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Designed experiments with full factorial design (left), response surface with second-degree polynomial (right) In statistics, a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors.
Example: Consider a fractional factorial design with factors ,,,, and maximum strength =. Then: All effects up to three-factor interactions are preserved in the fraction. Main effects are unaliased with each other and with two-factor interactions.
Factorial designs carry labels that specify the number of independent variables and the number of levels of each independent variable there are in the design. For example, a 2x3 factorial design has two independent variables (because there are two numbers in the description), the first variable having two levels and the second having three.
Design of experiments with full factorial design (left), response surface with second-degree polynomial (right) The design of experiments , also known as experiment design or experimental design , is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation.
A way to design psychological experiments using both designs exists and is sometimes known as "mixed factorial design". [3] In this design setup, there are multiple variables, some classified as within-subject variables, and some classified as between-group variables. [3] One example study combined both variables.
Andy Field (2009) [1] provided an example of a mixed-design ANOVA in which he wants to investigate whether personality or attractiveness is the most important quality for individuals seeking a partner. In his example, there is a speed dating event set up in which there are two sets of what he terms "stooge dates": a set of males and a set of ...
For example, the X 1 coefficient might change depending on whether or not an X 2 term was included in the model. This is not the case when the design is orthogonal, as is a 2 3 full factorial design. For orthogonal designs, the estimates for the previously included terms do not change as additional terms are added.
Plackett–Burman designs are experimental designs presented in 1946 by Robin L. Plackett and J. P. Burman while working in the British Ministry of Supply. [1] Their goal was to find experimental designs for investigating the dependence of some measured quantity on a number of independent variables (factors), each taking L levels, in such a way as to minimize the variance of the estimates of ...