What is Confounding?
Definition of confounding, from the Stat Trek dictionary of statistical terms and for an observed relationship between independent and dependent variables. Definition for confounding variable in plain English. How to Reduce Confounding Variables. They can suggest there is correlation when in fact there isn't. Internal validity means that a true cause-and-effect relationship exists between an exposure (the cause) and outcome (the effect) variable. Confounding is one of .
Beyond these factors, researchers may not consider or have access to data on other causal factors.
An example is on the study of smoking tobacco on human health. Smoking, drinking alcohol, and diet are lifestyle activities that are related. A risk assessment that looks at the effects of smoking but does not control for alcohol consumption or diet may overestimate the risk of smoking.
Decreasing the potential for confounding[ edit ] A reduction in the potential for the occurrence and effect of confounding factors can be obtained by increasing the types and numbers of comparisons performed in an analysis. If measures or manipulations of core constructs are confounded i.
Additionally, increasing the number of comparisons can create other problems see multiple comparisons. Peer review is a process that can assist in reducing instances of confounding, either before study implementation or after analysis has occurred. Peer review relies on collective expertise within a discipline to identify potential weaknesses in study design and analysis, including ways in which results may depend on confounding. Similarly, replication can test for the robustness of findings from one study under alternative study conditions or alternative analyses e.
Confounding effects may be less likely to occur and act similarly at multiple times and locations. Lastly, the relationship between the environmental variables that possibly confound the analysis and the measured parameters can be studied.
The information pertaining to environmental variables can then be used in site-specific models to identify residual variance that may be due to real effects. For example, if somebody wanted to study the cause of myocardial infarct and thinks that the age is a probable confounding variable, each year-old infarct patient will be matched with a healthy year-old "control" person.
In case-control studies, matched variables most often are the age and sex. Case-control studies are feasible only when it is easy to find controls, i. Suppose a case-control study attempts to find the cause of a given disease in a person who is 1 45 years old, 2 African-American, 3 from Alaska4 an avid football player, 5 vegetarian, and 6 working in education.
A theoretically perfect control would be a person who, in addition to not having the disease being investigated, matches all these characteristics and has no diseases that the patient does not also have—but finding such a control would be an enormous task.
A degree of matching is also possible and it is often done by only admitting certain age groups or a certain sex into the study population, creating a cohort of people who share similar characteristics and thus all cohorts are comparable in regard to the possible confounding variable.
For example, if age and sex are thought to be confounders, only 40 to 50 years old males would be involved in a cohort study that would assess the myocardial infarct risk in cohorts that either are physically active or inactive.
In cohort studies, the overexclusion of input data may lead researchers to define too narrowly the set of similarly situated persons for whom they claim the study to be useful, such that other persons to whom the causal relationship does in fact apply may lose the opportunity to benefit from the study's recommendations.
Similarly, "over-stratification" of input data within a study may reduce the sample size in a given stratum to the point where generalizations drawn by observing the members of that stratum alone are not statistically significant.
By preventing the participants from knowing if they are receiving treatment or not, the placebo effect should be the same for the control and treatment groups. By preventing the observers from knowing of their membership, there should be no bias from researchers treating the groups differently or from interpreting the outcomes differently. In the previous issue, we discussed aspects of statistical testing and precision to explore the question of whether an effect is real or due to chance.
This article takes a look at the potential for bias and its impact. Bias relates to systematic sources of error which need to be considered. The internal validity of a study depends greatly on the extent to which biases have been accounted for and necessary steps taken to diminish their impact. Bias may preclude finding a true effect; it may lead to an inaccurate estimate underestimate or overestimate of the true association between exposure and an outcome.
Significance testing in itself does not take into account factors which may bias study results.
Confounding Variable Examples
Bias can be divided into three general categories: For example, if you are researching whether a lack of exercise has an effect on weight gain, the lack of exercise is the independent variable and weight gain is the dependent variable. A confounding variable would be any other influence that has an effect on weight gain. Amount of food consumption is a confounding variable, a placebo is a confounding variable, or weather could be a confounding variable. Each may change the effect of the experiment design.
In order to reduce confounding variables, make sure all the confounding variables are identified in the study. Make a list of everything thought of, one by one, and consider whether those listed items might influence the outcome of the study.
Understanding the confounding variables will result in more accurate results.
Confounding Variable Examples
Examples of Confounding Variable: A mother's education Suppose a study is done to reveal whether bottle-feeding is related to an increase of diarrhea in infants. It would appear logical that the bottle-fed infants are more prone to diarrhea since water and bottles could easily get contaminated, or the milk could go bad.
However, the facts are that bottle-fed infants are less likely to get diarrhea than breast-fed infants.