Which factorial design

In the simplest psychology experiments, researchers look at how one independent variable affects one dependent variable. But what happens if researchers want to look at the effects of multiple independent variables? This type of study that involve the manipulation of two or more variables is known as a factorial design. As you may recall, the independent variable is the variable of interest that the experimenter will manipulate. The dependent variable, on the other hand, is the variable that the researcher then measures.

By doing this, psychologists can see if making changes to the independent variable results in some type of change in the dependent variable. For example, imagine that a researcher named Sarah wants to do an experiment looking at whether sleep deprivation has a negative impact on reaction times during a driving test. If she were to only perform the experiment using these variables — the sleep deprivation being the independent variable and the performance on the driving test being the dependent variable — it would be an example of a simple experiment.

She has just added a second independent variable of interest sex of the driver into her study, which now makes it a factorial design. In this type of study, there are two factors or independent variables and each factor has two levels.

The number of digits tells you how many in independent variables IVs there are in an experiment while the value of each number tells you how many levels there are for each independent variable. This should, of course, be confirmed by a two-way analysis of variance with interaction as described in section In contrast, here are the results with two different strains C3H and outbred CD Chloramphenicol seems to reduce red blood cell counts and CD-1 seems to have higher counts than C3H.

However, plotting the means below also shows that there is an interaction. Strain C3H triangles responded to chloraphenicol by a reduction in red blood cell counts, but in CD-1 circles there was no response.

The data should be analysed by a two-way ANOVA with interaction to see whether the interaction effect is statistically significant, as shown in section Implications of strain x treatment interactions. Strain by treatment interactions are almost universal. This means that results based on a single strain or outbred stock can not necessarily be generalised. However in none of 13 studies were any effects observed when the CD:SD stock of rats was used.

In vivo effects of bisphenol A in laboratory rodent studies. Reprod Toxicol ; Split plot designs. These are randomised block designs with a factorial treatment structure in which a main effect is confounded with blocks. They are worth knowing about because in some situations they may make efficient use of resources. Suppose the aim is to compare two or more treatments using a randomised block design. For example, the experiment on the right has two animals in a cage, each receiving a different treatment.

Factorial design has several important features. Whenever we are interested in examining treatment variations, factorial designs should be strong candidates as the designs of choice. Second, factorial designs are efficient. Instead of conducting a series of independent studies we are effectively able to combine these studies into one.

Finally, factorial designs are the only effective way to examine interaction effects. So far, we have only looked at a very simple 2 x 2 factorial design structure.

You may want to look at some factorial design variations to get a deeper understanding of how they work. You may also want to examine how we approach the statistical analysis of factorial experimental designs. We send an occasional email to keep our users informed about new developments on Conjoint. You can always unsubscribe later.

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Free Survey Tool Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys. Start now View details. An interaction is when the effects of one variable vary according to [Page ] the levels of another variable. Such interactions can only be detected when the variables are examined in combination.

When using a factorial design, the independent variable is referred to as a factor and the different values of a factor are referred to as levels. For example, a researcher might examine the effect of the factor, medication dosage, of different