The 10 Most Common Mistakes with Spearman's Rank Correlation and How to Avoid Them

This blog identifies the ten most frequent errors researchers make when using Spearman's rank correlation and explains why they occur. It also provides practical strategies to avoid these mistakes and ensure accurate statistical analysis.

In research, not every relationship is straightforward, and that is where Spearman's rank correlation proves its value. Known for its non-parametric strength, it provides us with the opportunity to discover monotonic relationships that are overlooked by Pearson's approach. 

That accounts for its extensive application, from Spearman's correlation in psychology research and Spearman's rank correlation in medical studies, to classrooms where an example of Spearman's correlation in educational research makes abstract concepts understandable. However, the truth is that misuse is a common occurrence. 

As the experts often highlight, many students and researchers misinterpret correlation coefficients. They also confuse Spearman and Pearson methods, which can gradually undermine the credibility of their entire analysis. This blog, written by the experts at Fast Assignment Help, discusses the ten most common mistakes researchers are prone to and demonstrates how you can avoid them.

What is Spearman's Rank Correlation and Why It's Unique?

Spearman's rank correlation (also known as Spearman's rho) determines the degree to which two ranked variables co-vary. The non-parametric nature of Spearman's rank correlation means that it does not assume a normal distribution, which makes it applicable in many situations. 

A monotonic relationship implies that as one variable grows, the other always increases or decreases.

Reader Tip: Imagine a student’s class rank vs. marathon finish rank—if one goes up, does the other always go up or down too? That’s monotonic!

So, when do you use it? Consider survey data or ranked preferences. For instance, a student's class rank versus their marathon finish rank. This is the best method for ordinal data, such as Likert scales, or when the relationship is not strictly linear but does progress in a consistent direction. 

Scholars extensively use Spearman's correlation in psychology studies and Spearman's rank correlation in medical studies. An example in educational research is comparing ranks of performance across different subjects. Businesses also conduct business research using Spearman's rank correlation to compare customer satisfaction.

Applications

Spearman’s Correlation in Psychology Research

Psychologists use Spearman's correlation to study behaviours, test scores, or survey rankings. It helps reveal consistent trends in ordinal or non-normal data without violating statistical assumptions.

Spearman’s Rank Correlation in Medical Studies

In medical research, Spearman's rho helps explore associations between patient outcomes, treatment ranks, or symptom severity. Its robustness to non-normal data makes it ideal for clinical datasets.

Example of Spearman’s Correlation in Education Research

An example is comparing students' ranks across different subjects or tests. Spearman's correlation can reveal whether students who excel in one subject tend to excel in another.

Business Research Using Spearman’s Rank Correlation

Companies use Spearman's rho to compare customer satisfaction rankings, product preferences, or employee performance. It helps identify consistent patterns without assuming linear relationships.

Real-Life Applications of Spearman’s Rank Correlation

It can be applied to survey data, customer reviews, sports rankings, or any scenario where data is ordinal or ranked. It simplifies understanding trends in everyday decision-making.

Spearman’s Correlation in Dissertation Data Analysis

In dissertations, Spearman's rho is used to quantify associations between variables, report statistical significance, and provide context for ordinal or non-normal data. Full reporting (ρ, n, p-value) ensures clarity and replicability.

Spearman’s Rank Correlation in Social Sciences

Social scientists apply Spearman's correlation to analyse survey results, public opinion rankings, or behavioural trends. It allows for insights from non-linear and ranked datasets, which are common in social research.

The important question now is: what is the difference between Spearman and Pearson correlation?

If you're asking yourself when to use Spearman rather than Pearson, keep in mind that Spearman is best used if your data are ranked or non-normal. And once put into practice, the interpretation of positive Spearman correlation and negative values assists you in describing the direction of the relationship.

Spearman’s Rho Formula Explained

• Spearman’s rho (ρ) measures the strength and direction of a monotonic relationship between two ranked variables.
• Formula:

ρ = 1 – 6Σd² / [n(n²–1)]

d = difference between paired ranks

n = sample size

• Key point: Focuses on differences in ranks rather than raw data values.

Online Calculator for Spearman’s Rank Correlation

How To Report Spearman’s Rho in APA Style?

As illustrated in the example, most universities today require that students provide statistics and narratives in APA style. The results of the Spearman correlation indicated that the river gradient and the size of the bedload long-axis were strongly positively correlated (rs[27] = .558, p < .001). Sample size is noted by the figure after r in parentheses. 

Next, the p-value is indicated after the R-s correlation coefficient. Please note that we do not quote p = 0.000 when p-value < 0.001. The reason for this is that p-values are never exactly equal to zero. 

Get professional support through our assignment writing services, where experienced researchers ensure your results are well-reported and ready for submission.

How to Perform Spearman’s Correlation in SPSS Step by Step?

Click Analyse> Correlate > Bivariate... on the main menu as shown below:

Move the variables into the Variables box by dragging and dropping the variables, or by clicking on each variable and then clicking the button. 

Ensure that you clear the Pearson checkbox (it is checked by default in SPSS Statistics) and choose the Spearman checkbox in the –Correlation Coefficients– section. 

Press the OK button to generate the results. 

Spearman’s Rank Correlation in R Programming

• Use cor(x, y, method= "spearman") for the correlation coefficient only.
• Use cor—test (x, y, method= "spearman") to get both ρ and p-value.
• Works with vectors of numeric or ordinal data.

Spearman’s Correlation in Python (scipy, pandas)

Using Scripy: 

• From scipy.stats 
• import spearmanr

Pandas:

• rho, p = spearmanr(x, y)

How to Calculate Spearman’s Rho Step by Step

• Rank the values for both variables separately.
• Compute the difference (d) between the paired ranks.
• Square each difference (d²) and sum them (Σd²).
• Apply the formula: ρ = 1 – 6Σd² / [n(n²–1)], where n is the number of paired observations.
• Interpret the resulting rho according to its sign and magnitude.

The 10 Most Common Mistakes with Speaman’s Rank (And the Expert Solutions)

Even experienced researchers get confused when applying Spearman’s rank correlation. Small oversights can turn solid data into misleading conclusions. 

To make this guide practical, we’ll use a simple dataset: five students with class ranks (1–5) and marathon ranks (1–5), where a lower rank means better performance. This shared example will anchor each mistake in a clear, visual way.

Mistake #1: Ignoring the Monotonicity Assumption

• The Error: A common beginner’s mistake is thinking that Spearman's rank correlation can be applied to any relationship. In reality, it can only analyse monotonic ones, where variables trend in one direction, either up or down.
  
  If the data curve is in a U-shape or another form, Spearman's rho will not be able to detect it. Omitting this results in inaccurate conclusions, especially in Spearman's correlation in psychology research, where behavioural data can exhibit non-linear trends.
  
  
• The Fix: Always plot your dataset before computation. A scatterplot is the fastest method for observing whether the trend is monotonic. If the pattern is not linear, consider using alternative methods such as polynomial regression or Kendall's tau. This practice keeps you from abusing Spearman where it just does not belong.
  
  

Example: Suppose students consistently get better until Student D falls behind in both class and marathon performance. The trend initially rises, then falls. Spearman would write rho ≈ 0, indicating no association. However, there is one, and it's just not monotonic. That's why verifying monotonicity is crucial in real-life applications of Spearman's rank correlation.

Mistake #2: Confusing Spearman and Pearson Correlation

• The Error: Another common error is smudging the difference between Spearman and Pearson correlation. Some mistakenly believe Spearman is "Pearson for non-linear data." That's incorrect.
  
  Spearman is suitable for ordinal data and non-linear monotonic relationships, while Pearson is suited for linear, continuous, normally distributed data. Getting them confused can result in incorrect conclusions, particularly in business research using Spearman's rank correlation when both forms are prevalent.
  
  
• The Fix: Employ a straightforward decision tree. 

Ask: Is the data continuous, linear, and approximately normal? → Employ Pearson. 

Is the data ordinal, rank-based, or monotonic but not perfectly linear? → Employ Spearman. This clarity enables the use of the correct test for the corresponding dataset.

Example: Ranking class scores (ranked test marks) against GPA requires the use of the Pearson correlation coefficient. However, ranking marathon performance against ranked class standing demands Spearman's correlation. Being aware of when to use Spearman's correlation instead of Pearson's precludes one of the most common analytical blunders.

Mistake #3: Forgetting the Significance of Sample Size

• The Error: With tiny samples (e.g., less than 10 pairs), Spearman's rho can vary wildly. A single outlier can reverse the coefficient entirely, leaving conclusions unsteady. This is especially problematic in Spearman's correlation in psychology research, where tiny pilot tests are the norm.
  
  
• The Fix: When possible, have at least 20–30 pairs. Although not an absolute rule, larger samples make the interpretation of the correlation coefficient's scale more stable. If you have to use fewer, be honest about the constraints and don't over-generalise.
  
  
• Example: It may shift from 0.7 to 0.2 simply by virtue of one misplaced rank among five students. In contrast, Spearman's rank correlation is more robust and generalisable in medical studies, where data sets typically consist of several hundred patients.

Mistake #4: Misinterpreting Statistical Significance (p-values)

• The Error: Most think that a low p-value means a strong or significant relationship. This muddies statistical hypothesis testing with Spearman's rho, providing a practical interpretation. A significant p-value indicates that the correlation is unlikely to have occurred by chance, not that it's strong or meaningful.
  
  
• The Fix: Always give both the rho and the p-value, and then comment on practical significance. Rho = 0.1, for instance, can be statistically significant in vast datasets but is practically insignificant. The strength is in the coefficient value, not simply in the p-value.
  
  

Example: In Spearman's correlation in dissertation data analysis, you would report: "ρ = 0.12, n = 600, p < .01." Statistically significant? Sure. Useful in practice? Likely not. This point of clarification is important when instructing students on how to understand research results.

Mistake #5: Failing to Check for Tied Ranks

• The Error: Tied ranks, such as those found in questionnaires where several participants score the same point on the Likert scale, are inevitable. Left uncorrected, they can create a distorted rho. Discarding ties misrepresents relationships in ranking data for Spearman's correlation.
  
  
• The Fix: Contemporary tools, such as Spearman's correlation in SPSS or Spearman's rank correlation in R programming, deal with ties by assigning average ranks. Always specify in your methodology that ties were adjusted, particularly if manual calculations are involved in your work.
  
  

Example: When two students are tied for third place, giving them ranks "3 and 4" rather than both "3.5" alters rho inappropriately. An example of Spearman's correlation in educational research, these kinds of adjustments can have substantial effects on findings.

Mistake #6: Not Reporting the Full Results

• The Error: Some reports only go so far as to report rho and overlook important information, such as the p-value or sample size. Readers cannot assess the validity or significance of these without them.
• The Fix: Always report rho, n, and p. Adhere to reporting conventions, for example, how to report Spearman's rho in APA style. A good statement would be: "ρ = 0.52, n = 45, p < .01."
  
  

Example: If you report "ρ = 0.52," readers don't have a clue if the sample was 10 or 1000. Full reporting is demanded in Spearman's correlation in SPSS step-by-step tutorials, journal articles, and dissertations.

Mistake #7: Mislabeling or Incorrectly Interpreting the Direction

• The Error: Most people equate negative correlation with weakness. However, rho = –0.85 indicates a strong negative relationship, not a weak one. This misconstruing is most commonly found in social science, Spearman's rank correlation.
  
  
• The Fix: Keep in mind that the sign indicates direction (+ or –), whereas the absolute value suggests strength. That's the essence of proper positive Spearman correlation interpretation vs. negative Spearman correlation interpretation.
  
  

Example: If poorer class rank (worse performance) correlates with better marathon rank (better performance), then rho = –0.8. That's a super-strong negative relationship, not a weak one.

Mistake #8: Assuming Causation from Correlation

• The Error: Correlation is not causation. This is still one of the most misused definitions of research. Simply because two variables move in parallel, it does not mean that one causes the other.
• The Fix: Always frame your results in terms of association, not cause-and-effect. Focus on the possibility of confounding factors. In Spearman's rank correlation in medical research, for example, patient recovery can be attributed to a treatment but may also be due to other determinants, such as lifestyle or genetics.
  
  

Examples: Sometimes, good students are also good marathon runners, not necessarily because studying makes a person fit, but rather because the same element of discipline or classes that helps them excel in their studies also helps them become fit.

This is a subtlety that should be taken into consideration to render the Spearman correlation more palatable for data analysis in dissertations.

Mistake #9: Ignoring the Impact of Outliers.

• The Error: Outliers: A single poor observation can significantly impact the outcome and lead to inaccurate conclusions.
  
  

• The Fix: It is always a good idea to plot a scatter and check for outliers. Ask if the outlier is an error in entry or an out-of-the-ordinary but valid case. Programs such as Spearman's correlation in Python (using scipy and pandas) or Spearman's correlation in Excel, with an example, allow it to be clearly seen before tests are run.
  
  

Example: When the four students have a neat 1–1, 2–2, 3–3 pattern and the fifth is 5th in class and 1st in a marathon, rho becomes extremely low. Care should be taken to investigate whether this outlier is actual or not before reaching conclusions.

Mistake #10: Relying on a Single Test

• The Error: Using Spearman's rho as a blanket technique closes off analysis. The use of a single measure could trivialise the richness of relationships.
  
  
• The Fix: Supplement Spearman with other methods. Try Kendall's tau, visual inspection, or bootstrapping. Triangulation adds depth of conviction, particularly in dissertations or business reports.
  
  

Example: In our sample dataset of five students, Spearman's rho may only indicate a value of 0.65. Supplementing statistical hypothesis testing with Spearman's rho or comparing it with Kendall's tau provides additional confidence. This is a best practice in Spearman's correlation in dissertation data analysis, where several methods attest to robustness.

Checklist for a Flawless Spearman Analysis

Follow the given checklist for a smooth step-by-step Spearman analysis.

• Begin by graphing your data in a scatterplot to check for monotonicity so that the variables decrease or increase steadily. This is essential when using Spearman's rank correlation in social sciences or any other research area.

• Ensure that your data is appropriate for the non-parametric nature of Spearman's rank correlation, which performs better with ranks, ordinal data, questionnaires, or performance scores where normality is not assumed.
  
  Let us take some examples to make it clear. The following table provides x and y values for the relation. We can see here that this is a perfectly increasing monotonic relation from the graph.

• Calculate Spearman's rho with the aid of software such as SPSS, R, or Python. Adopt a step-by-step procedure for the precise calculation and interpretation of findings.
• Present the correlation coefficient (ρ), sample size, and p-value clearly, adhering to research reporting best practice. This is especially required when adding Spearman's correlation to dissertation data analysis or scholarly work.

• Be cautious in interpretation by being aware of the difference between Spearman and Pearson correlation, since they quantify relationships differently and are used under different assumptions.
• Use the findings judiciously in real-life situations, showing real-life applications of Spearman's rank correlation in business, education, or psychology studies.

How to Calculate Spearman’s Rho Step by Step?

• Rank the values for both variables separately.
• Compute the difference (d) between the paired ranks.
• Square each difference (d²) and sum them (Σd²).
• Apply the formula: ρ = 1 – 6Σd² / [n(n²–1)], where n is the number of paired observations.
• Interpret the resulting rho according to its sign and magnitude.
  
  

Interpretation of Negative Spearman Correlation

• A negative rho indicates an inverse monotonic relationship: as one variable increases, the other tends to decrease.
• Absolute value indicates the strength of the association.
• Example: ρ = –0.85 → strong negative correlation, not weak

Significance Test for Spearman’s Correlation

• Null hypothesis (H₀): There is no association between variables (ρ = 0).
• Alternative hypothesis (H₁): There is an association (ρ ≠ 0).
• Use p-value to determine significance. A small p-value (e.g., p < 0.05) suggests the correlation is unlikely due to chance.
• Always report ρ, n, and p-value.
  
  

Non-parametric Nature of Spearman's Rank Correlation

• Does not require a normal distribution of data.
• Works with ordinal data, ranks, or skewed distributions.
• Less sensitive to outliers compared to Pearson correlation.
  
  

Ranking Data for Spearman’s Correlation

• Convert raw scores to ranks for each variable.
• If tied values occur, assign the average rank.
• This step ensures Spearman handles ordinal or non-linear data appropriately.
  
  

Statistical Hypothesis Testing with Spearman’s Rho

• Test whether the observed correlation differs significantly from zero.
• Steps: calculate ρ, compute p-value, compare p-value to significance threshold (usually 0.05).
• Report both statistical significance and practical strength.

Conclusion

Becoming an expert in Spearman's rank correlation is less of a formula memorisation exercise and more about executing it with accuracy. Avoiding the ten pitfalls ensures that your results are still credible, defensible, and useful in a wide range of industries. 

Whether you're investigating Spearman's correlation in psychology studies, evaluating patient outcomes in Spearman's rank correlation in healthcare studies, or sharing an example of Spearman's correlation in education research studies, precision is key. 

Be alert, report completely, and interpret carefully—your findings will hold well in both scholarly and professional contexts.

FAQs

What is Spearman’s rank correlation used for? 

When data are not ordinal, ranked, or regularly distributed, Spearman's rank correlation is used to quantify the direction and strength of a monotonic relationship between two variables. It is frequently used in fields where raw scores don't meet parametric assumptions, such as psychology, education, health, and business studies. 

How do you calculate Spearman’s rho manually? 

Rank each variable, find the difference between paired ranks, square those differences, sum them, and insert into the formula: ρ = 1 – 6Σd² / [n(n²–1)]. Software can compute this automatically, but manual calculation aids understanding. 

What is the difference between Spearman’s and Pearson’s correlation? 

Pearson's correlation assesses linear relations among continuous, normally distributed variables, and Spearman's correlation assesses monotonic relations among ranked or non-normal data, but is not as sensitive to outliers. 

How do you interpret Spearman’s rho values in research? 

Strong positive monotonic relationships are shown by values close to +1, strong negative monotonic relationships are indicated by values close to 1, and little to no relationship is indicated by values around 0. 

When should you use Spearman’s rank correlation instead of Pearson's? 

Use Spearman when variables are ordinal, non-linear but monotonic, have extreme outliers, or violate normality assumptions. 

What are the assumptions of Spearman’s rank correlation test?

Paired observations, a monotonic relationship, and independence of observations. Interval data and homoscedasticity are not required.

How do you run Spearman’s correlation in SPSS or R? 

 In SPSS: Analyse → Correlate → Bivariate → select variables → check "Spearman". In R: use cor(x, y, method="spearman") or cor.test(x, y, method="spearman"). 

What does a negative Spearman’s rho mean? 

A negative rho means that as one ranked variable rises, the other falls, demonstrating an inverse monotonic relationship. 

Can Spearman’s rank correlation handle ordinal data? 

Yes, it can handle ordinal data since it relies on ranks rather than raw values, making it suitable for Likert scales or rank-order data. 

How do you report Spearman’s rank correlation in a dissertation?

Report the rho coefficient, sample size, and p-value (e.g., "ρ = –0.45, n = 120, p < .01") and briefly explain the direction and strength in context to ensure clarity and replicability.