In a research study, a confounding variable can change the outcome of an experiment, as an external variable, the third factor can transform both independent and dependent variables in a research and thus affecting outcomes of correlational or experimental research. During a research experiment, a confounding variable is a third factor that can influence an experiment via generating the wrong research results. For example, it can render an incorrect correlational association between explanatory and target variables.
There is a much requirement to restrict the effect of confounding variables or confounders during the research process. As a core action, a researcher could control or avoid these variables in research through identifying and measuring the correlated third factor in the research framework.
It involves the distribution of confounders over the research data seldomly, and used especially in machine learning for assigning variables randomly in order to manage a group in the research that assists in preventing any condition of selection bias in research tasks.
During an experimental research, randomization enables researchers to control these variables by diverting the experiment to assemblage of observations from considering each individual case where statistical tools are practiced for interpreting the insights.
A random sample is the sample where each element has an equal opportunity to be sampled under the sampling group, since a perfect random sample of observation is problematic to collect, an action of relatively closed randomization is achieved. This method constraints the study of research variables with the control of confounding variables, and if it is not conducted precisely, it can result in confounding bias.
Basically, it restricts the research data by introducing control variables to confine or bound the confounding variables. Under this method, confounding variables are spread across the research data evenly via controlled research process as before and after experiments.
It makes observations in pairs for each value of the independent variable, identical to a possible confounding variable.
Case-control study works as a matching method, it matches variables of similar characteristics with the same set of controls variables. A case-study method can have two or more control variables for each case as it provides more statistical accuracy in the research process. From segmenting the data sample into tiny groups to examining the association among the dependent and independent variables, this method involves the assessment of altering effects and controlling of confounding variables.
Another method is introducing counterbalancing by examining several research analysis parameters, where half of the group is evaluated under first condition and another half under second condition. These variables can be positive or negative to correlate with both dependent and independent variables. Confounders are extraneous variables and their existence affects the variables being studied such that the outcome would never reflect the actual relationship amid the underlying studied variables.
Confounding results in invalid correlations, increasing variance, and introducing a bias. Be a part of our Instagram community.
What are Confounding Variables? Introduction In statistical data analysis , the independent variable has an effect on the dependent variable. Bias can be divided into three major categories Selection Bias Information Bias, and Confounding During the blog discussion, we focus on confounding, recognizing it, and controlling its effects.
Confounding Variables Confounding variables or confounders are often defined as the variables that correlate positively or negatively with both the dependent variable and the independent variable. Research Paper , Cause-effect relationship with confounders These variables are confounding because they perform such that to confuse and complicate both the findings from the data and the inferences drawn from the study. A confounding variable is closely related to both the independent and dependent variables in a study.
An independent variable represents the supposed cause , while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists. There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.
In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables. In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable.
In statistical control , you include potential confounders as variables in your regression. In randomization , you randomly assign the treatment or independent variable in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables. Have a language expert improve your writing. Check your paper for plagiarism in 10 minutes.
Do the check. Generate your APA citations for free! APA Citation Generator. Home Knowledge Base Methodology Understanding confounding variables. Understanding confounding variables Published on May 29, by Lauren Thomas. What can proofreading do for your paper? What is a confounding variable? What is the difference between confounding variables, independent variables and dependent variables?
Why do confounding variables matter for my research? Strengths of Randomization There is no limit on the number of confounders that can be controlled It controls for both known and unknown confounders If successful, there is no need to "adjust" for confounding Limitations of Randomization to Control for Confounding It is limited to intervention studies clinical trials It may not be completely effective for small trials Restriction of Enrollment Limiting the study to subjects in one category of the confounder is a simple way of ensuring that all participants have the same level of the confounder.
For example, If smoking is a confounding factor, one could limit the study population to only non-smokers or only smokers. If sex is a confounding factor, limit the participants to only men or only women If age is a confounding factor, restrict the study to subjects in a specific age category, e. Drawbacks of Restriction Restriction is simple and generally effective, but it has several drawbacks: It can only be used for known confounders and only when the status of potential subjects is known with respect to that variable Residual confounding may occur if restriction is not narrow enough.
For example, a study of the association between physical activity and heart disease might be restricted to subjects between the ages of , but that is a wide age range, and the risk of heart disease still varies widely within that range. Investigators cannot evaluate the effect of the restricted variable, since it doesn't vary Restriction limits the number of potential subjects and may limit sample size If restriction is used, one cannot generalize the findings to those who were excluded.
Restriction is particularly cumbersome if used to control for multiple confounding variables. Matching Compared Groups Another risk factor can only cause confounding if it is distributed differently in the groups being compared. For example, In a case-control study of lung cancer where age is a potential confounding factor, match each case with one or more control subjects of similar age.
If this is done the age distribution of the comparison groups will be the same, and there will be no confounding by age. In a cohort study on effects of smoking each smoker the index group who is enrolled is matched with a non-smoker reference group of similar age.
Once again, the groups being compared will have the same age distribution, so confounding by age will be prevented Advantages of Matching Matching is particularly useful when trying to control for complex or difficult to measure confounding variables, e.
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