The knowledge of control variables is extremely important for the students and academic professionals which will give us critical insight into how it improves our research outcome. So, first, we will start with the control variables’ definitions and examples associated with them.
A control variable is an experimental element which is constant or limited throughout the course of the research investigation. More often, the control variables may not have a direct interest in the aim and objectives of the study, but it tends to have a significant influence on the resulting outcome of the research.
Not just the control variables definition, but we will put forth the examples of it for better clarity.
For example, to evaluate the effect of soil quality on plant growth, the temperature, light and water are held constant during an experiment, which is referred to as the controlled variables.
Similarly, to investigate the relationship between happiness and income, we measure the control variables of age, health and marital status.
Let us provide some more examples for better understanding!!
Experiment | Control variables |
Medicine can reduce illness | Health Age |
Supplements can improve the memory recall | Time of medication Sleep amount Familiarity with recall |
Effect of temperature and kiln time on clay pot quality | Clay type Level of ambient humidity Clay moisture |
In research studies and experiments, the aim is to understand the impact of an independent variable on a dependent variable. The control variables ensure to keep the experimental results are fair and unskewed devoid of any experimental manipulation.
The above examples indicate that control variables ensure the results obtained are solely dependent on the experimental evaluation. The variables independent or dependent, are not the primary focus of any research, rather keeping their values constant throughout helps in the establishment of true correlations between the dependent and independent variables. Now don’t get confused between the control variables and control groups as they strike a stark distinction.
In the research methodology, the use of control variables must be identified with recorded values to evaluate the results with precision. Moreover, the implication of control variables increases the internal validity of your research study which is otherwise a pretty difficult task to attain. To be specific, internal validity improves the degree of confidence in the differences you observe in the findings and attain the correct conclusions.
Why is it important to have control variables?
Very simple!! If researchers do not have control variables planned in the research methodology, it will become difficult to figure out or prove their exact impact on the results. It is crucial to find out whether the results of the research are an effect of the independent variable to justify experimental errors. Moreover, controlled variations are important because even the slightest variations in the research findings could have a significant influence on the results. Another major advantage of control variables points out the convenience of reproducing any research study while creating a strong relationship between the dependent and independent variables.
Taking over the examples set above, while we try to determine the effect of soil quality on plant growth, the independent variable refers to the soil quality whereas the dependent variable indicates the rate of plant growth. Hence, if we do not have control over the soil quality, we may end up with skewed results which may distort the actual outcome of the study.
Approaches to control variables in Research
You can make use of several approaches to control the variables in a research study. In some scenarios, variables can be controlled directly or by using standardized procedures which will be discussed further.
- Some experiments can have several variables to control whereas in some cases the researchers may not be aware of all the potential variables that need to be controlled. Now, this sounds confusing, isn’t it? The random process controls of variables ensure to average of all the associated traits across the experimental groups which make them roughly equivalent while the experiment begins to start. Such random process controls prevent the occurrence of systematic differences between the multiple variable groups.
Nevertheless, the direct approach and random control of the variables are effective in equalizing the experimental groups, however, it may not be feasible always. So we apply the statistical approaches for better clarity in the process.
- Statistical techniques such as multiple regression do not create a balance between variable groups, rather it employs a robust model which can statistically control the variables. For example, the multiple regression analysis includes a variable within the model and holds it constant while the treatment variables keep on fluctuating. You can implement ANCOVA and ANOVA for the same process.
Plan your research methodology with us
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