Research experiments usually focus on the effects that one variable has on another. In some instances, the experiment only focuses on one variable, which makes the experiment simpler. In other cases, the situation has many variables that all affect the results. For example, the aquarium owner may want to know how much feed he needs to add to his aquarium. However, the effects of the feed quantity is dependent on how many fish are in the tank and how crowded they are.
While scientists try to study variables independently when they can, so they can more easily control the results in the experiment, sometimes the variables are interdependent. Therefore, scientists need to use different methods of experimentation. For example, educational researchers may study the effects of particular teaching methods on student performance on tests, but they must also consider other influences, such as socioeconomic background of the students.
Fractional factorial design is highly efficient. This method allows scientists to get the runs done all in one day. This allows scientists to focus on other aspects of their research. It can speed up research when it includes many variables.
Variables can increase the amount of time that research can take because scientists often must test each variable separately. Fractional factorial design can effectively test two different variables. It can also test more than two variables if necessary. This can become very unwieldy in instances where testing more than two variables is needed. However, this keeps the possibilities for experimental research open, since researchers sometimes need to study data with more than two variables.
Fractional factorial design is effective for preliminary research into connections between different variables. This allows scientists to quickly determine what direction to take the research, saving resources and time. This approach allows scientists to easily single out and analyze a single variable and decide whether a particular effect is the result of one variable or another.
Affordably Avoid Errors
The fractional factorial design can reduce the possibility of experimental error and confounding variables. This method allows scientists to have several levels of analysis more cheaply and simplistically. Since it is more affordable, this type of research can open up possibilities for research by freeing up grant money and other resources for other tasks.
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