Confounding variables (aka third 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 rejectionof 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 battery 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?
For example, it is quite possible that the heaviest drinkers hailed from a different background or social group. Heavy drinkers may be more likely to smoke, or eat junk food, all of which could be factors in reducing longevity. A third variable may have adversely influenced the results.
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.
After a research, when the results are discussed and assessed, by a group of peers, this is the area that stimulates the most heated debate. When you read stories of different foods making you die young, or hear claims about the next super-food, assess these findings carefully.
Many media outlets jump upon sensational results, but never pay any regard to the possibility of confounding variables.
The principle is closely related to the problem of correlation and causation.
For example, a scientist performs statistical tests, sees a correlation and incorrectly announces that there is a causal link between two variables.
Constant monitoring, before, during and after an experiment, is the only way to ensure that any confounding variables are eliminated.
Statistical tests, whilst excellent for detecting correlations, can be almost too accurate.
Human judgment is always needed to eliminate any underlying problems, ensuring that researchers do not jump to conclusions.
Want the full version to study at home, take to school or just scribble on?
Whether you are an academic novice, or you simply want to brush up your skills, this book will take your academic writing skills to the next level.