Pearson vs Spearman Correlation: Which One Should You Use in Your Dissertation?

Pearson vs Spearman correlation comes down to one question: is the relationship between your two variables a straight line, or just a consistent trend? Pearson correlation measures how strongly two variables move together in a straight line, and it needs normally distributed, interval or ratio data. Spearman correlation measures how consistently two variables move together in the same direction, even if the relationship is not a straight line, and it works on ranked or non-normal data. That is the core difference, and everything else in this article is about applying it correctly to your own dissertation dataset.

I have been guiding dissertation students through this exact decision for 12 years now, across SPSS data analysis sessions, Stata models, and R scripts. And I can tell you honestly, this one choice, Pearson or Spearman, is one of the most common reasons a methodology chapter comes back with revision comments. Not because the concept is hard. Because most students pick the test first and check the assumptions later, which is backwards.

What Is Pearson Correlation?

Pearson correlation, developed by Karl Pearson, tells you the strength and direction of a linear relationship between two continuous variables, a form of bivariate analysis since it looks at exactly two variables at once. The output is called Pearson’s r, and it ranges from -1 to +1. A value close to +1 means the variables rise together in a straight line pattern (a positive correlation), close to -1 means one rises as the other falls (a negative correlation), and close to 0 means no linear pattern at all.

What Pearson’s r Actually Measures

Pearson’s r is calculated using the covariance of the two variables divided by the product of their standard deviations. In plain language, it checks how far each data point sits from the average, for both variables, and sees if those distances move together consistently. This is why it only works well when the relationship genuinely looks like a straight line on a scatterplot. If you want to see this worked out by hand, step by step, I have a full walkthrough here: How to Calculate Pearson Correlation Coefficient by Hand.

Assumptions Pearson Requires

Pearson correlation is a parametric test, which means it comes with conditions attached. Before you run it, your data should meet these:

1. Both variables are continuous (interval or ratio level, not ordinal or categorical)
2. Both variables are approximately normally distributed
3. The relationship between them is linear, not curved
4. There are no serious outliers pulling the line in one direction
5. The variance is roughly consistent across the range of values (homoscedasticity)

If even one of these breaks down badly, your Pearson result becomes unreliable, and that is usually the point where Spearman enters the conversation.

A common question I get here: does “approximately normal” mean perfectly normal? No, Pearson is reasonably tolerant of mild non-normality once your sample size is above roughly 30, thanks to the central limit theorem. What genuinely breaks Pearson is pronounced skew, heavy-tailed data, or a handful of extreme outliers, not minor departures from a perfect bell curve. Minitab’s own comparison of the two methods makes the same point, small deviations rarely change the conclusion, large ones do.

What Is Spearman Correlation?

Spearman correlation, named after Charles Spearman, measures a monotonic relationship, meaning one variable consistently increases (or decreases) as the other increases, without needing that increase to happen in a straight line. Instead of using the raw values, Spearman converts each variable into ranks first, then correlates the ranks. The output is Spearman’s rho, written as the Greek letter ρ, and it also ranges from -1 to +1.

What Spearman’s Rho Actually Measures

Because Spearman works on ranks and not raw scores, it does not care whether your data is shaped like a bell curve. It only asks, does a higher rank on one variable tend to go with a higher (or lower) rank on the other. This makes it far more forgiving of skewed distributions and outliers. I have written a separate, deeper explainer on this if you want the ranking mechanics in more detail: What Is Spearman’s Rank Correlation Coefficient.

Assumptions Spearman Requires

Spearman is a non-parametric test, so the requirements are lighter:

1. The two variables should be at least ordinal (ranked), interval, or ratio level
2. The relationship should be monotonic, meaning it consistently goes up or consistently goes down, even if not in a straight line
3. Observations should be independent of each other

Notice there is no normality requirement here. That single difference is why Spearman becomes the safer default for a lot of dissertation data, especially Likert scale surveys.

Pearson vs Spearman Correlation: Key Differences at a Glance

I get asked to explain this table more than anything else in a supervision session, so here it is in one place.

Feature	Pearson Correlation	Spearman Correlation
Data type needed	Interval or ratio (continuous)	Ordinal, interval, or ratio
Relationship type	Linear only	Monotonic (linear or curved)
Distribution requirement	Should be normally distributed	No normality requirement
Sensitive to outliers	Yes, quite sensitive	Less affected by outliers
What it uses	Raw values	Ranked values
Symbol	r	ρ (rho)
Test family	Parametric	Non-parametric

A lot of blog posts on this topic stop right here, at the comparison table, and call it done. In my experience that is where students get into trouble, because knowing the definitions does not tell you what to do with your own messy, real world dataset. If you want to see how correlation coefficients get applied outside a stats classroom, I have covered that separately in Correlation Coefficient and Its Practical Significance in Business, and for a third academic reference point beyond my own notes, UVA Library’s guide to Pearson, Spearman, and Kendall’s tau is a solid one to cross-check against. The next section is the part that actually matters for your results chapter.

When to Use Pearson vs Spearman (Decision Checklist)

Use Pearson When…

• Both your variables are continuous, like test scores, income, or age in years
• A scatterplot of your two variables shows a roughly straight line pattern
• Your data passes a normality check (Shapiro-Wilk test or a visual histogram check)
• You do not have extreme outliers skewing your results

Use Spearman When…

• One or both variables are ordinal, such as Likert scale responses (strongly disagree to strongly agree)
• Your continuous data is skewed or fails the normality test
• Your scatterplot shows a consistent upward or downward trend that curves rather than sitting in a straight line
• You have outliers you cannot justify removing

What If Your Data Fails Both Sets of Assumptions?

This happens more often than most textbooks admit. If your relationship is not even monotonic, meaning it goes up then comes down (a U shape, for example), neither Pearson nor Spearman is appropriate, and you are better off looking at a different technique entirely, such as a polynomial regression or reporting the non-linear pattern descriptively with a scatterplot. Do not force a correlation coefficient onto a relationship that clearly is not correlational. Peer-reviewed work backs this up too, a Cureus paper on correlation coefficients in medical sciences makes the same warning, that applying the wrong coefficient to the wrong relationship shape can hide genuine associations rather than reveal them.

Pearson vs Spearman for Dissertations and Thesis Work

Every Pearson correlation dissertation chapter and Spearman correlation dissertation chapter needs the same thing: a clear, evidence-backed justification for the test you picked.

What Examiners Look For in Your Justification

An examiner is not checking whether you ran the “correct” test in some absolute sense. They are checking whether you can justify your choice with evidence, meaning did you actually test your assumptions before picking. I tell every one of my mentees the same thing: show your normality test output (Shapiro-Wilk or Kolmogorov-Smirnov), show a scatterplot, then state your choice. That one paragraph, backed by a table, closes off almost every “justify your correlation choice” comment before it gets written.

One more thing worth saying plainly: Spearman is not a weaker or lazier choice than Pearson. Examiners do not penalise a well justified Spearman correlation, they penalise an unjustified one. Picking the test that actually matches your data’s shape is what real statistical competence looks like, not defaulting to whichever test sounds more advanced.

Here is roughly how I ask students to structure that paragraph, adapted to their own variables:

“Prior to correlation analysis, normality of the two variables was assessed using the Shapiro-Wilk test. Variable A did not meet the normality assumption (p < .05), and visual inspection of the scatterplot indicated a monotonic but non-linear relationship. Spearman’s rank correlation was therefore selected as the appropriate test.”

Three sentences. That is all it takes. Most revision comments on this topic come not because the wrong test was picked, but because this exact justification was missing, and the examiner had to guess whether the choice was deliberate or accidental.

Where This Goes Wrong at the Viva Stage

I have sat in on mock vivas where a student picked the right test but froze when asked “why not the other one.” An examiner in a viva does not want to hear the textbook definition read back to them. They want you to point at your own scatterplot and your own Shapiro-Wilk output and say, in your own words, why that data shape ruled out the alternative. If you cannot explain your own assumption tests without notes, you have memorised the answer instead of understanding it, and that usually shows within the first follow-up question.

Common Mistakes Students Make Choosing the Wrong Test

• Running Pearson by default because SPSS opens with it pre-selected in the Bivariate Correlation dialog box
• Using Pearson on Likert scale data because it “still gives a number”
• Never checking normality at all, and assuming the sample size is “big enough” to not matter
• Ignoring one or two extreme outliers that are quietly inflating or deflating the Pearson coefficient
• Reporting both Pearson and Spearman side by side without picking one, hoping the reader decides for them (reviewers do not appreciate this, it reads as indecision, not thoroughness)

(The two examples below are anonymised and composited from patterns I have seen across many students, not any single identifiable case.)

I had a masters student in the UK, researching the relationship between remote work hours and self-reported burnout scores, who had run Pearson correlation on a five-point Likert burnout scale. The scatterplot showed the classic step pattern you get from ordinal data, not a smooth line. We reran it as Spearman, the coefficient actually came out stronger and more defensible, and the methodology comment from her supervisor changed from “justify this choice” to “well explained.” Small correction, but it saved her a full revision cycle.

A second case, this one a doctoral student in the US working on a finance-adjacent dissertation, correlating executive compensation with company performance across a sample of mid-cap firms. She ran Pearson first, and got a surprisingly weak, statistically insignificant r. The scatterplot told the real story, two firms with extreme, one-off compensation packages were dragging the whole line off balance. Rather than removing “inconvenient” data points, which raises its own ethical red flag in a defence, we reran the analysis as Spearman, since it is far less sensitive to that kind of outlier.

The rho came back moderate and significant, which matched what her qualitative interview data was already suggesting. The lesson for her, and for most students in finance or economics-adjacent topics, is that outlier-heavy data is extremely common. Defaulting to Pearson without a scatterplot check can genuinely hide a real relationship rather than reveal one.

How to Run and Interpret Both in SPSS

Running a Pearson correlation SPSS analysis and a Spearman correlation SPSS analysis both start from the same menu, the Bivariate Correlate dialog, but the settings you tick differ. Here is the exact sequence for each.

Step-by-Step: Running Pearson in SPSS

1. Go to Analyze > Correlate > Bivariate
2. Move your two continuous variables into the Variables box
3. Under Correlation Coefficients, tick Pearson
4. Select Two-tailed significance (unless you have a directional hypothesis)
5. Click OK and review the output table

Step-by-Step: Running Spearman in SPSS

1. Go to Analyze > Correlate > Bivariate
2. Move your two variables (ordinal or continuous) into the Variables box
3. Under Correlation Coefficients, tick Spearman instead of Pearson
4. Select Two-tailed significance
5. Click OK and review the rho value and significance column

Interpreting the Output (r, rho, and p-value)

SPSS gives you the coefficient (r or rho) and a significance value (p-value) for each pair. A common rule of thumb for correlation strength, whether Pearson or Spearman, is roughly 0.10 to 0.29 as weak, 0.30 to 0.49 as moderate, and 0.50 and above as strong. The p-value tells you the statistical significance of the relationship, typically using the 0.05 cutoff as the threshold. Remember, a significant correlation only tells you the relationship is unlikely to be random, it does not tell you one variable causes the other.

If your examiner asks for more precision than a single coefficient, request a confidence interval alongside it. If your SPSS license includes the Bootstrapping option, you can generate a bootstrapped confidence interval for both Pearson’s r and Spearman’s rho directly from the same dialog box. A tight confidence interval around your coefficient makes your results section noticeably more convincing than reporting r or rho alone.

How to Report Pearson and Spearman Correlation (APA Style)

For Pearson, the standard APA format looks like this:

A Pearson correlation revealed a significant positive relationship between study hours and exam scores, r(148) = .42, p < .001.

For Spearman, the format is similar but uses rho:

A Spearman correlation revealed a significant negative relationship between work stress ranking and job satisfaction ranking, rs(96) = -.38, p = .002.

The number in brackets is your degrees of freedom for Pearson, calculated as sample size minus 2. For Spearman, APA convention is less rigid, some departments still expect degrees of freedom in the same format, while others prefer you report the sample size (n) directly since Spearman is a non-parametric test. Always check your specific university’s reporting template before you finalise the format, this is a small detail that examiners do notice.

Worked Example: Choosing the Right Test for Dissertation Data

Say you are researching whether employee engagement scores (measured on a 1 to 5 Likert scale) relate to monthly sales performance (measured in actual currency, a continuous ratio variable). You have one ordinal variable and one continuous variable. In this mixed situation, Spearman is the safer choice, because Pearson technically requires both variables to be interval or ratio, and forcing a Likert scale into a Pearson test is a stretch most examiners will flag.

Now compare that to a second scenario, where you are correlating hours of sleep per night against a continuous cognitive test score, both measured numerically with no ranking involved. Here, if your scatterplot looks like a straight line and your normality test passes, Pearson is the more precise and more powerful choice, since it uses the actual values rather than converting everything to ranks and losing some information in the process.

This is exactly the kind of decision I sit through with students during statistics tutoring sessions, because the textbook definition rarely maps cleanly onto real survey data without a proper walkthrough.

A Quick Word on What Most Articles Get Wrong

Most articles on Pearson vs Spearman correlation use finance or sports examples, stock prices, cricket scores, things like that. Those examples are fine for teaching the maths, but they do not prepare you for the actual mess of dissertation data, which is usually a mix of Likert scale surveys, demographic variables, and a handful of continuous measures all in the same dataset. I have also noticed several guides skip the “what if your data fails both assumptions” scenario entirely, as if every dataset conveniently fits one box or the other. In my 12 years of reviewing student data, that clean scenario is the exception, not the rule.

Frequently Asked Questions

Written by Siddharth Gupta, independent statistics and data analysis mentor with 12 years of experience guiding dissertation students across the USA, UK, and Middle East. His background spans an MBA in Finance and an M.Tech, with hands-on work across SPSS, R, Stata, and Power BI. Full profile and background: statssy.com/siddharth-gupta. Connect on LinkedIn.