How to Perform Linear Regression Analysis in SPSS: Step-by-Step Guide

Linear regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Researchers use SPSS to run regression analyses in many fields because it offers an intuitive interface and robust statistical algorithms.

**1. Prepare Your Data**

Before running a regression, ensure your dataset includes a clearly defined dependent variable and one or more independent variables. SPSS can read data from various sources such as Excel, CSV, text and other formats. Check your data for missing values and outliers, and clean or transform variables as needed. Regression assumes a linear relationship between variables, independent observations, and normally distributed residuals, so exploring descriptive statistics and scatterplots helps you assess these conditions.

**2. Run the Regression**

In SPSS, navigate to *Analyze > Regression > Linear*. In the dialog box, assign your dependent variable to the “Dependent” field and your predictors to the “Independent(s)” field. You can add multiple independent variables for multiple regression. Under “Statistics” select options to display estimates, confidence intervals and model fit measures. Click “OK” to run the analysis.

**3. Interpret the Output**

The SPSS output begins with a table showing the correlation coefficient (R) and the coefficient of determination (R‑square). R measures the strength of the linear relationship between observed and predicted values, while R‑square indicates the proportion of variance in the dependent variable explained by the model. A subsequent ANOVA table tests whether the regression model significantly improves prediction compared to a model with no predictors. The coefficients table lists the unstandardized and standardized regression coefficients, their standard errors, t values and p values. Significant predictors (p < 0.05) contribute meaningfully to the dependent variable. The constant (intercept) represents the predicted value of the dependent variable when all predictors are zero.

**4. Check Assumptions**

After fitting the model, examine diagnostic plots to verify assumptions. Plot standardized residuals against predicted values to detect non‑linearity or heteroscedasticity (non-constant error variance). Use histograms or normal probability plots of residuals to assess normality. Look for influential outliers using Cook’s distance or leverage values. If assumptions are violated, you may need to transform variables or consider a different model.

**5. Report and Use the Findings**

When reporting regression results, state the model fit (R and R‑square), F statistics and their significance, and the coefficients with confidence intervals. For example: “The model explained 62 % of the variance in test scores (R² = 0.62) and was statistically significant (F = 35.7, p < 0.001). Study hours (β = 0.45, p = 0.002) and class attendance (β = 0.30, p = 0.01) positively predicted test scores.” Such interpretation helps readers understand the practical significance of your findings.

**6. Professional Support**

Regression analysis can be complex, particularly with multiple predictors or violations of assumptions. Our consulting service can help you structure your dataset, run appropriate models in SPSS and interpret the output correctly. Contact us to ensure your regression analyses are accurate and insightful.