A confounding variable is an extraneous variable in a statistical model that correlates with both the dependent variable and the independent variable. A perceived relationship between an independent variable and a dependent variable that has been misestimated due to the failure to account for a confounding facto is termed a spurious relationship and the presence of misestimating for this reason is termed omitted-variable bias.
An example of confounding variables is as followed: suppose that there is a statistical relationship between ice-cream consumption and number of drowning deaths for a given period. These two variables have a positive correlation with each other. An evaluator might attempt to explain this correlation by inferring a causal relationship between the two variables.
Confounding variables are categorised according to their source. The choice of measurement instrument, situational characteristics or inter-individual differences as followed: an operational confound, a procedural confound an a person confound. An operational confound can occur in both experimental and non-experimental research designs. It occurs when a measure designed to assess a particular construct inadvertently measures something else as well. A procedural confound can occur in a laboratory experiment or a quasi-experiment. It occurs when a researcher mistakenly allows another variable to change along with the manipulated independent variable. A person confound occurs when two or more groups of units are analyzed together, despite varying according to one or more other characteristics.