Regression Analysis in SPSS: Simple and Multiple Linear Regression
You have collected your data. Your questionnaire is complete. Your respondents have answered. Now comes the moment many students dread: What do I actually do with all these numbers?
If you are asking questions like “Does study time predict exam performance?” or “What factors influence customer satisfaction?” then you need regression analysis. Regression is the statistical tool that moves you from simply describing data to actually predicting outcomes—and it is one of the most commonly required analyses in Kenyan undergraduate, master’s, and PhD theses.
The good news? SPSS makes regression analysis surprisingly straightforward once you know the steps. This guide will walk you through simple and multiple linear regression, from checking assumptions to interpreting every table in your output.
However, if at any point you feel overwhelmed or your deadline is approaching faster than your analysis is progressing, know that help is available. At Proposal Writers Kenya, our data analysis experts can handle your SPSS regression analysis for you—from running the tests to writing up the results section. Visit our service page here to learn more.
What Is Linear Regression?
In plain language, regression helps you understand how one or more variables predict another variable.
Simple linear regression: One predictor variable (X), one outcome variable (Y)
Example: Does hours of study (X) predict exam score (Y)?
Multiple linear regression: Two or more predictor variables (X₁, X₂, X₃…), one outcome variable (Y)
Example: Do study hours, lecture attendance, and sleep predict exam score?
The word “linear” means the relationship follows a straight line. Think of it like this: as X increases, Y increases (or decreases) in a consistent, predictable way.
When to Use Regression in Your Thesis
You should consider regression analysis if:
Your research objectives ask about relationships between variables
Your conceptual framework shows arrows pointing from independent variables to a dependent variable
You want to know which predictor matters most
You need to control for other variables while testing a specific relationship
If this sounds like your study, regression is likely the right tool for you.
Assumptions of Linear Regression (Check These First!)
Before you run any analysis, you must check that your data meets the assumptions of linear regression. Skipping this step is the number one reason students get wrong results.
| Assumption | What It Means | How to Check in SPSS |
|---|---|---|
| Linearity | The relationship between X and Y is straight | Scatterplot |
| Independence of residuals | Observations don’t influence each other | Durbin-Watson (1.5–2.5 is good) |
| Homoscedasticity | Constant variance of errors | Residual plot (no fan shape) |
| Normality of residuals | Residuals are normally distributed | Histogram with normal curve, P-P plot |
| No perfect multicollinearity | Predictors aren’t too correlated (multiple regression only) | VIF (<5 or <10) and Tolerance (>0.2) |
If your data violates these assumptions, your results may be misleading. In that case, you may need data transformation or a different statistical test—or you can let our experts at Proposal Writers Kenya handle it for you.
How to Perform Simple Linear Regression in SPSS
Follow these steps carefully:
Open your dataset in SPSS
Click Analyze > Regression > Linear
Move your dependent variable (outcome) into the Dependent box
Move your independent variable (predictor) into the Independent(s) box
Click Statistics and select: Confidence intervals, Descriptives, Part and partial correlations
Click Plots – put *ZRESID on the Y axis and *ZPRED on the X axis
Click OK
SPSS will generate several tables. Here is how to interpret each one:
Model Summary Table
Look at R Square. This tells you how much variance in your outcome is explained by your predictor. For example, “Study time explains 45% of the variance in exam scores.”
ANOVA Table
This tests whether your model is significantly better than chance. If the p-value (Sig.) is less than .05, your model is significant.
Coefficients Table
This is the most important table for answering your research question. Look at:
Unstandardized B – Use this to write your prediction equation
Standardized Beta – Use this to compare the importance of different predictors
p-value (Sig.) – If less than .05, the predictor has a significant relationship with the outcome
How to Perform Multiple Linear Regression in SPSS
The steps are almost identical:
Analyze > Regression > Linear
Move your dependent variable into the Dependent box
Move ALL your independent variables into the Independent(s) box
Click Statistics and add Collinearity diagnostics (critical for multiple regression!)
Under Method, choose Enter (this enters all predictors together)
Click OK
Additional Interpretation for Multiple Regression
When you have multiple predictors, pay special attention to:
Adjusted R Square – A more honest estimate of variance explained (penalizes adding weak predictors)
Collinearity diagnostics (VIF and Tolerance) – If VIF is above 5 or 10, your predictors are too correlated, and your results are unreliable
Standardized Beta – This tells you which predictor is strongest. The predictor with the largest absolute Beta value has the biggest unique contribution
How to Report Regression Results in Your Thesis (APA 7th Format)
Here is a template for reporting simple linear regression:
“A simple linear regression was conducted to predict exam scores from study hours. There was a significant positive relationship between study hours and exam scores, F(1, 148) = 12.45, p < .001. Study hours explained 45.2% of the variance in exam scores (R² = .452). The regression equation was: Predicted Exam Score = 42.30 + 5.67(Study Hours).”
For multiple linear regression:
“A multiple linear regression was conducted to predict exam scores from study hours, lecture attendance, and sleep. The full model was significant, F(3, 146) = 18.92, p < .001, and explained 62.3% of the variance in exam scores (R² = .623). Study hours made the strongest unique contribution (β = .48, p < .001), followed by lecture attendance (β = .31, p = .002). Sleep was not a significant predictor (β = .09, p = .182).”
Common Mistakes to Avoid
| Mistake | Why It Is a Problem |
|---|---|
| Running regression without checking assumptions | Your conclusions may be completely wrong |
| Treating correlation as causation | Regression shows relationships, not cause-and-effect |
| Ignoring multicollinearity | You cannot trust which predictor is truly important |
| Reporting only unstandardized coefficients | Readers cannot compare the importance of different predictors |
| Using categorical variables without dummy coding | SPSS will treat them as numbers, giving nonsense results |
Conclusion
Regression analysis is one of the most powerful tools in quantitative research. With SPSS, you can move beyond simple percentages and averages to actually test relationships, predict outcomes, and answer your research questions with confidence.
The key is to proceed systematically: check your assumptions first, run your analysis, interpret each table carefully, and report your results following APA guidelines. If you follow the steps in this guide, you will produce regression results that impress your supervisor and strengthen your thesis.
But let us be honest—data analysis can be time-consuming and frustrating, especially when your deadline is approaching. You do not have to do it alone.
At Proposal Writers Kenya, we specialize in helping students like you complete their data analysis correctly and on time. Our experts can:
Run your SPSS regression analysis
Check all assumptions
Interpret every table
Write your complete results chapter in APA format
Do not let statistics delay your graduation. Visit Proposal Writers Kenya today to get a free quote for your data analysis needs. Your successful thesis is closer than you think.