| Module 1: Producing Data
3. Sources of data > 3.2 Generating your own data > 3.2.3. Generating data from experimentation
iii. Influence of extraneous or confounding variables
A confounding variable is another variable whose effect on the response variable cannot be separated from the explanatory variable under study.
To examine the effect that confounding variables can have on data, examine the table below that comes from the same study into anaesthetics reported in the previous scenario.
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Which of the following variables could confound or influence the results shown in the table above?
- The age of the patients
- The type of anaesthetic used
- The sex of the patients
- The general health / physical condition of the patients
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Confounding variables in clinical trials
The placebo effect
Confounding variables can be very important in clinical trials where a new drug or procedure is being tested. Let’s say a doctor gives a patient a new drug in tablet form and the patient gets better. How can you tell whether it was
1) the attention that was given to the patient as the drug was administered, or
2) the drug itself that caused the improvement?
Many patients respond positively to any treatment, even when they are given a placebo, i.e. a dummy medication. In other words, it is the process of being treated, not the action of the drug, which produces patient improvement. As a result, it becomes important to separate the drug (explanatory variable) from the treatment (confounding variable). An improvement in a person's health that occurs when they are given a dummy medication is called the placebo effect.
Observer bias
Another effect that can confound the results of an experiment is related to the expectations of the tester. If the doctor administering the treatment knows which patients are being given the drug and which are being given the placebo, the doctor might note improvements for those patients on the drug and no improvement for those on the placebo. This is called observer bias.
Identify the explanatory variable and the most likely confounding variable in the following scenarios.
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Identify the explanatory variable in this example.
- Having a cold or not
- Adult women
- General health of women
- Prevalence of cold viruses
- Taking Echinacea capsule
- The time of year
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| Test your knowledge
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Identify any confounding variables.
- Having a cold or not
- Adult women
- General health of women
- Prevalence of cold viruses
- Taking Echinacea capsule
- The time of year
| | Answer
Click here for answers |
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Identify the explanatory variable in this example.
- Engineers
- Level of schooling
- Income
- Age of engineers
- Working environment
| | Answer
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| Test your knowledge
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Identify the explanatory variable in this example.
- Engineers
- Level of schooling
- Income
- Age of engineers
- Working environment
| | Answer
Click here for answers |
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