# Understanding  Factoral Design

When it comes to conducting experiments, researchers often use a technique called factorial design. This is an experimental design that allows for the manipulation of more than one independent variable at a time. In this post, we'll explore the basics of factorial design and answer some common questions about it.

## What is Factorial Design?

Factorial design is a type of experimental design that involves manipulating more than one independent variable at a time. This means that the researcher creates multiple treatment groups, each with a different combination of independent variables. The goal is to determine how each independent variable affects the dependent variable and whether there are any interactions between them.

## What are Independent Variables?

Independent variables are variables that are manipulated by the researcher in an experiment. These are the variables that have an effect on the outcome of the experiment. In factorial design, there can be more than one independent variable.

## What are Dependent Variables?

Dependent variables are the outcome or result of an experiment. These are the variables that are measured to determine the effect of the independent variables.

## What is a Control Group?

A control group is a group in an experiment that does not receive any treatment or manipulation. It serves as a baseline against which the treatment groups can be compared.

## What is a Treatment Group?

A treatment group is a group in an experiment that receives a specific treatment or manipulation. In factorial design, there can be multiple treatment groups, each with a different combination of independent variables.

## How does Factorial Design work?

Factorial design works by manipulating multiple independent variables and measuring their effects on the dependent variable. By creating multiple treatment groups with different combinations of independent variables, researchers can determine how each independent variable affects the dependent variable and whether there are any interactions between them.

### References:

1. Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data: A model comparison perspective. Psychology Press.
2. Rogosa, D. R. (1979). Factorial Experiments. Handbook of Social Science Methodology, 193-231.
3. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
4. Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical principles in experimental design.
5. Campbell, D.T., & Stanley, J.C. (1966). Experimental and Quasi-Experimental Designs for Research. Houghton Mifflin Company