Multiple Regression Using SPSS Statistics
Multiple regression analysis is a statistical technique that is used to predict the value of one variable based on the values of two or more other variables. In this tutorial, we will walk you through the steps of conducting a multiple regression analysis using SPSS Statistics.
Step 1: Load Your Data
Open SPSS Statistics and load your dataset. Ensure that your data is correctly formatted and that there are no missing values. In our example, we will use a dataset that includes variables for salary, years of experience, and education level.
Step 2: Perform the Multiple Regression Analysis
Navigate to Analyze > Regression > Linear…. In the Linear Regression dialog box, move your dependent variable to the Dependent box and your independent variables to the Independent(s) box.
Step 3: Interpret the Results
Once you click OK, SPSS will run the analysis and produce output that includes several tables. Key tables include the Model Summary, ANOVA, and Coefficients tables.
Model Summary
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
|---|---|---|---|---|
| 1 | 0.846 | 0.715 | 0.702 | 1.988 |
ANOVA Table
| Model | Sum of Squares | df | Mean Square | F | Sig. |
|---|---|---|---|---|---|
| 1 | 102.135 | 2 | 51.067 | 12.964 | 0.000 |
Coefficients Table
| Model | Unstandardized Coefficients B | Std. Error | Standardized Coefficients Beta | t | Sig. |
|---|---|---|---|---|---|
| Constant | 2.451 | 0.745 | 3.291 | 0.001 | |
| Years of Experience | 0.675 | 0.123 | 0.512 | 5.487 | 0.000 |
| Education Level | 0.332 | 0.105 | 0.305 | 3.162 | 0.002 |
Discussion
The results indicate that both years of experience and education level are significant predictors of salary. The model explains 71.5% of the variance in salary, which is considered a good fit. The ANOVA table shows that the model is statistically significant (p < 0.001).
In APA format, you would report these results as follows:
A multiple regression was performed to predict salary based on years of experience and education level. The overall model was significant, F(2, 97) = 12.964, p < 0.001, R2 = 0.715. Both predictors were significant, with years of experience (B = 0.675, p < 0.001) and education level (B = 0.332, p = 0.002) positively predicting salary.
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