How can you design a survey for cluster analysis?

1 Choose your clustering variables

The first step is to decide what variables you want to use to cluster your respondents. These can be demographic, behavioral, attitudinal, or psychographic variables, depending on your research objectives and the type of clusters you want to create. For example, if you want to cluster your customers based on their lifestyle, you might use variables such as hobbies, values, opinions, and interests. You should choose variables that are relevant, measurable, and consistent across your sample.

2 Design your questions

The next step is to design your questions that will measure the clustering variables. You should use clear, concise, and unbiased language, and avoid leading, double-barreled, or ambiguous questions. You should also use appropriate scales and formats, such as Likert scales, multiple-choice, or rating questions, to capture the variations and nuances of your respondents' answers. You should avoid open-ended questions, as they are difficult to code and analyze for cluster analysis.

3 Test your survey

The third step is to test your survey before launching it to your target population. You should conduct a pilot test with a small sample of respondents who represent your target market, and check for any errors, inconsistencies, or confusion in your questions, scales, or formats. You should also analyze the data from the pilot test to see if the clustering variables are reliable, valid, and discriminant, and if they produce meaningful and distinct clusters. You should revise your survey based on the feedback and results from the pilot test.

4 Collect your data

The fourth step is to collect your data from your target population. You should choose a sampling method that ensures that your sample is representative, diverse, and large enough to allow for cluster analysis. You should also choose a survey mode that suits your research budget, timeline, and objectives, such as online, phone, mail, or face-to-face. You should ensure that your data collection process is ethical, transparent, and compliant with the relevant laws and regulations.

5 Prepare your data

The fifth step is to prepare your data for cluster analysis. You should clean your data by removing any outliers, missing values, or errors that might affect the quality and accuracy of your analysis. You should also standardize your data by transforming the values of your clustering variables into a common scale, such as z-scores or percentages, to avoid any bias or distortion due to different units or ranges. You should also check the assumptions of cluster analysis, such as normality, linearity, and homogeneity of variance, and apply any necessary corrections or transformations.

6 Perform cluster analysis

The final step is to perform cluster analysis on your data. You should choose a clustering method that suits your data type, structure, and objectives, such as hierarchical, k-means, or fuzzy clustering. You should also choose a clustering criterion that determines how many clusters to create and how to assign data points to clusters, such as the elbow method, the silhouette method, or the gap statistic. You should evaluate the quality and validity of your clusters by using measures such as the within-cluster sum of squares, the between-cluster sum of squares, or the Dunn index. You should also interpret and label your clusters by using descriptive statistics, charts, or profiles that summarize the characteristics and differences of each cluster.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?