confounding variable

(noun)

an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable

Related Terms

Examples of confounding variable in the following topics:

  • Confounding

    • A confounding variable is an extraneous variable in a statistical model that correlates with both the dependent variable and the independent variable.
    • A confounding variable is an extraneous variable in a statistical model that correlates (positively or negatively) 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 factor is termed a spurious relationship, and the presence of misestimation for this reason is termed omitted-variable bias.
    • Situational characteristics (procedural confound) - This type of confound occurs when the researcher mistakenly allows another variable to change along with the manipulated independent variable.
    • Break down why confounding variables may lead to bias and spurious relationships and what can be done to avoid these phenomenons.

  • Sex Bias in Graduate Admissions

    • The Berkeley study is one of the best known real life examples of an experiment suffering from a confounding variable.
    • The above study is one of the best known real life examples of an experiment suffering from a confounding variable.
    • Once we extract these relationships we can test algorithmically whether a given partition, representing confounding variables, gives the correct answer.
    • One of the best real life examples of the presence of confounding variables occurred in a study regarding sex bias in graduate admissions here, at the University of California, Berkeley.
    • Illustrate how the phenomenon of confounding can be seen in practice via Simpson's Paradox.

  • Observational studies

    • Sun exposure is what is called a confounding variable, which is a variable that is correlated with both the explanatory and response variables.
    • While one method to justify making causal conclusions from observational studies is to exhaust the search for confounding variables, there is no guarantee that all confounding variables can be examined or measured.
    • In the same way, the county data set is an observational study with confounding variables, and its data cannot easily be used to make causal conclusions.
    • However, it is unreasonable to conclude that there is a causal relationship between the two variables.
    • Suggest one or more other variables that might explain the relationship visible in Figure 1.9.

  • Including and assessing many variables in a model

    • Sometimes there are underlying structures or relationships between predictor variables.
    • This would help us evaluate the relationship between a predictor variable and the outcome while controlling for the potential influence of other variables.
    • What does β4 , the coefficient of variable x4 (Wii wheels), represent?
    • That model was biased by the confounding variable wheels.
    • When we use both variables, this particular underlying and unintentional bias is reduced or eliminated (though bias from other confounding variables may still remain).

  • Introduction to Multiple Regression

    • Table 3 shows the partitioning of the sums of squares into the sum of squares uniquely explained by each predictor variable, the sum of squares confounded between the two predictor variables, and the sum of squares error.
    • It is clear from this table that most of the sum of squares explained is confounded between HSGPA and SAT.
    • The confounded sum of squares in this example is computed by subtracting the sum of squares uniquely attributable to the predictor variables from the sum of squares for the complete model: 12.96 - 3.21 - 0.32 = 9.43.
    • The variance explained by the set would include all the variance explained uniquely by the variables in the set as well as all the variance confounded among variables in the set.
    • It would not include variance confounded with variables outside the set.

  • Correlation and Causation

    • A positive correlation means that as one variable increases (e.g., ice cream consumption) the other variable also increases (e.g., crime).
    • Causation refers to a relationship between two (or more) variables where one variable causes the other.
    • change in the independent variable must precede change in the dependent variable in time
    • it must be shown that a different (third) variable is not causing the change in the two variables of interest (a.k.a., spurious correlation)
    • It is important to not confound a correlation with a cause/effect relationship.

  • Unequal Sample Sizes

    • Therefore, Diet and Exercise are completely confounded.
    • The problem with unequal n is that it causes confounding.
    • In short, weighted means ignore the effects of other variables (exercise in this example) and result in confounding; unweighted means control for the effect of other variables and therefore eliminate the confounding.
    • The second gets the sums of squares confounded between it and subsequent effects, but not confounded with the first effect, etc.
    • Data for Diet and Exercise with Partial Confounding Example

  • Controlling for a Variable

    • Controlling for a variable is a method to reduce the effect of extraneous variations that may also affect the value of the dependent variable.
    • For instance, temperature is a continuous variable, while the number of legs of an animal is a discrete variable.
    • There are also quasi-independent variables, which are used by researchers to group things without affecting the variable itself.
    • In a scientific experiment measuring the effect of one or more independent variables on a dependent variable, controlling for a variable is a method of reducing the confounding effect of variations in a third variable that may also affect the value of the dependent variable.
    • The failure to do so results in omitted-variable bias.

  • t-Test for Two Samples: Paired

    • A paired difference test uses additional information about the sample that is not present in an ordinary unpaired testing situation, either to increase the statistical power or to reduce the effects of confounders.
    • A paired-samples $t$-test based on a "matched-pairs sample" results from an unpaired sample that is subsequently used to form a paired sample, by using additional variables that were measured along with the variable of interest.
    • The matching is carried out by identifying pairs of values consisting of one observation from each of the two samples, where the pair is similar in terms of other measured variables.
    • This approach is sometimes used in observational studies to reduce or eliminate the effects of confounding factors.

  • Critical Evaluation

    • Causality: A relationship between two variables does not mean that one causes the other to occur.
    • They may both be related (correlated) because of their relationship through a different variable.
    • Confounding: When the effects of multiple factors on a response cannot be separated.
    • Confounding makes it difficult or impossible to draw valid conclusions about the effect of each factor.