Why rank correlation

The characteristics of both variables are carried over to the ranking positions. This chapter explains in detail the procedure of the rank correlation based on the following question:. Is there a correlation between public expenditures on education in and adult literacy rates in different countries in ? The literature summarizes the procedure of the rank correlation in three steps, which are described in the following section.

The question is examined based on a dataset from Caramani that captures the situation in countries. The answer to the question can be found with the help of a model, which in this case looks as follows:.

One of the two variables Adult literacy rate is not normal distributed. This section explains how to calculate the correlation coefficient. Table 1 shows the example data as raw data that has been ranked:. Some advantages of the rank correlation are The rank correlation is always in the interval [-1, 1].

For "tame" data, the Spearman and Pearson correlations are close to each other. The rank correlation is robust to outliers. Therefore for any third variable Z, the rank correlation between X and Z is the same as the rank correlation between Y and Z. The rank correlation can be used for any ordinal variable. Tags Data Analysis Getting Started. Thomas Muasya on December 15, am. This is my main interest Reply. Rick Wicklin on December 15, am.

Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. A positive correlation indicates the extent to which those variables increase or decrease […]. A positive correlation indicates the extent to which those variables increase or decrease in parallel. A negative correlation indicates the extent to which one variable increases as the other decreases.

In our example this is Multiplying this by 6 gives Now for the bottom line of the equation. The value n is the number of sites at which you took measurements. This, in our example is The R s value of A further technique is now required to test the significance of the relationship. This is the number of pairs in your sample minus 2 n In the example it is 8 10 - 2.

Now plot your result on the table.