Assumption #1: Your two variables should be measured at a continuous level (i.e., they are interval or ratio variables) or at an ordinal level.

If this assumption is not met, there is likely to be a different statistical test that you can use instead.

However, you should check whether your study meets this assumption before moving on. You cannot test the first of these assumptions with Minitab because it relates to your study design and choice of variables. Spearman's correlation has two assumptions. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a Spearman's correlation to give you a valid result. In this guide, we show you how to carry out a Spearman's correlation using Minitab, as well as interpret and report the results from this test. If there was a strong, positive association, we could say that the longer the length of unemployment, the greater the level of depression. Alternately, you could use a Spearman's correlation to understand whether there is an association between length of unemployment and depression (i.e., your two variables would be 'length of unemployment', measured in days, and 'depression', measured using a continuous scale). If there was a moderate, negative association, we could say that more time spent training was associated with better running performance (because the 10 km was covered more quickly). Spearman's correlation coefficient is often denoted by the symbol r s (or the Greek letter ρ, pronounced rho).įor example, you could use a Spearman's correlation to understand whether there is an association between running performance and time spent training (i.e., your two variables would be 'running performance', measured in time taken to run 10 km, and 'time spent training', measured in hours per week). It is often considered the nonparametric alternative to Pearson's correlation and can be run when there are violations of normality, a non-linear relationship or ordinal variables (such that Pearson's correlation cannot be used). The Spearman rank-order correlation coefficient (shortened to Spearman’s correlation in Minitab) is a test which measures the strength and direction of association between two variables that are measured on an ordinal or continuous scale. Spearman's correlation using Minitab Introduction

P value minitab