Goodman and Kruskal's lambda

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In probability theory and statistics, Goodman & Kruskal's lambda (\lambda) is a measure of proportional reduction in error in cross tabulation analysis. For any sample with a nominal independent variable and dependent variable (or ones that can be treated nominally), it indicates the extent to which the modal categories and frequencies for each value of the independent variable differ from the overall modal category and frequency, i.e. for all values of the independent variable together. \lambda can be calculated with the equation

\lambda = \frac{\varepsilon_1 - \varepsilon_2}{\varepsilon_1}.

where

\varepsilon_1 is the overall non-modal frequency, and
\varepsilon_2 is the sum of the non-modal frequencies for each value of the independent variable.

Values for lambda range from zero (no association between independent and dependent variables) to one (perfect association).

Weaknesses[edit]

Although Goodman and Kruskal's lambda is used to calculate association between variables, it yields a value of 0 (no association) whenever two variables are in accord—that is, when the modal category is the same for all values of the independent variable, even if the modal frequencies or percentages vary. Consider the table below, which describes a fictitious sample of 350 individuals, categorized by relationship status and blood pressure.

Relationship Status and Blood Pressure (fictitious)
Relationship Status Total
Unmarried Married
Blood Pressure Normal 80%
(120)
51%
(102)
63.4%
(222)
High 20%
(30)
49%
(98)
36.6%
(128)
Total 42.9%
(150)
57.1%
(200)
100%
(350)

For this sample,

\lambda = \frac{128 - (30 + 98)}{128} = 0

even though the data demonstrate a pronounced relationship between the independent and dependent variables.

See also[edit]

References[edit]