Chi-Square Test in SPSS: Step-by-Step English Guide with Example

The Chi-Square Test of Independence is a statistical test that examines the association or independence between two categorical variables. This test analyzes the difference between observed and expected frequencies in a contingency table. Performing this test in SPSS (Statistical Package for the Social Sciences) is very easy.

In this article, we’ll look step-by-step at how to perform the Chi-Square Test in SPSS, with a practical example. This guide is in English and is useful for students, researchers, and professionals.

What is Chi-Square Test?

The Chi-Square Test checks whether there is a statistically significant relationship between two categorical variables. The null hypothesis (H₀) states that the variables are independent, while the alternative hypothesis (H₁) states that there is an association between the variables.

Formula:

Where:

• \(O_i\): Observed frequency.
• \(E_i\): Expected frequency.
• \(\sum\): Sum over all cells.

SPSS performs these calculations automatically and provides a p-value.

How to Perform Chi-Square Test in SPSS: Step-by-Step Guide

Below is the process with an example. We’ll use a dataset to check whether there’s an association between Gender and Smoking Status.

Example Dataset

Problem: A survey collected data from 100 people recording their Gender (Male/Female) and Smoking Status (Smoker/Non-Smoker). We want to check if there’s an association between Gender and Smoking Status.

Data: Contingency table (observed frequencies):

Hypothesis:

• Null Hypothesis (H₀): No association between Gender and Smoking Status (they are independent).
• Alternative Hypothesis (H₁): There is an association between Gender and Smoking Status.

Step 1: Open Dataset in SPSS

1. Open SPSS software and load your dataset (File > Open > Data).
2. Ensure your variables are categorical. Check in Variable View:
   • Type: String or Numeric.
   • Measure: Nominal or Ordinal.
   • Values: For Gender (e.g., 1=Male, 2=Female), for Smoking Status (e.g., 1=Smoker, 2=Non-Smoker).
3. If variables aren’t categorical, recode them (Transform > Recode into Different Variables).

Example: Our dataset has two columns: Gender (1=Male, 2=Female) and Smoking_Status (1=Smoker, 2=Non-Smoker).

Step 2: Select Crosstabs Command

1. Go to SPSS top menu and click:
   • Analyze > Descriptive Statistics > Crosstabs.
2. A Crosstabs dialog box will open.

Step 3: Select Variables

1. In the Crosstabs dialog box, variables will appear on the left side.
2. Select two variables:
   • Add Gender to the Row(s) box.
   • Add Smoking_Status to the Column(s) box.
3. Note: It doesn’t matter which variable goes in Row or Column.

Step 4: Enable Chi-Square Test

1. In the Crosstabs dialog box, click the Statistics button.
2. A new dialog box will open. Check:
   • Chi-square (for Chi-Square Test of Independence).
   • Optional: Phi and Cramer’s V (for association strength).
3. Click Continue.

Step 5: Enable Observed and Expected Counts

1. In the Crosstabs dialog box, click the Cells button.
2. A new dialog box will open. Here:
   • Under Counts section:
     • Check Observed.
     • Check Expected.
   • Optional: Under Percentages section, check Row, Column, or Total.
3. Click Continue.

Step 6: Optional – Clustered Bar Chart

1. In the Crosstabs dialog box, check Display clustered bar charts.
2. This will create a bar chart visually showing the relationship between variables.

Step 7: Run the Test

1. In the Crosstabs dialog box, click OK.
2. SPSS will run the analysis and display output in the Output Viewer window.

Step 8: Interpret SPSS Output

SPSS output will show three main tables:

1. Case Processing Summary

This table shows how many cases are valid and how many are missing.

Interpretation: There are 100 valid cases, no missing data.

2. Crosstabulation Table

This contingency table shows observed and expected frequencies.

Interpretation: There are differences between observed counts (20, 30, 10, 40) and expected counts (15, 35, 15, 35), which the Chi-Square test will analyze.

3. Chi-Square Tests Table

This table shows the Chi-Square statistic and p-value.

Interpretation:

• Chi-Square Statistic: 4.762
• Degrees of Freedom (df): 1 (because it’s a 2×2 table: (2-1)*(2-1)).
• p-value: 0.029 (less than 0.05, so we reject H₀).
• Phi/Cramer’s V: 0.218 (weak association).
• Conclusion: There is a statistically significant association between Gender and Smoking Status.

Step 9: Check Assumptions

Assumptions for Chi-Square Test:

• Categorical Variables: Gender and Smoking Status are nominal.
• Independent Observations: Each observation is independent (survey data is assumed).
• Expected Frequencies: All cells should have expected counts ≥ 5. Here all counts (15, 35, 15, 35) ≥ 5.
• No Missing Data: Confirmed from Case Processing Summary that there’s no missing data.

Step 10: Report Results (APA Style)

For formal reporting, use APA style:

A Chi-Square Test of Independence was performed to examine the association between Gender and Smoking Status. Results revealed a significant association between the two variables, \(\chi^2(1, N=100) = 4.762, p = 0.029\), Cramer’s V = 0.218, indicating a weak association.

Common Errors and Troubleshooting

• Error: Expected Count < 5:
  • Solution: Select Fisher’s Exact Test in the Statistics dialog box or combine categories.
• No Output:
  • Solution: Check that variables are in Row/Column boxes and Chi-square option is checked.
• Missing Data:
  • Solution: Check missing values via Analyze > Descriptive Statistics > Frequencies and apply listwise deletion or imputation.
• Non-Categorical Variables:
  • Solution: Use Transform > Recode into Different Variables to make variables categorical.

Tips for Accurate Chi-Square Test in SPSS

• Data Cleaning: Check for missing values or incorrect entries.
• Variable Coding: Properly code categorical variables (e.g., 1=Male, 2=Female).
• Visuals: Use clustered bar charts for better presentation.
• Save Output: Save SPSS output (File > Export) for future reference.
• Effect Size: Report Phi or Cramer’s V to show association strength.

Summary

• Chi-Square Test: Tests association between two categorical variables.
• SPSS Process: Analyze > Crosstabs > Select variables > Statistics (Chi-square) > Cells (Observed, Expected) > OK.
• Example: With Gender and Smoking Status data, we got \(\chi^2 = 4.762\), df = 1, p = 0.029, showing significant association.
• Assumptions: Categorical variables, independent observations, expected counts ≥ 5.
• SEO Keywords: Targeted keywords include “Chi-Square Test in SPSS English”, “How to do Chi-Square in SPSS”.

This guide will help you perform Chi-Square Test in SPSS. If you have more questions, please comment!