Coefficient of Association: Definition, Types, Examples

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What is a Coefficient of Association?

A Coefficient of Association measures the strength of a relationship. “Association” means that the variables have shared or common elements or some degree of agreement.

A large number of different association coefficients is available. Which you choose is dependent on many factors, including the data type (e.g. Kendall’s Tau for ranked nominal variables or Yule’s Y for binary variables). That said, a coefficient of association is independent of its measurement scale.

These coefficients typically range between 0 and 1, where 0 is no relationship and 1 is a perfect relationship. However, some measures of association range from -1 to 1, where -1 indicates a perfect inverse relationship.

Coefficient of Association for Nominal Variables

Kendall’s Tau (Kendall Rank Correlation Coefficient) measures relationships between columns of ranked data.

  • Tau-A and Tau-B are usually used for square tables (with equal columns and rows).
  • Tau-B will adjust for tied ranks.
  • Tau-C is usually used for rectangular tables. For square tables, Tau-B and Tau-C are essentially the same.

Binary Variables

1. Coefficient of Colligation (Yule’s Y)

Coefficient of Colligation (Yule’s Y) ratio

Yules Y (Coefficient of Colligation) or, more simply, Y, can be used to approximate tetrachoric correlation (Warren’s, 2008); Tetrachoric correlation is used to measure rater agreement for binary data. Yule’s Y, a transformation of the odds ratio, is not used very often. One reason is that its used is generally restricted to 2×2 tables; In addition Digby’s (1983) coefficient H, is generally considered to be a better approximation.

Yule’s Q, Yule’s Y, and Digby’s H coefficients are part of a general family of coefficients which raise the odds ratio to a power (c) (Bonnett & Price, 2007).

  • Yule’s Q: c = 1
  • Yule’s Y: c = .5 (i.e. the square root of the OR)
  • Digby’s H = .75

2. Phi Coefficient of Association

The Phi Coefficient of association is used for contingency tables when:

  • At least one variable is a nominal variable.
  • Both variables are dichotomous variables.

Cramer’s V is a similar measure, used when tables are 3×3 or larger.

Related Measures

See also: Measures of Association.


Bonett, D.G. and Price, R.M, (2007) Statistical Inference for Generalized Yule Coefficients in 2 x 2 Contingency Tables. Sociological Methods and Research, 35, 429-446.
Digby, P.G.N. (1983). Approximating the tetrachoric correlation coefficient. Biometrics, 39, 753–757.
Warrens, M. (2008). On Association Coefficients for 2×2 Tables and Properties That Do Not Depend on the Marginal Distributions. Psychometrika. 2008 Dec; 73(4): 777–789. Published online 2008 Jul 23. doi: 10.1007/s11336-008-9070-3.
Yule, G.U. (1912). On the methods of measuring the association between two attributes. Journal of the Royal Statistical Society, 75, 579–652.

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