Categorize your employee attribute data

Employee attributes are incredibly valuable when you are analyzing the feedback from your organization as they allow you to analyze and compare different groups. This is why it is so important that you set them up correctly when you are designing your survey and preparing your distributions.

Employee attributes such as age and tenure are two attributes that we highly recommend including in your analysis. However, these attributes are often exported from your HR data as individual values for each employee and performing analysis on continuous values makes it challenging to compare different groups effectively. This is why we recommend you segment this kind of data into meaningful categories or ‘buckets’.

What’s the benefit of categorizing data?

• Employee privacy will be better protected because you are using broad categories instead of specific values
• Your analysis will be simplified with categories making it easier to compare groups and identify trends
• You will be able to use these categories as filters so that you can analyze the answers and behaviours of specific segments

Categorizing age:

Instead of having the exact age of each employee, assign each employee to an age range. You can determine what the ranges should be. The guide below lists how you might want to define these age buckets.

Age ranges:

• 16 – 24
• 25 – 34
• 35 – 44
• 45 – 54
• 55 – 64
• 65 – 74
• 75+

Categorizing tenure:

Instead of having the exact tenure calculated for each employee, assign them to the appropriate tenure range. The guide below lists how you might want to define tenure buckets.

Tenure ranges:

• 0 – 0.5 years
• 0.5 – 1 year
• 1 – 2 years
• 2 – 5 years
• 5 – 10 years
• 10 – 20 years
• 20+ years

The guidelines above work with other attributes as well.

Small work in establishing your data beforehand will pay big dividends later in the process when you are analysing survey results, and tracking trends over time.