SPSS is a powerful statistical analysis tool that offers a variety of functions, including cluster analysis. However, many users are not familiar with how to effectively use the clustering features in SPSS. Cluster analysis in SPSS allows you to group cases based on their similarities, making it a valuable technique for data exploration and segmentation.
Here’s a step-by-step guide to using SPSS cluster analysis:
1. Go to **Analyze > Classify > Hierarchical Cluster** to open the hierarchical clustering dialog box, as shown in Figure 1.
2. In the dialog box, move the variables you want to use for clustering into the "Variables" field, as illustrated in Figure 2.
3. Place the variable that identifies each case (e.g., region) into the "Case Label" field. This will label each data point with the corresponding value, as shown in Figure 3.
4. Click the **Plot** button to configure the output graph. Check the option for a **dendrogram**, which shows the hierarchical clustering structure, as displayed in Figure 4.
5. In the settings, ensure the dendrogram is selected, as it visually represents the clustering process, as seen in Figure 5.
6. Click the **Method** button to choose the clustering method. The **Ward's method** is commonly used for its accuracy. You may also need to standardize the variables by converting them to Z-scores, as shown in Figure 6.
7. Click the **Save** button and specify the range of clusters you want to save (e.g., 3–8). SPSS will add new variables to your dataset indicating the cluster membership of each case, as shown in Figure 7.
8. Set the number of clusters you want to output (e.g., 3–8), then click **Continue**.
9. Finally, click **OK** to run the analysis and generate the results, as shown in Figure 8.
10. After running the analysis, you’ll see the **clustering process table**, which includes the **clustering coefficient**. This helps determine the optimal number of clusters by identifying where the largest jump in the coefficients occurs.
11. The **dendrogram** provides a visual representation of the clustering process. You can use it to decide how many clusters to form based on your specific needs, as shown in Figure 9.
The above steps demonstrate how to use SPSS for hierarchical clustering. Each stage plays a key role in understanding and interpreting the clustering outcomes.
**Case Data Source:**
This example uses data from 20 brands of 12-ounce beer, including variables such as calories, sodium content, alcohol content, and price. The dataset is taken from *SPSS for Windows Statistical Analysis* (data11-03). You can download the dataset for further practice.
**Question 1: Which variables should be used for clustering? – R-type clustering**
We have four variables, but not all may be necessary. To reduce complexity, we perform **R-type clustering** (variable clustering). By analyzing the **similarity matrix**, we find that alcohol content and calories are highly correlated (correlation coefficient = 0.903). We can choose one of them to avoid redundancy and reduce costs. In this case, alcohol content is selected as the representative variable.
**Question 2: How many categories can the 20 beers be divided into? – Q-type clustering**
Using **Q-type clustering**, we aim to group the 20 beers into several categories. We first standardize the data using Euclidean distance and test between 3–5 clusters. The final decision is made based on the dendrogram, and we choose to divide the data into **four categories**.
**Question 3: Are the clustering variables useful? – One-way ANOVA**
After clustering, we assess whether the chosen variables significantly contribute to the classification. Using **one-way ANOVA**, we find that all three variables (alcohol content, sodium, and price) show significant differences across clusters, confirming their importance in the analysis.
**Question 4: How to interpret the clustering results? – Mean comparison and descriptive statistics**
Finally, we describe the characteristics of each cluster. Using **mean comparisons** and **descriptive statistics**, we summarize the average values of each variable in every group. This helps define and explain the distinct categories formed during the clustering process.
This case study demonstrates how SPSS can be used for hierarchical clustering, R-type and Q-type clustering, ANOVA, and descriptive statistics—showing the power of combining multiple analytical methods for deeper insights.
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