Independent and dependent variables are the two variables you mostly see in research. Whether it is scientific, sociological, or psychological research, you will mostly see these two types of variables. But do you know that there is also a third type of variable that governs the results of a study? Ohh, you do not know about that variable. Okay, do not worry; I am going to explain that variable in detail. The third type of variable that acts in secrecy is confounding variables, often called confounders or confounding factors.
Many researchers either do not know about these variables or neglect them. In both cases, this thing is fatal for research. It is because the research results obtained will not be valid unless you consider confounding variables. Due to this, there is a need to discuss these variables in detail so everyone knows about them. It is why today’s article is about explaining the importance, examples, and other things related to these variables. Hence, let’s begin our discussion with the following question:
What are confounding variables? Also, explain its importance in research.
Definition
In cause-and-effect research, the third type of variable affects the proposed cause and proposed effect. Therefore, it is important to define those variables first-hand. Confounding variables are a type of extraneous variables that relate to the independent and dependent variables. In simple words, you can say that it is a variable that is not present in an experiment, yet it can severely affect the relationship of other variables. A variable must meet the following conditions to be a confounder:
- There must be a correlation between the confounder and the independent variable. This relationship can either be a casual one or not.
- The relationship with the dependent variable in the study must be casual.
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Importance
The researchers must account for confounders because they often affect the study results. The degree of precision of the results of the research depends on the influence of these variables on other variables. Therefore, you must always account for these in your study. Also, knowing about these variables when you perform surveys or observational studies. It is because you cannot control the variables. So, there is always room for confounders, and you must consider them.
Example of confounding variable
From the discussion above, you must have got an idea about what confounding variables are and why considering them in research is important. Now, let’s look at an example to give practical touch to the discussion above. The example is as follows:
A mother’s education
Suppose a researcher has done a study to show whether bottle-feeding is related to an increase in diarrhoea in infants or not. As you can see, this research is cause-and-effect research. The cause of the effect of diarrhoea is bottle-feeding. Logically, it seems that bottle feeding is more prone to this disease in infants. But the research has proved that breastfed infants are more likely to catch diarrhoea. Bottle feeding actually shields children from this illness. Now, in this whole study, you see an invisible variable that is affecting the entire scenario. That variable is the mother’s education. Therefore, you must take it into account.
Decreasing the impact of confounding variables
The effect of confounding variables is evident from the discussion above. The researchers need to lower this impact to obtain better and more reliable results. Therefore, a brief description of the tips to follow in doing so is as follows:
Distribute confounders equally
The first tip to minimize the effect of confounding factors is to distribute them equally. The variation in the sample can cause problems in obtaining the results. Therefore, following this tip, the researcher can collect the samples independently. As the researcher has collected the samples on his own, there will be less influence of external variables on the study. For example, a researcher conducts a study to measure academic performance versus screen time. So, in the study, he selects all the students who are now in their first years and do not do a job. The qualities of all students are the same. So, it is how you distribute the confounders equally.
Restrict the study
Another great technique to minimize the effect of confounders on a study is to restrict the study. It is almost impossible for a researcher to find respondents that exactly meet the research criteria. It is because it is very difficult to look for data that meet the research goals and objectives. In this scenario, the best practice is to restrict the study. In this technique, the researcher identifies the confounders and eliminates them from the study altogether. Hence, instead of ticking the boxes against participants meeting the criteria, he ticks the participants that do not come under any criteria. In this way, the researcher restricts the study.
Randomization
As the name suggests, it is about selecting the respondents randomly. The researcher first defines a large population for research. After that, he adopts the easiest and most common method of doing research: randomization. He picks the research participants randomly to diminish the effect of confounders.
Also, in this method, the researcher divides the confounding characteristics of the samples equally among all of the samples. In this way, he gets rid of knowing the characteristics of individual samples. This method reduces the distortion in the samples and speeds up the selection process of samples. Using this method does not guarantee the complete elimination of confounding variables. To be sure of this, you must study the characteristics of the initially selected samples. If they do not meet the requirements of the study, make adjustments.
Conclusion
To summarise, confounding variables are problematic for research. They do not allow to obtain reliable results from the study. Therefore, you must know about the techniques to minimize their effects on the study results. The technique of randomization mentioned above is the most widely used one. Therefore, read it carefully along with other methods to remove the effect of the confounders.
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