Comprehensive Guide on Confounding Variables

Want to eliminate the complex factor from the confounding variables in a min? Let’s consider that you are conducting a study on how women’s age affects their employment rate. Women’s age here is your independent variable which cannot be changed by you. While the employment rate of women is the dependent variable that changes with the independent variable. You will find that the low employment rate of women is dependent on the age of women. But is this cause-and-effect result right? No. The lower rate of women’s employment is because of the other factors like gender discrimination, early marriage and lack of education. These factors, if not considered, will make your research faulty. These factors are known as confounding variable. 

If you weren’t able to understand the confounding variable by the example above, then let’s focus on its definition. 

What is Confounding Variable?

A confounding variable is the factor that is related to the independent variable and hence has an impact on the dependent variable. In any quantitative research, independent and dependent variables are used to produce a cause-and effect result. Sometimes, if the environment is controlled, like in labs, the relationship between independent and dependent variables can be achieved accurately. 

But if the study is conducted in an uncontrolled environment, like interviews and surveys, the cause-and-effect relationship is not as direct and simple. It becomes complex due to the confounding variables that don't let the independent variable to directly impact the dependent variable. 

Confounding variables are also known as confounders, confounding factors or extraneous variables. It is very important to consider the impact and meaning of confounding variable in your paper to avoid the biased and faulty results and maintain the credibility of the paper.

Talking about credibility, let us see how confounding variables might impact your research if ignored 

Impact of Confounding Variables 

Credibility of the Assignment 

If you ignore the impact of the confounding variables on the data collected, the result of the data analysis will not be accurate. Hence your entire paper which is based on the result will lack validity. Hence being ignorant to confounding variables can reduce the credibility of your assignment. 

Biased Result 

Another impact of ignoring confounding variable is biased results. When you ignore the marital status, education and inequality women face, the result of your data analysis will be biased because of course women without education or those who have faced gender discrimination will not get employment, irrespective of their age. 

Biasness is not expected in academia which is responsible for creating results that change society. 

Ethical Problems

If your result is biased and inaccurate and the study is invalid, it can lead to ethical violations. The basic rule of academic writing is to produce a valid and original study. Not considering concluding factors will never provide you the original data and hence the original result. 

Generalising 

If you don’t consider confounding variables, your result will be a generalization. Because it won’t include the factors that differentiate the members of the treatment group. Hence the result will be merely a generalization which will not be applicable to a larger group.

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But if you consider the confounding variable, how will the research be different? Let’s see

Why is it Important to Consider Confounding Variables? 

Valid Inferences 

If you take confounding variables into consideration and plan how to control them, your data collected will be valid. It will apply to the entire population and will lead to a valid solution. 

Improve the Research Design 

Considering confounding variables and collecting data accordingly will improve the quality of your research. It will include a deep study that doesn’t float on the surface.

Now, you might be wondering that confounding variables will always be there. Then how can any data collection be accurate? Don’t fret. Confounding variables are necessarily inevitable but not uncontrollable. This means that you can control or minimize the impact of confounding variables in your data collection and results. How? Read on.

How to Control Confounding Variables?

Restriction 

In this method, you can control the impact of confounding variables on your data collection by restricting your categories to specific characteristics. Rather than including a huge group of participants in your study, include only those who have the potential confounding factors. This will help you get the right data because the impact and meaning of confounding variable are already in consideration and cannot impact the independent variable. 

For example, rather than studying a huge population of women with different ages, include only those who are of the same age, married early, have no education and have faced instances of gender discrimination. Then, the correlation between independent and dependent variables is direct. 

Matching 

In this method, you will have to create two groups- one is the main treatment group and the other is the comparison group. Each member of the treatment group must have a counterpart in the comparison group. This means that each woman who was married early with no education in the group under study must have another woman in the same situation. But the difference will be in the independent variable. Each woman of the treatment group will have a different age than their counterpart in the comparison group. 

This will help you eliminate the potential of confounding variable to affect the data collection. Even if there is a confounder, it is because of the difference in independent variables which you can change. 

Statistical Control 

In this method, you have to use a regression model of measuring how dependent variable changes with the change in the independent variable. After collecting the data (irrespective of the confounding variables) measure it on the regressive model. Turn confounding variables into control variables and consider them during the measurement. Hence you can easily bifurcate the data collected from those that are influenced by confounding variables. 

For example, after collecting data of employment rate from a population of women with different ages, remove those women who are influenced by confounding variables. Hence you will be able to find the relationship between the age of women and their employment rate. 

Randomisation 

The last and the most effective method is randomisation. In this, you can randomly assign people into two groups- treatment group and the control group. In randomisation, each person has the equal chance to get assigned in the group. So in this group assignment, the potential confounding variables and the other confounding factors will be average and hence won’t impact the independent variable. 

Now that you are familiar with the meaning of confounding variable and how it might affect your data collection, start researching the potential confounding variables. Choose the right sampling method and the right method to control the confounding variables. The process will be really informative and interesting if you do it with dedication. So start the process now . 

It might be possible that your research is not enough to cover all the potential confounding variables. Some might be left because in our society everyday there are new factors taking birth which are affecting humanity. For example, if your assignment is on statistics, you might be aware how they change everyday. Consider the share market. Each day or each specific event leads to rise and fall in the share market. If you want to study the share market, you cannot do it independent of the factors affecting it. So in this case you can reach out to online statistics assignment help. 

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