Question

Discuss validity and reliability as "confounding variables" and how can they negatively impact the results of a study? What are confounding variables? Try to provide an example to illustrate your point.

Answer 1

First, let us define the concept of confounding variables. And then we will define validity and reliability in the context of confounding variables.

• What are confounding variables? Ans: In statistics, a confounding variable is a variable, other than the independent variables (that one is interested in) and the dependent variable, which may affect or influence both the dependent and independent variables. Thus confounding variables are extraneous variables (any variable that researchers are not deliberately studying in an experiment is an extraneous (or, outside) variable) being unaware of or failing to control for which may lead to erroneous conclusions about the relationship between the independent and dependent variables.
• Validity and reliability as "confounding variables"
  Ans: In this context, validity means internal validity. Internal validity is a measure which ensures that a researcher's experiment design closely follows the principle of cause and effect. Reliability refers to the extent that the experiment yields the consistent results over multiple trials.
  Let us have some examples to understand the effect of confounding variables on validity & reliability of an experiment. Confounding variables are like extra independent variables that are having a hidden effect on your dependent variables (in some cases, on independent variables also). Confounding variables can cause two major problems: increasing variance & introducing bias. For example, if one is researching whether lack of exercise leads to weight gain, lack of exercise is the independent variable and weight gain is the dependent variable. Let’s say one tests 200 volunteers (100 men and 100 women). It is found that a lack of exercise indeed leads to weight gain. One confounding variable is how much people eat. It’s also possible that men eat more than women; this could also make sex a confounding variable. Nothing was mentioned about starting weight, occupation or age either. A poor study design like this could lead to bias. For example, if all of the women in the study were middle-aged, and all of the men were aged 16, age would have a direct effect on weight gain. That makes age a confounding variable. Thus through this example, we start to question the reliability of the experiment.

  For example, a research group might design a study to determine if heavy drinkers die at a younger age. They proceed to design a study and set about gathering data. Their results, and a battery of statistical tests, indeed show that people who drink excessively are likely to die younger. Unfortunately, when the researchers gather data from their subjects’ non-drinking peers, they discover that they, too, die earlier than average. Maybe there is another factor, not measured, that influences both drinking and longevity? The weakness in the experimental design was that they failed to take into account confounding variables, and did not try to eliminate or control any other factors. Imagine that in this case, there is, in fact, no relationship between drinking and longevity. But there may be other variables which bring about both heavy drinking and decreased longevity. If they are unaware of these variables, the researchers may assume that heavy drinking is causing reduced longevity, i.e. they’ll make what’s called a “spurious association.” In reality, decreased longevity may be better explained by a third, confounding variable. For example, it is quite possible that the heaviest drinkers hailed from a different background or social group. This group might be, for unrelated reasons, shorter-lived than other groups. Heavy drinkers may be more likely to smoke or eat junk food, all of which could be factors in reducing longevity. In any case, it is the fact they belong to this group that is responsible for their decreased longevity and not heavy drinking. Hence this example tells us to question the validity of the experiment due to the presence of the confounding variables.