One of the ways that people like to classify their KPIs and performance measures is by whether they are quantitative or qualitative. Should we do it? And if we do, are we doing it right?
The distinction between quantitative and qualitative measures is often misunderstood. Really if you’re measuring anything, you’re gauging the amount to which it’s happening. And numbers are the essential building blocks of amounts. Even when you use rating scales to turn attitudes into numbers, you’re doing it to gauge an amount. So, technically, every measure is quantitative.
In the field of statistics, we distinguish variables as qualitative (or attribute) when those variables are not gauging an amount but rather are simply putting things into buckets. The buckets are classifications like gender or market segment or geographical region or product group.
Qualitative variables aren’t performance measures. But they are used to help us analyse our measures. We can slice Customer Satisfaction Rating into product groups to explore which products to prioritise for improvement. We can dice Employee Engagement Ratio by profession and location to explore where morale might need boosting.
In the field of statistics, we distinguish two types of quantitative variables: continuous and discrete. Continuous variables can take any value (including decimals) over a range, and are measured in units like kilograms, hours and minutes and seconds, dollars and cents, metres.
Discrete variables are generally counts of things like complaints, accidents, new customers – anything that takes an integer value. This includes rating scales for measuring attitudes, such as satisfaction or agreement on a 10-point scale.
Performance measures can be based on either continuous or discrete variables. Measures such as Average Delivery Cycle Time or Net Profit and Non-recyclable Mass Sent to Landfill and Average Kilometres Travelled are based on continous variables. Measures such as Average Customer Satisfaction Rating and Number of Lost Time Injuries and Percentage of Projects Completed On-Time are based on discrete variables.
Both types of performance measures – continuous and discrete – are equally useful.
Do you agree that discrete measures are as useful as continuous measures? What’s your reasoning? Share your suggestions on the blog.
Thank you for starting this important discussion.
While working with my clients, what I usually mean by Qualitative Measure is a Qualitative Expression turned into numbers.
Lets take the example of a team working on embedding a tool such as Balanced Scorecard in a company. Now their performance can be measures quantitatively by many expressions such as # people trained, % departments with dashboards etc. And you can measure their performance very well by asking their functional head, who has an experience of more than 30 years in the same company, on how they did.
Which one works better for you – well, you make a call. I will, generally use Quantitative stuff for input/process/output measures and Qualitative for outcome.
This sounds Nadeem like a bit of confusion between the definition of qualitative measure versus quantitative measure. You can certainly measure quality-related outcomes but that’s not the same as a qualitative measure. You can quantitatively measure quality-related outcomes.