A measure of association between two ordinal variables, based on the ranks of the data values. For example, you analyze customer satisfaction for a car dealership that offers three levels of ongoing service for new cars: no service, standard service, and premium service. You take a random sample of customers and ask them whether they are unsatisfied, neutral, or satisfied with customer service. Your data consists of two ordinal variables: service package and customer satisfaction. You want to determine whether an association exists between the level of service customers receive and their overall satisfaction. You enter the data in this two-way table:
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Service plan and customer satisfaction |
|||
|
|
No service |
Standard service |
Premium service |
|
Satisfied |
39 |
105 |
171 |
|
Neutral |
99 |
91 |
93 |
|
Unsatisfied |
162 |
104 |
36 |
Spearman's rho for this table is 0.424, so you conclude there is a positive association between level of service and customer satisfaction: customers who choose a higher service plan tend to express more satisfaction with this business.
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Note |
Strong correlations and associations do not necessarily imply causation; only a properly designed experiment can prove causation. For example, this analysis does not prove that choosing a higher service plan causes greater customer satisfaction, but you can use this analysis to justify further investigation into the association. |
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