Pearson vs. Spearman correlation: Understanding the key differences and when to use each

Choosing between Pearson vs. Spearman correlation means choosing how you model the relationship between two variables. The right choice helps you align your data, assumptions, and research questions so your findings remain reliable.

We’ll take a look at what each technique involves, when each should be used, and the types of research questions that could be addressed. Also, if you are conducting usage and attitudes (U&A) research or concept testing, we can perform the analysis for you.

Correlational analysis is a bivariate (two variable) statistical procedure that sets out to identify the mean value of the product of the standard scores of matched pairs of observations.

In survey research, correlation helps you see whether changes in one measure tend to line up with changes in another.

Correlation on its own never proves causation; even a very strong correlation does not tell you which variable drives the change. When you use correlation in survey data, you move from individual answers to patterns in the whole dataset, which is why it often appears in performing correlation analysis with survey responses.

Pearson correlation measures the strength of a linear relationship between two variables—which can be used to find out if there is strong association between one variable versus another.

Imagine you have two variables—such as employee engagement and employee salaries​​—plotted on a simple scatter plot graph. The Pearson correlation essentially tries to utilize a scatter plot by drawing a line through the data in order to find out whether the two compariables are covary with one another and to what extent.

That is, Pearson correlation coefficient identifies whether:

Insight into this relationship is a first step in understanding how variables of interest might relate to one another, and could also prompt further causal investigation.

The Pearson correlation coefficient, r, is calculated with this expression:

Where:

The result always falls between –1 and +1. The sign shows direction (positive or negative), and the magnitude shows how tightly points cluster around a straight line.

Use Pearson correlation when your goal is to measure a linear relationship between two continuous variables under fairly standard statistical assumptions:

The Pearson correlation coefficient test compares the mean value of the product of the standard scores of matched pairs of observations. Once performed, it yields a number that can range from -1 to +1.

Positive figures are indicative of a positive correlation between the two variables, while negative values indicate a negative relationship. Furthermore, the value of r represents the strength of the relationship. A Pearson’s r that is near the value of 1 is suggestive of a stronger relationship between the two variables. 

As a rule of thumb, the following values can be used to determine the strength of the relationship:

Example

Imagine that you’ve developed some marketing concepts that you’ve begun testing with some potential customers. For each concept, you’re interested in learning whether evaluations of the appeal of the concept are associated with stronger intent to purchase.

Comparisons of Concepts A, B and C yield Pearson correlation coefficients of .3 .6 and .9 respectively. Based on these three figures, you can infer the following:

Using these inferences, you might decide that Concept C is the most appropriate concept to employ in your next marketing campaign.

Pearson correlation is a workhorse for continuous survey metrics and KPIs. Examples include:

In each case, Pearson correlation gives an at-a-glance sense of whether higher scores on one variable are associated with higher (or lower) scores on another, under a linear relationship model.

The Spearman correlation test examines whether two variables are correlated with one another or not.

The Spearman’s test can be used to analyse ordinal level, as well as continuous level data, because it uses ranks instead of assumptions of normality. This makes the Spearman correlation great for 3, 5, and 7-point Likert scale questions or ordinal survey questions.

Spearman’s ρ is computed by:

For data without many ties, Spearman’s rho is often written as:

Where:

Spearman correlation is helpful when the data or relationship does not fit Pearson’s assumptions:

Spearman helps you measure relationships even when the data doesn’t follow typical statistical assumptions.

The Spearman’s rank-order tests determine the strength and direction of the monotonic relationship between two variables measured at ordinal, interval or ratio level.

As with the Pearson equivalent, the test will yield a figure of between -1 and +1, and the closer the figure is to 1, the stronger the monotonic relationship. As a rule of thumb, you can use the following figures to determine the effect size:

In addition, probability values should be used to determine statistical significance.

Example

Imagine you’ve gathered some data on evaluations of a market concept, and the average price a consumer is willing to pay. Take the following steps:

Finally, square the differences (d²) and then sum them.

In our example, we would first multiply the sum of the d² values (6) by 6 (i.e. 36). To address the denominator, we would raise the number of observations (concepts) to the power of 3 and then subtract the number of observations (i.e. 4³-4 = 60). We can then calculate Spearman’s rho as 1-36/60= -.058.

This indicates a moderate, negative monotonic correlation between concept evaluation and the price that consumers are willing to pay. A difficult one to interpret!

Spearman correlation shines when you’re working with ordinal scales, ranks, or skewed distributions. Common uses include:

Because it relies on ranks, Spearman correlation stays more stable when a few outliers or oddly spaced responses would distort a linear correlation.

This is the core Pearson vs. Spearman correlation question: both coefficients measure association between two variables, but they rely on different assumptions and highlight different patterns.

The table below summarizes the difference between Pearson and Spearman correlation at a glance:

In practice, analysts often calculate both Pearson and Spearman correlations when working with mixed data types or when they want to check how much linear and monotonic perspectives agree.

So, that’s correlation in a nutshell, and how and when to use it. It can be complicated, but the good news is that if you’re planning on a usage and attitudes (U&A) survey or performing some concept testing, we can handle the correlation analysis for you through our Key Driver Analysis feature.