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The confounding variable makes the results of the analysis unreliable. It is quite likely that we are just measuring the fact that highway driving results in better fuel economy than city driving. In statistics terms, the make of the truck is the independent variable, the fuel economy (MPG) is the dependent variable and the amount of city ...
Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...
The phenomenon may disappear or even reverse if the data is stratified differently or if different confounding variables are considered. Simpson's example actually highlighted a phenomenon called noncollapsibility, [32] which occurs when subgroups with high proportions do not make simple averages when combined. This suggests that the paradox ...
Quasi-experimental estimates of impact are subject to contamination by confounding variables. [1] In the example above, a variation in the children's response to spanking is plausibly influenced by factors that cannot be easily measured and controlled, for example the child's intrinsic wildness or the parent's irritability.
Propensity scores are used to reduce confounding by equating groups based on these covariates. Suppose that we have a binary treatment indicator Z, a response variable r, and background observed covariates X. The propensity score is defined as the conditional probability of treatment given background variables:
All of those examples deal with a lurking variable, which is simply a hidden third variable that affects both of the variables observed to be correlated. That third variable is also known as a confounding variable, with the slight difference that confounding variables need not be hidden and may thus be corrected for in an analysis. Note that ...
Researchers could, for example, simply compare exposed vs. nonexposed groups, or they could take into account the number of family members who smoked, years of marriage to a smoker, or the level ...
Using the example of general happiness, a researcher could create an inventory where there is a very high positive correlation between general happiness and contentment, but if there is also a significant positive correlation between happiness and depression, then the measure's construct validity is called into question.