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Scatterplot Matrix (SPLOM) in SPSS: Visualizing Multiple Correlations

Scatterplot Matrix (SPLOM) in SPSS: Visualizing Multiple Correlations
IBM SPSS Statistics 27 File Edit View Data Transform Analyze Graphs Utilities Chart Builder ▶ Scatter/Dot ▶ ▶ Scatterplot Matrix Menü Yolu: Graphs → Chart Builder → Scatter/Dot → Scatterplot Matrix Yukarıdaki menü yolunu takip ederek analiz penceresini açın

📸 Scatterplot Matrix in SPSS: Graphs → Chart Builder

What Is a Scatterplot Matrix?

A scatterplot matrix (SPLOM) displays all pairwise scatter plots between multiple variables in a single grid. It is the most efficient visual tool for simultaneously exploring relationships, linearity, outliers, and distributional shapes across several variables before running correlations or regression.

Each cell shows the scatter plot for one pair of variables; the diagonal typically shows the variable distribution (histogram or density plot).

Creating SPLOM in SPSS

Step 1: Graphs → Chart Builder. In the Gallery, select Scatter/Dot → drag the Scatterplot Matrix icon to the canvas.
Step 2: Drag all variables of interest into the "Scatterplot Matrix Variables" box.
Step 3: Optionally add a grouping variable for color-coded points.
Alternative: Graphs → Legacy Dialogs → Scatter/Dot → Matrix Scatter. Add variables to Matrix Variables → OK.
SPSS Statistics Output Viewer Correlation Matrix (Pearson r) Stress Sleep Anxiety Performance Stress 1.00 -.51* .63* -.44* Sleep -.51* 1.00 -.38* .52* Anxiety .63* -.38* 1.00 -.61* Performance -.44* .52* -.61* 1.00 * p < .05 anlamlı sonuç gösterir

📸 Correlation matrix accompanying the scatterplot matrix visualization

What to Look For

In the SPLOM, look for: linear vs. curved relationships (inform regression choices), outliers (visible as isolated points), homoscedasticity (even spread across the range), and patterns by group when color-coded. Always inspect SPLOM before running multivariate analyses.

APA Reporting

A scatterplot matrix was examined to assess linearity and outliers prior to regression analysis. All bivariate relationships appeared approximately linear with no extreme outliers. Significant correlations among predictors ranged from r=-.38 to r=.63 (all p<.001).

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