SPSS has been intimidating researchers for decades. The interface is dense, the output tables are full of numbers with no obvious meaning, and most textbooks explain regression using equations that make sense only if you already understand what you are trying to learn.

This guide takes a different approach. We will walk through linear regression in SPSS step by step, in plain English, using a realistic social research example. By the end, you will know how to run the analysis, read the output tables, and write up your results in academic language — without needing to understand the mathematics behind it.

What Is Linear Regression — and When Do You Use It?

Linear regression is a statistical technique that examines the relationship between one or more predictor variables (also called independent variables) and one outcome variable (also called the dependent variable).

Use linear regression when:

• Your outcome variable is continuous (e.g., a score, an index, a measurement)
• You want to know whether one or more variables predict that outcome
• You want to know how much each predictor contributes to the outcome

In our example: Burnout score (0–100) is the outcome. Years of experience and access to support are the predictors. We want to know: do these two variables predict burnout, and if so, by how much?

“Regression does not prove causation — it identifies and quantifies relationships. The causal argument must come from your theory and research design, not from the statistics alone.”

Before You Run the Analysis: Preparing Your Data

Before running any analysis in SPSS, your data must be properly entered and coded. Here is what to check:

• Each row = one participant. Every respondent has their own row in the data view.
• Each column = one variable. Give each variable a short, clear name with no spaces (e.g., burnout_score, years_exp, support_access).
• Numeric coding. All variables must be numeric. If support access was measured as “Yes/No”, recode it as 1/0.
• Check for missing data. SPSS excludes cases with missing values by default. Decide how to handle missing data before running the analysis.

Reading the Output: The Three Key Tables

Table 1 — Model Summary

This table tells you how well your predictors explain the outcome variable.

Table 2 — ANOVA

This table tells you whether your model is statistically significant overall — i.e., whether your predictors collectively do better than simply guessing the mean.

Table 3 — Coefficients

This is the most detailed table — it tells you the contribution of each individual predictor to the outcome.

How to Write Up Your Results

Academic reporting of regression results follows a standard format. Here is a model write-up based on our example output:

Common Mistakes to Avoid

• Confusing statistical significance with practical importance. A p-value below .05 tells you the result is unlikely to be random — it does not tell you the effect is large or meaningful. Always report and interpret effect sizes (R², β) alongside p-values.
• Ignoring assumptions. Linear regression assumes normally distributed residuals, no multicollinearity, and homoscedasticity. Run the assumption checks (available via the Plots and Diagnostics options in the regression dialog) before trusting your results.
• Reporting only significant results. Report all predictors you entered, whether or not they reached significance. Selective reporting is a form of research bias.
• Claiming causation from correlation. Regression shows relationships, not causes. Your causal argument must come from your theory, design, and logic — not from the statistics.

Conclusion

Running your first regression in SPSS is far less intimidating once you understand what each table is telling you. The process is straightforward: prepare your data, run the analysis through Analyze → Regression → Linear, and then read three tables — Model Summary for R², ANOVA for overall significance, and Coefficients for individual predictor contributions.

The statistics are a tool for answering your research questions. They are not the research itself. Keep your focus on what the numbers mean for the people and phenomena you are studying — and your analysis will always stay grounded in what matters.

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