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- For homogeneity of variance see homoscedasticity.

In statistics, homogeneity arises in describing the properties of a dataset, or several datasets, and relates to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part. In meta-analysis, which combines the data from several studies, homogeneity measures the differences or similarities between the several studies.

Homogeneity can be studied to several degrees of complexity. For example, considerations of homoscedasticity examine how much the variability of data-values changes throughout a dataset. However, questions of homogeneity apply to all aspects of the statistical distributions, including the location parameter. Thus, a more detailed study would examine changes to the whole of the marginal distribution. An intermediate-level study might move from looking at the variability to studying changes in the skewness. In addition to these, quesions of homogeneity apply also to the joint distributions.

The concept of homogeneity can be applied in many different ways and, for certain types of statistical analysis, it is used to look for further properties that might need to be treated as varying within a dataset once some initial types of non-homogeneity have been dealt with.

- Hall, M.J. (2003) The interpretation of non-homogeneous hydrometeorological time series a case study. Meteorological Applications, 10, 61–67. (doi:10.1017/S1350482703005061 )
- Krus, D.J., & Blackman, H.S. (1988).Test reliability and homogeneity from perspective of the ordinal test theory. Applied Measurement in Education, 1, 79–88 (Request reprint).
- Loevinger, J. (1948). The technic of homogeneous tests compared with some aspects of scale analysis and factor analysis. Psychological Bulletin, 45, 507–529.

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Last updated on Saturday September 20, 2008 at 22:14:34 PDT (GMT -0700)

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