Exploring Relationships in SPSS: From Means to Regression Analysis

Every research question in education eventually comes down to one thing: are two variables connected, and if so, how? Does attendance affect exam scores? Does teaching method influence retention? SPSS gives you a toolbox of procedures to answer exactly this, moving from something as simple as an average to something as advanced as testing whether a relationship bends instead of following a straight line. This post walks through four of the most commonly used tools in that toolbox: the Mean procedure, Linear Regression, Curve Estimation, and the ever-important distinction between correlation and causation.

The mean procedure: comparing averages across groups 🔗

Before you can talk about relationships between variables, you often need to understand how a single variable behaves within different groups. That is exactly what the Compare Means procedure in SPSS does. You will find it under Analyze, then Compare Means, then Means (in newer versions of SPSS, this has been relabelled as Compare Means and Proportions). It lets you summarise a numeric variable, such as exam scores, separately for each category of a grouping variable, such as gender, school type, or teaching method, as explained in this SPSS tutorial from Kent State University.

Say you are studying whether students taught using activity-based learning perform differently from those taught through the traditional lecture method. Using Compare Means, you place the exam score variable in the Dependent List and the teaching method variable in the Layer box. SPSS then produces a neat table showing the mean, standard deviation, and number of cases for each group, giving you an immediate sense of whether the groups differ.

When compare means isn’t enough 🔗

Compare Means is descriptive. It tells you the averages look different, but it does not tell you whether that difference is statistically meaningful. For that, you typically move to an independent samples t-test if you are comparing two groups, or a one-way ANOVA if you are comparing three or more. The distinction that matters here is whether your groups are independent (different students in each group) or related (the same students measured twice, such as before and after an intervention), a point well explained in this UCLA guide on setting up data for mean comparisons. Getting this distinction right affects which SPSS procedure you should even attempt to run, so it is worth pausing on before you touch the Analyze menu.

Linear regression in spss: predicting outcomes 🔗

Once you know that two variables are related, the natural next question is how much one changes when the other does, and whether you can use that relationship to make a prediction. This is where linear regression comes in. In SPSS, you access it through Analyze, then Regression, then Linear.

Regression works by fitting a straight line through your data points that best represents the relationship between a predictor (independent variable) and an outcome (dependent variable). If you wanted to predict a student’s final exam score based on the number of hours spent studying per week, study hours would go into the Independent box and exam score into the Dependent box, as described in this University of Guelph guide to running regression in SPSS.

The output gives you a regression equation in the form Y = a + bX, where a is the constant (the predicted score when study hours are zero) and b is the slope (how much the exam score is expected to change for every additional hour of study). Three tables matter most here. The Model Summary table gives you R Square, which tells you what percentage of the variation in exam scores is explained by study hours. The ANOVA table tells you whether the model as a whole is statistically significant. And the Coefficients table gives you the actual slope and intercept values along with their significance levels.

Checking assumptions before you trust the model 🔗

A regression output only means something if the underlying assumptions hold up. Linearity, normality of residuals, and homoscedasticity (constant spread of errors across all predicted values) all need to be checked using scatterplots and residual plots before you report your results, as outlined in this step-by-step linear regression tutorial. Skipping this step is one of the most common mistakes in student research projects. A regression line fitted to data that is clearly curved, for instance, will give you numbers, but those numbers will mislead rather than inform.

Curve estimation: understanding non-linear relationships 🔗

Not every relationship in education research is a straight line. Consider stress and academic performance. A moderate amount of stress can push a student to prepare better, but beyond a certain point, stress starts hurting performance. Plotted on a graph, this looks like an inverted U, not a straight diagonal line. Forcing a linear regression onto data like this will underestimate or completely miss the pattern.

This is exactly the situation the Curve Estimation procedure is built for. In SPSS, you go to Analyze, then Regression, then Curve Estimation, where you can test several curve models, quadratic, cubic, logarithmic, and more, against your data simultaneously to see which one fits best, as shown in this guide to drawing regression and curve fit lines in SPSS. SPSS returns an R Square value for each model type, letting you compare which curve explains the data better than a plain straight line.

This matters more than it might seem. Research on modelling non-linear relationships has shown that when a truly curved relationship is forced into a linear model, the resulting effect estimate can be biased, sometimes overstating and sometimes understating the real pattern in the data, according to this study on modelling non-linear relationships in research data. Whether you are studying anxiety and test performance, screen time and attention span, or age and learning speed, checking for curvature before settling on a model is good practice, not an optional extra, as reinforced by this explanation of curve fitting using linear and non-linear regression.

Correlation vs causation: the trap every researcher must avoid 🔗

Here is where many student research projects go wrong. SPSS can tell you that two variables move together. It cannot, on its own, tell you that one causes the other. A correlation coefficient only measures the strength and direction of an association. It says nothing about why that association exists.

The textbook example used across statistics courses is the strong correlation between ice cream sales and violent crime rates. It is not that ice cream causes aggression. Both variables rise together because of a third factor, hot weather, that independently affects each one, as explained in this overview of the difference between correlation and causation. This is called the third variable problem, and it is one of the most common reasons a correlation can look meaningful while actually being misleading.

In an education research context, imagine finding a strong positive correlation between the number of extra classes a student attends and their exam scores. It is tempting to conclude that extra classes cause better scores. But a more motivated or academically stronger student might simply be more likely to attend extra classes in the first place, meaning motivation, not the classes themselves, could be driving both variables. There is also the directionality problem to consider: does attending more classes lead to better scores, or do students who are already doing well decide to attend more classes to stay ahead?

This is why researchers increasingly emphasise training students to actively question correlational findings rather than accepting the popular but often too-quickly repeated phrase that correlation does not imply causation. Structured interventions that teach students to generate alternative explanations for why two variables might move together have been shown to meaningfully improve how well people distinguish a genuine causal relationship from a coincidental one, according to this research on improving how correlation and causation are taught. For SPSS users specifically, this means that running a correlation or regression procedure is only the first step. Interpreting what that output actually means, and being honest about what it cannot tell you, is the harder and more important part of the job.

Bringing it all together 🔗

These four procedures build on each other in a logical sequence. Compare Means helps you spot differences between groups. Linear Regression helps you quantify and predict relationships once you suspect they exist. Curve Estimation checks whether that relationship is actually a straight line or something more complex. And the correlation versus causation distinction keeps you honest about what any of these numbers actually prove. Used together, they form a fairly complete toolkit for exploring how variables in your research relate to one another, and just as importantly, for knowing where the limits of that exploration lie.

What do you think? Have you come across a research finding, in education or elsewhere, that assumed causation based on nothing more than a correlation? And when analysing your own data, how do you decide whether a relationship might be curved rather than a straight line before you run your regression?

References

1. https://libguides.library.kent.edu/SPSS/CompareMeans
2. https://stats.oarc.ucla.edu/spss/library/spss-library-data-setup-for-comparing-means-in-spss/
3. https://guides.lib.uoguelph.ca/c.php?g=525348&p=5358825
4. https://www.spss-tutorials.com/spss-simple-linear-regression-tutorial/
5. https://www.spss-tutorials.com/how-to-draw-a-regression-line-in-spss/
6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10910897/
7. https://statisticsbyjim.com/regression/curve-fitting-linear-nonlinear-regression/
8. https://www.scribbr.com/methodology/correlation-vs-causation/
9. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1645518/pdf