design (DSD)** allows you to study the effects of a large number of factors* in a relatively small experiment. In simple terms, **DSDs are an improvement on standard screening designs** (like the Plackett-Burman) that prevent confounding of factors and can also detect non-linear responses.

**Factors *are sets of observed variables with similar response patterns; They are associated with a confounding variable that isnâ€™t directly measured.

## Definitive Screening Design vs. Standard Screening Design

One of the main differences (SAS, 2015) is that a definitive screening design can estimate quadratic (curvilinear) effects when the model contains only main effects and quadratic effects; a standard screening design can only detect linear effects on the response.

Another significant difference between the two designs is how they handle confounding. In Definitive Screening Designs, there is **never complete confounding** with any sets of two-factor interactions. For standard screening designs, the issue of confounding is only addressed with fractional factorial designs that are Resolution IV or higher. *Resolution* tells you how much confounding is in your model; resolution IV designs have—at most—confounding involving three-factor interactions. Confounding in resolution III designs (which are probably used the most) isn’t addressed at all with a standard screening design.

## Appropriate Uses for DSDs

Factors (levels of independent variables) should ideally have the following characteristics for a successful DSD:

**Continuous factors**are ideal.**Categorical factors are strongly**. A small percentage of categorical factors are fine, but too many and the result is what Jones and Nachtsheim (2013) call “…an undesirable choice.”*not*preferred- Start with at least
**six factors**. For 4 or 5 factors, create a DSD for 6 factors, then drop the last two columns. **Each individual factor should be able to combine with any other factor**. If any combinations are not possible, this reduces the design’s power.- Factors should be independent of each other. For example, if one goes up, it shouldn’t cause another factor to go down.

General Tips:

- Use during the
**first experimental stages**when reducing a large number of factors to a more manageable number. - Do not run the DSD as a
*split-plot design*or when the*a priori*model has higher order effects.

## References

Jones, B. & Nachtsheim, C. (2013). Definitive Screening Designs with Added Two-Level Categorical Factors. Journal

Journal of Quality Technology. Volume 45, Issue 2. Retrieved December 10 2017 from: https://www.jmp.com/content/dam/jmp/documents/en/white-papers/dsd-with-added-two-level-categorical-factors.pdf

Jones, B. (2016). Proper and improper use of Definitive Screening Designs (DSDs). Retrieved December 10 2017 from: https://community.jmp.com/t5/JMP-Blog/Proper-and-improper-use-of-Definitive-Screening-Designs-DSDs/ba-p/30703

SAS (2015). JMP 12 Design of Experiments Guide. SAS Institute.

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