Comparison of variables

There are mainly two variables; independent and dependent variables. The independent variables are those that a researcher or experimenter changes or manipulates and are expected to impact the dependent variable (Mcleod, 2019) directly. For instance, when one wants to experiment if exhaust fumes from vehicles of high concentrations affect asthma incidence in children, the independent variable is represented by the vehicle exhaust while the dependent variable is asthma. Usually, the independent variable functions as the only significant difference between control groups and experimental activity in well-structured empirical research. A dependent variable stands for the variable being measured and tested in an experiment, and it depends solely on the independent variable. For example, depression symptoms are a dependent variable that is dependent on therapy (an independent variable).

On the other hand, an Extraneous Variable has an effect on the relationship between the dependent and independent variable when explaining an outcome. In the case of asthma and car fumes, the Extraneous Variable would be a different exposure to factors that increase the chances of respiratory issues, such as smoke from cigarettes and factories (Sheppard, 2020). It is unethical to expose random people to high concentrations of exhaust deliberately. Therefore, an experiment seeking to compare two populations with differential exposure to car exhaust would depend on natural experimenting or circumstances that such occurrences are already happening due to unrelated situations. In a natural experiment, a community residing close to areas with greater exhaust concentrations may also live near factories with higher levels of smoking. Therefore, the Extraneous Variable could easily lead to bias during research. If an extraneous variable is the real cause of an outcome, it is referred to as a confounding variable since it confuses the relationship that a researcher was focused on.

Two ways of controlling Extraneous Variables

          While analyzing statistics, researchers must account, control, or attempt to remove any extraneous variables in the study design. Researchers can try to control Extraneous Variables using the following two ways. The first is the use of standardized procedures (Sheppard, 2020). Such a process involves the researcher ensuring that every feature of the experiment is the same, excluding the independent variable. For example, an experimenter can utilize the same procedure to recruit volunteers and participants and then carry out the experiment in the same environment. These researchers should provide some explanations to participants, and feedback, when the study ends, should also be the same. When rewarding participation, it should be done the same way for all participants. Furthermore, the researchers should ensure that the study is conducted simultaneously, such as time, month, or hour. The area of experimentation or lab must be maintained at a constant temperature, brightness, and noise.

          The second method used by a researcher experimenting to control extraneous variables is by employing random assignations. This technique lowers the probability that some participants could affect the independent variable due to having certain specific characteristics (Sheppard, 2020). Random assignation calls for every participant in the experiment having an equal chance to be assigned to either side, the control group or the test group. This technique is more efficient if the sample size is large.