Style control - access keys in brackets
1.3.4 Explanatory and response variables
Consider the following question from page (1) for the county data set:
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(1)
Is federal spending, on average, higher or lower in counties with high rates of poverty?
If we suspect poverty might affect spending in a county, then poverty is the explanatory variable and federal spending is the response variable in the relationship.66Sometimes the explanatory variable is called the independent variable and the response variable is called the dependent variable. However, this becomes confusing since a pair of variables might be independent or dependent, so we avoid this language. If there are many variables, it may be possible to consider a number of them as explanatory variables.
TIP: Explanatory and response variables
To identify the explanatory variable in a pair of variables, identify which of the two is suspected
of affecting the other and plan an appropriate analysis.
Caution: association does not imply causation
Labelling variables as explanatory and
response does not guarantee the relationship between the two is actually causal, even if
there is an association identified between the two variables. We use these labels only to keep
track of which variable we suspect affects the other.
In some cases, there is no explanatory or response variable. Consider the following question from page (2):
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(2)
If homeownership is lower than the national average in one county, will the percent of multi-unit structures in that county likely be above or below the national average?
It is difficult to decide which of these variables should be considered the explanatory and response variable, i.e. the direction is ambiguous, so no explanatory or response labels are suggested here.