![]() The values from the Spearman’s test can range from -1 to +1 (negative one to positive one). The measures of values you obtain from this test will determine the strength of the monotonic relationship. If your scatter plot shows your data to look linear and monotonic, you can perform a Pearson’s Correlation test. In order to determine how strong of a monotonic relationship exists between the data of two variables and in what direction this relationship is, you need to perform a Spearman Rank-Order Correlation test. This will help give you a visual indication of if a monotonic relationship exists between the variables. ![]() In preparation for running a test of the size and direction of a monotonic relationship between two variables, you will first need to present all your raw data in the form of a scatter plot. Non-monotonic relationship Test of Monotonic Relationships The examples below are of a non-linear monotonic relationship, a linear monotonic relationship and a scatterplot of data that has a non monotonic relationship. This direct relationship can also be referred to as a positive correlation. In the scenario in which the independent variable increasing results in the dependent variable also increasing, this is known as a monotonic direct relationship. This inverse relationship can also be referred to as a negative correlation. When considering variables in a monotonic relationship, one must consider both independent variables and dependent variables.įor instance, a monotonic inverse relationship is said to occur when the independent variable increasing results in the dependent variable decreasing. That is to say that a monotonic relationship can be linear relationship such that the rate of increase or decreases of both variables is the same.Ī monotonic relationship can also be non-linear with an increase or decrease occurring at different rates between the two variables. ![]() The rate at which an increase or decrease occurs does not necessarily have to be the same for both variables. The term monotonic relationship is a statistical definition that is used to describe a scenario in which the size of one variable increases as the other variables also increases, or where the size of one variable increases as the other variable also decreases. ![]()
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