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 \phi_c = \sqrt{ \frac{\chi^2}{N(k - 1)}} 

Cramér's V (φc)

In statistics, Cramér's V (sometimes referred to as Cramér's phi or Cramers C and denoted as φc) is a popular[citation needed] measure of association between two nominal variables, giving a value between 0 and +1 (inclusive). It is based on Pearson's chi-squared statistic and was published by Harald Cramér in 1946.[1]

Usage and interpretationEdit

φc is the intercorrelation of two discrete variables[2] and may be used with variables having two or more levels. φc is a symmetrical measure, it does not matter which variable we place in the columns and which in the rows. Also, the order of rows/columns doesn't matter, so φc may be used with nominal data types or higher (ordered, numerical, etc)

Cramér's V may also be applied to goodness of fit chi-squared models when there is a 1×k table (e.g: r=1). In this case k is taken as the number of optional outcomes and it functions as a measure of tendency towards a single outcome.

Cramér's V varies from 0 (corresponding to no association between the variables) to 1 (complete association) and can reach 1 only when the two variables are equal to each other.

φc2 is the mean square canonical correlation between the variables[citation needed].

In the case of a 2×2 contingency table Cramér's V is equal to the Phi coefficient.

Note that as chi-squared values tend to increase with the number of cells, the greater the difference between r (rows) and c (columns), the more likely φc will tend to 1 without strong evidence of a meaningful correlation.[citation needed]


Cramér's V is computed by taking the square root of the chi-squared statistic divided by the sample size and the length of the minimum dimension (k is the smaller of the number of rows r or columns c).

The formula for the φc coefficient is:

 \phi_c = \sqrt{\frac{\varphi^2}{(k-1)}} = \sqrt{ \frac{\chi^2}{N(k - 1)}} 


The p-value for the significance of φc is the same one that is calculated using the Pearson's chi-squared test [citation needed].

The formula for the variance of φc is known.[3]

Unlike the contingency tablethe value of V is relatively independent of the number of columns or rows.

See alsoEdit

Other measures of correlation for nominal data:

Other related articles:


  1. Cramér, Harald. 1946. Mathematical Methods of Statistics. Princeton: Princeton University Press, p282. ISBN 0-691-08004-6
  2. Sheskin, David J. (1997). Handbook of Parametric and Nonparametric Statistical Procedures. Boca Raton, Fl: CRC Press.
  3. Liebetrau, Albert M. (1983). Measures of association. Newbury Park, CA: Sage Publications. Quantitative Applications in the Social Sciences Series No. 32. (pages 15–16)
  • Cramér, H. (1999). Mathematical Methods of Statistics, Princeton University Press

External linksEdit

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