In general, experiments have these three types of variables: independent, dependent, and controlled.
Identifying the Independent Variable
If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:
Is the variable one that is being manipulated by the experimenters?Are researchers trying to identify how the variable influences another variable?Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?
Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants). Below are the key differences when looking at an independent variable vs. dependent variable.
Types
There can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating. In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable.
Examples
To understand this concept, it’s helpful to take a look at the independent variable in research examples.
In Organizations
A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the color of the office is the independent variable.
In the Workplace
A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example.
In Educational Research
Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable.
In Mental Health Research
Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. The independent variable in this example is the type of therapy.
Impact
Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured. At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.
Choosing an Independent Variable
If you are designing an experiment, here are a few tips for choosing an independent variable (or variables):
Select independent variables that you think will cause changes in another variable. Come up with a hypothesis for what you expect to happen. Look at other experiments for examples and identify different types of independent variables. Keep your control group and experimental groups similar in other characteristics, but vary only the treatment they receive in terms of the independent variable. For example, your control group will receive either no treatment or no changes in the independent variable while your experimental group will receive the treatment or a different level of the independent variable.
Potential Pitfalls
It is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:
Extraneous variables: These are variables that might affect the relationships between the independent variable and the dependent variable; experimenters usually try to identify and control for these variables. Confounding variables: When an extraneous variable cannot be controlled for in an experiment, it is known as a confounding variable.
Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond).
