Guide SPSS How to: Correlation

Calculating Correlation in SPSS

Step 1: Prepare Your Data

• Enter your data into SPSS, with each variable in a separate column.
• Ensure your variables are measured on an interval or ratio scale for Pearson’s r, or ordinal scale for Spearman’s rho

Step 2: Access the Correlation Analysis Tool

1. Click on “Analyze” in the top menu.
2. Select “Correlate” from the dropdown menu.
3. Choose “Bivariate” from the submenu

Step 3: Select Variables

• In the new window, move your variables of interest into the “Variables” box.
• You can select multiple variables to create a correlation matrix

Step 4: Choose Correlation Coefficient

• For Pearson’s r: Ensure “Pearson” is checked (it’s usually the default).
• For Spearman’s rho: Check the “Spearman” box

Step 5: Additional Options

• Under “Test of Significance,” select “Two-tailed” unless you have a specific directional hypothesis.
• Check “Flag significant correlations” to highlight significant results

Step 6: Run the Analysis

• Click “OK” to generate the correlation output

Interpreting the Results

Correlation Coefficient

• The value ranges from -1 to +1.
• Positive values indicate a positive relationship, negative values indicate an inverse relationship[1].
• Strength of correlation:
• 0.00 to 0.29: Weak
• 0.30 to 0.49: Moderate
• 0.50 to 1.00: Strong

Statistical Significance

• Look for p-values less than 0.05 (or your chosen significance level) to determine if the correlation is statistically significant.

Sample Size

• The output will also show the sample size (n) for each correlation.

Remember, correlation does not imply causation. Always interpret your results in the context of your research question and theoretical framework.

To interpret the results of a Pearson correlation in SPSS, focus on these key elements:

1. Correlation Coefficient (r): This value ranges from -1 to +1 and indicates the strength and direction of the relationship between variables

• Positive values indicate a positive relationship, negative values indicate an inverse relationship.
• Strength interpretation:
  • 0.00 to 0.29: Weak correlation
  • 0.30 to 0.49: Moderate correlation
  • 0.50 to 1.00: Strong correlation

1. Statistical Significance: Look at the “Sig. (2-tailed)” value

• If this value is less than your chosen significance level (typically 0.05), the correlation is statistically significant.
• Significant correlations are often flagged with asterisks in the output.

1. Sample Size (n): This indicates the number of cases used in the analysis

Example Interpretation

Let’s say you have a correlation coefficient of 0.228 with a significance value of 0.060:

1. The correlation coefficient (0.228) indicates a weak positive relationship between the variables.
2. The significance value (0.060) is greater than 0.05, meaning the correlation is not statistically significant
3. This suggests that while a small positive correlation was observed in the sample, there’s not enough evidence to conclude that this relationship exists in the population
4. Remember, correlation does not imply causation. Always interpret results in the context of your research question and theoretical framework.