Minimization of Confounding in research in medical sciences

Abstract

Confounding variables are variables that the researcher failed to control, or eliminate, damaging the internal validity of an experiment. A confounding variable, also known as a third variable or a mediator variable, can adversely affect the relation between the independent variable and dependent variable. This may cause the researcher to analyze the results incorrectly. The results may show a false correlation between the dependent and independent variables, leading to an incorrect rejection of the null hypothesis. 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 series of statistical tests, indeed show that people who drink excessively are likely to die younger. Unfortunately, when the researchers do a crosscheck with their peers, the results are ripped apart, because their peers live just as long - maybe there is another factor, not measured, that influences both drinking and living age? In many fields of science, it is difficult to remove entirely all of the variables, especially outside the controlled conditions of a lab. A well-planned experimental design, and constant checks, will filter out the worst confounding variables. For example, randomizing groups, utilizing strict controls, and sound operationalization practice all contribute to eliminating potential third variables. In this work we explore types of confounding and how to control them.