You are viewing documentation for the old version 2.20 of Analyse-it. If you are using version 3.00 or later we recommend you go to the Spearman rank correlation.

Spearman correlation

This procedure is available in both the Analyse-it Standard and the Analyse-it Method Evaluation edition

Spearman correlation is a non-parametric test to determine the degree of correlation (association) between two variables.

The requirements of the test are:

  • Two variables measured on an ordinal or continuous scale.

Arranging the dataset

Data in existing Excel worksheets can be used and should be arranged in a List dataset layout. The dataset must contain two ordinal or continuous scale variables.   

When entering new data we recommend using New Dataset to create a new 2 variables dataset ready for data entry.

Using the test

To start the test:

  1. Excel 2007:
    Select any cell in the range containing the dataset to analyse, then click Correlation on the Analyse-it tab, then click Spearman
  2. Excel 97, 2000, 2002 & 2003:
    Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Correlation then click Spearman.

  3. Click Variable X and Variable Y and select the variables.
  4. Click Alternative hypothesis and select the alternative hypothesis to test.
  5. rs ≠ 0 to test if the variables are correlated.
    rs > 0 to test if the samples are positively correlated, where observations of the variables tend to increase together.
    rs < 0 to test if the samples are negatively correlated, where observations of one variable tend to increase as observations in the other variable decrease.
  6. Enter Confidence interval to compute around the Spearman rs statistic. The level should be entered as a percentage between 50 and 100, without the % sign.
  7. Click OK to run the test.

The report shows the number of observations analysed, and, if applicable, how many missing cases were pairwise deleted.

The Spearman rs correlation statistic and confidence interval are shown. 

METHOD The confidence interval is calculated using the Fisher's Normal transformation (see [1] or [2]).

The hypothesis test is shown. The p-value is the probability of rejecting the null hypothesis, that the variables are independent, when it is in fact true. A significant p-value implies that the two variables are correlated.

METHOD The p-value is calculated using the t- approximation (see [1]). For small sample sizes ≤ 30 exact tables should be used (see [2]), or use the Kendall correlation which calculates exact p-values for small samples. 

When both variables are continuous scale the scatter plot (see below) shows a visual assessment of the strength of association.

Further reading & references

  1. Handbook of Parametric and Nonparametric Statistical Procedures (3rd edition)
    David J. Sheskin, ISBN 1-58488-440-1 2003; 1016.
  2. Practical Non-parametric Statistics (3rd edition)
    Conover W.J. ISBN 0-471-16068-7 1999; 314.