Consider a number of notices or orders of rank provided by M judges or interviewees on subjects or products n. In this context, we propose a new unit of general measurement (agreement). This ratio, when applied to ranking data, is reduced to Kendalls (1948) the measure of compliance. A new ranking code, which depends on the Kendall average for rank correlation, is also proposed and is also a special case of our overall ratio. This particular classification measure can be seen as an alternative to Friedman`s well-known statistic for the analysis of two-sided variance. The general measure is also compared to another measure proposed by Lin (1989). Reports with the intraclassifific correlation coefficient of the rating data are presented. Some distribution results are also presented. The proposed matching measures serve as the basis for examining the agreement between two or more methods, instruments or respondents in biometric research or market surveys. For example, if one variable is the identity of a college basketball program and another variable is the identity of a college football program, one could test a relationship between the survey rankings of the two types of program: do colleges with a high-level basketball program tend to have a high-level football program? A rank correlation coefficient can measure this relationship, and measuring the significance of the rank correlation coefficient can indicate whether the measured relationship is small enough to be likely a coincidence. Comparing the alternative rankings of a group of elements is a general and frequent task in applied statistics. Predictive variables are categorized according to the size of the association with a result, predictive models classify subjects based on the personalized risk of an event, and genetic studies evaluate genes based on their difference in gene expression rates. We propose a sequential ranking agreement to quantify the rank agreement between two or more classified lists.

This measure has an intuitive interpretation, it can be applied to any number of lists, even if some are partially incomplete, and contains information about the agreement along the lists. The sequential ranking agreement can be analyzed or compared graphically to a permutation-based reference rate to identify changes in list agreements. The usefulness of this measure is illustrated by genetic classifications and data from two Danish studies on ovarian cancer, in which we assess the consistency of different methods of statistical classification within and between different methods of statistical classification. The maximum value for correlation is r-1, which means that 100% of pairs prefer the hypothesis. A correlation of r-0 indicates that half of the pairs favour the hypothesis and half do not; In other words, the sample groups do not differ in the ranks, so there is no evidence that they come from two different populations. A size of effect of r-0 cannot be considered a relationship between group affiliation and membership ranks.