In social science research, we often want to not only identify group differences but also understand which variables cause those differences and predict group membership for individuals. In such cases, decision trees and classification analysis offer a visual and easy-to-interpret solution. In this article, we’ll explain what decision trees are, how they work, and how to use them in your thesis—clearly and with examples.

1. What Is a Decision Tree?

A decision tree is an algorithm that classifies and predicts data by splitting it into branches. Each branch represents a decision rule; the leaves show the classification outcome.

Example: Predicting whether a student will pass an exam based on study time, attendance, and motivation level.

2. When to Use Decision Trees

• When the dependent variable is categorical (e.g., pass/fail, satisfied/not satisfied)
• When independent variables are mixed types (continuous and categorical)
• When a visual presentation of the model is desired
• When complex relationships need to be simplified

3. Common Decision Tree Algorithms

4. Advantages of Decision Trees

• Easy to interpret and visualize
• Can handle missing data
• Works with both categorical and continuous variables
• Simplifies complex models

5. How to Perform Classification with Decision Trees

Data Preparation

• Dependent variable should be categorical (e.g., “successful” vs. “unsuccessful”)
• Independent variables can be numeric or categorical

Model Building

• Use tools like SPSS, R (rpart, party), or Python (scikit-learn) to build decision trees

Model Evaluation

• Accuracy rate (% of correct classifications)
• Confusion matrix
• ROC curve (model’s discriminative power)

6. How to Build a Decision Tree in SPSS

• Go to: Analyze > Classify > Tree
• Select dependent and independent variables
• Choose algorithm: CHAID, CART, or QUEST
• Check “Display tree diagram”
• Click “OK”

7. How to Report in Your Thesis

“Decision tree analysis showed that study time was the most distinguishing variable for exam success. The model correctly classified 84% of students. According to the tree structure, students who study more than 10 hours per week have a 92% success rate.”

8. Decision Tree vs. Logistic Regression

9. Conclusion

Decision trees are a powerful and understandable method for classification and prediction in social sciences. Their visual structure not only aids analysis but also helps present findings effectively. Using decision trees in your thesis can be a strong choice both academically and practically.