Measurement
We use measures in a project to track progress, communicate your story and assess whether changes are being maintained. We can see whether the changes we make are improvements or not.
The Quality Improvement Journey
Why measurement is important
Measurement is core to your improvement journey. It demonstrates if your changes have lead to improvement, and track your progress towards your project aim.
Watch the animation below for an overview of Measurement.
Topics in Measurement for Improvement
Introduction to Measurement for Improvement
- Why is measurement necessary?
- The difference between data for improvement and data for other purposes
- Qualitative and quantitative data
- What is variation and why we need to understand it
- Types of variation
- How to look at variation
- SPC charts
- Reacting to variation
- Improvement project measures
- Deciding what to measure
- Types of data
- Defining your measures
- Planning your data collection
- Sampling
- Collecting and storing your data
- The importance of good data visualisation
- Principles of data visualisation and design
- Choosing the right chart for the job
- Tips for good charts
- Presenting charts to a group
- The importance of presenting data in a way that is easy to understand
- Choosing the right layout
- Helping others engage with data
Tools for Measurement for Improvement
A measurement plan sets out details for each measure proposed for an improvement project.
A run chart is a line graph of data plotted over time. By collecting and charting data over time, you can find trends or patterns in the process.
Statistical Process Control (SPC) charts are simple graphical tools that enable process performance monitoring.
A funnel plot is a chart that helps to understand variation within a system.
A graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.
A Pareto Chart is a tool to help you understand your system.
A histogram is a plot that lets you discover, and show, the underlying frequency distribution (shape) of a set of continuous data.