Correlation coefficient

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A correlation coefficient is a

distribution.[citation needed
]

Several types of correlation coefficient exist, each with their own definition and own range of usability and characteristics. They all assume values in the range from −1 to +1, where ±1 indicates the strongest possible correlation and 0 indicates no correlation.

causal relationship between the variables (for more, see Correlation does not imply causation).[3]

Types

There are several different measures for the degree of correlation in data, depending on the kind of data: principally whether the data is a measurement,

categorical
.

Pearson

The

Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, is a measure of the strength and direction of the linear relationship between two variables that is defined as the covariance of the variables divided by the product of their standard deviations.[4]
This is the best-known and most commonly used type of correlation coefficient. When the term "correlation coefficient" is used without further qualification, it usually refers to the Pearson product-moment correlation coefficient.

Intra-class

Intraclass correlation (ICC) is a descriptive statistic that can be used, when quantitative measurements are made on units that are organized into groups; it describes how strongly units in the same group resemble each other.

Rank

Rank correlation is a measure of the relationship between the rankings of two variables, or two rankings of the same variable:

  • Spearman's rank correlation coefficient is a measure of how well the relationship between two variables can be described by a monotonic function.
  • The
    Kendall tau rank correlation coefficient
    is a measure of the portion of ranks that match between two data sets.
  • Goodman and Kruskal's gamma is a measure of the strength of association of the cross tabulated data when both variables are measured at the ordinal level.

Tetrachoric and polychoric

The polychoric correlation coefficient measures association between two ordered-categorical variables. It's technically defined as the estimate of the Pearson correlation coefficient one would obtain if:

  1. The two variables were measured on a continuous scale, instead of as ordered-category variables.
  2. The two continuous variables followed a bivariate normal distribution.

When both variables are

dichotomous instead of ordered-categorical, the polychoric correlation
coefficient is called the tetrachoric correlation coefficient.

See also

Notes

  1. ^ Correlation coefficient: A statistic used to show how the scores from one measure relate to scores on a second measure for the same group of individuals. A high value (approaching +1.00) is a strong direct relationship, values near 0.50 are considered moderate and values below 0.30 are considered to show weak relationship. A low negative value (approaching -1.00) is similarly a strong inverse relationship, and values near 0.00 indicate little, if any, relationship.[1]

References

  1. ^ "correlation coefficient". NCME.org. National Council on Measurement in Education. Archived from the original on July 22, 2017. Retrieved April 17, 2014.
  2. ISBN 0-935702-75-X. Archived from the original
    (PDF) on 15 February 2019. Retrieved 14 February 2019.
  3. ISBN 978-0-470-74664-6
    .
  4. ^ Weisstein, Eric W. "Statistical Correlation". mathworld.wolfram.com. Retrieved 2020-08-22.
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