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A Toolbook for Quality Improvement and Problem Solving (contents)

How to measure

The Quality Toolbook > Measurement > How to measure

Stratification | Sampling | Recording data


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When taking measures, there are several approaches which can help to ensure that the right data is selected and collected in a way that helps with the subsequent analysis.


When investigating a problem, a single general measurement is often insufficient and can cloak useful information. By measuring the situation in a number of different ways (stratifying or segmenting it), one or more 'cuts' may reveal new information that will allow specific corrective action to be identified, as in Fig. 1.

Common measurements used in stratifying data include:

  • Raw materials and completed products
  • Machines and tools
  • People
  • Processes and actions within them
  • Time
  • External factors, such as temperature and season


Fig. 1. Significant stratification in a Scatter Diagram


For example, a customer support organization counts the number of calls about each product, and find that a heater product is receiving a very high call rate. They have identified a problem, but cannot find out why without making more measurements. They therefore stratify the calls by taking intrusive measures, asking customers questions about suspected causes, such as the type of problem (failure, cutout, etc.), customer (age, occupation, etc.), how they are using the product (indoors/outdoors, hours of usage per day, etc.) and so on.


In order to know exactly how a set of measured items behaves, they must all be measured, such as when determining the distribution of the values of a batch of electrical resistors. However, this is seldom possible, for several possible reasons:

  • There is a significant cost in measurement, for example when there are a large number of items or when it takes a long time to measure each.
  • Not all items may be available for measurement.
  • There may always be more items (an infinite population).
  • Measuring the item effectively destroys it.

In such cases, a limited sample may be measured, from which the characteristics of all other items (the population) are deduced. In order to be able to do this reliably, there are two factors that must be taken into account:

  • The sample must be large enough to contain a representative set of items from the population, to enable an accurate extrapolation for all other items (see below).
  • The sample must be selected entirely at random, to ensure that no biases (intended or not) result in an incorrect picture of the rest of the population.



Fig. 2. Using a sufficient sample size


Most tool descriptions identify the size of samples that need to be taken to ensure a representative sample, so knowledge of statistics is not essential, although a deeper understanding in this area (or access to someone with this knowledge) can be very useful.

For more information on sampling calculations, go Google!

Recording data

When actually measuring data, it is important that the accuracy of the data is maintained through careful measurement and accurate instruments. This is best achieved through the use of a clearly defined data collection process.

It is usually useful to collect not only the data that is to be used, but also information about the situation in which it was collected. This may include:

  • The name of the person collecting the data.
  • The date and time of collection.
  • The method or process used for collecting data.
  • Identification of any instruments used during data collection.

Where the data is to be collected by hand, then a Check Sheet may be designed to ease both the recording and interpretation of data.

A variable often overlooked when recording data is the person doing the job. The best way of reducing any potential variation from this source is through training. This need not be complex or long, but it should be enough for the person to understand how to use any instruments, operate any machines and reliably record all requisite data. It can help if they know how the information is likely to be used afterwards, as a fear that the information may be used to their disadvantage can tempt them to tamper with it.



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