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There are two types of data: variable and attribute. Both types are useful in measuring process performance by analyzing process problems, but they need to be treated differently.

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Quick reference

Data Types

There are two types of data: variable and attribute. Both types are useful in measuring process performance by analyzing process problems, but they need to be treated differently.

When to use

Whenever data is collected or analyzed – which can happen in all phases of a Lean Six Sigma project – the differentiation between the data types should be clear so that the data is handled correctly.

Instructions

Data is the set of real world facts about the problem or process that is being studied. Statistics are the numerical interpretation of those facts. We will spend many lessons discussing ways to interpret the data. But it is important to recognize that depending upon the category of the data, different types of statistical analysis can be done.

There are two categories of data: attribute and variable. Both are meaningful for our analysis.

Attribute data is used to indicate a status or condition of the process or problem. It is often expressed in descriptive terms such as: on/off, true/false, Brazil/Argentina/Chile, First place/Second place/Third place, or Good/Better/Best. It's often easier to collect than variable data because typically there is no measurement  to be made; it's just a check of the status. Attribute data may be expressed in numerical terms, but it is still a status. For instance, on/off may be represented as 1/0.

Variable data is measured data along some scale. Because it is along a scale, it can take on many values, essentially an infinite number of values depending upon the ability of the scale to discriminate. Variable data is considered to be richer data than attribute because the analysis can indicate values moving and drifting.  The movement can be analyzed to provide insight into process stability. Examples of variable data include time, temperature, distance, dimensions, and percentages.

Hints & tips

  • When collecting variable data, be sure you are using consistent units. There have been some disastrous analysis because one team member measured in meters and another with feet and the two data sets were combined without converting to common units.
  • During the Measure phase, more data is good. Collect any and all data, even if you don’t know whether you will need it or not. When you get to the Analyze phase, it may become very valuable.
  • Some in the Lean Six Sigma community treat attribute data with disdain because it is not as rich. However, it is much easier to collect. In addition, when the problem is a special cause problem – meaning a unique or isolated event - the attribute data will often provide a clearer picture of what is happening. Attribute data can identify massive changes very quickly, whereas variable data is much more powerful for finding trends and slow changes.
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  • 00:05 Hi, this is Ray Sheen.
  • 00:07 During the measure phase, we collect data in order to understand the problem.
  • 00:11 And I've been talking a lot about data, so let's just take a few moments and
  • 00:15 understand what we mean by that term.
  • 00:19 Data characteristics help us to understand how to work with the data.
  • 00:23 Data is information and facts about the world.
  • 00:26 In our case it'll be facts about things happening within and
  • 00:29 around our process and problem.
  • 00:32 Now we can differentiate data from statistics.
  • 00:34 Statistics are not facts or rather statistics are an analysis of facts.
  • 00:39 These statistics give us an opportunity to interpret the characteristics of the data
  • 00:43 concerning the problem or process.
  • 00:46 The type of data analysis and
  • 00:48 statistics will vary based upon the nature of the data itself.
  • 00:52 Then it can be characterized as either attribute data or variable data.
  • 00:56 Either type of data can be used in our Lean Six Sigma analysis.
  • 01:00 You may have heard from others that you have to use variable data, not the case.
  • 01:05 We'll be using both.
  • 01:07 Let's look at each in order to understand them.
  • 01:10 I'll start with attribute data.
  • 01:13 Attribute data is typically a count of things or status of things.
  • 01:17 It is normally much easier to collect because most of the time,
  • 01:20 there's no need for measuring equipment.
  • 01:22 The data describes things like a position or a status.
  • 01:26 Examples would be true or false, on or off,
  • 01:29 the sequence of items that are first, second or third.
  • 01:33 Account of occurrences, such as the number of defects or
  • 01:36 the number of calls, which country, Brazil, Argentina, or Chile.
  • 01:40 Which product A, B or C or status of excellent good, fair or poor.
  • 01:46 Attribute data has limited number of possible values.
  • 01:49 The switch is either on or off.
  • 01:52 The manufacturing facility is either in Brazil or Argentina.
  • 01:56 The car is either red, blue or green.
  • 02:00 Now you can't say that attribute data is any that is not numeric.
  • 02:03 Because we will often use a number to reflect the status.
  • 02:07 On as one off as zero, first is one, second is two and third is three.
  • 02:13 But even though we may use numbers there's no meaningful information
  • 02:16 between the number values.
  • 02:19 If on is 1 and off is 0, then what does .37 indicate?
  • 02:24 The question doesn't even make sense.
  • 02:27 That's because this is not a measured value but rather a status indicator.
  • 02:33 Let's contrast attribute data with variable data.
  • 02:36 Variable data is considered by statisticians to be much richer data
  • 02:40 There are many more statistical analysis that could be done with variable data.
  • 02:45 Variable data exists on a continuous scale.
  • 02:48 That means that there are fractions and decimals between the major values.
  • 02:52 Examples of this kind of data is time, distance,
  • 02:56 temperature, almost any dimension of a product such as length, width and height.
  • 03:01 Percentages are also variable, and
  • 03:03 that's a way that we can often transpose attribute data into variable data,
  • 03:07 by determining the percentage in each of the attribute categories.
  • 03:12 Variable data can take on an infinite number of values.
  • 03:15 It is limited only by the precision of the measuring equipment that is being used.
  • 03:20 Which means that there can be meaningful data values between data points.
  • 03:24 Data values like the median or average value may be unique from any of the data
  • 03:28 points, but still is a very significant number in some of our analyses.
  • 03:34 One caution when collecting variable data from multiple data sets.
  • 03:37 Be sure the measurement system is in the same units, so
  • 03:41 if you're measuring temperature make sure you know if it is centigrade or fahrenheit.
  • 03:45 If measuring distance, is it in meters, miles, or furlongs?
  • 03:52 In your project you'll be collecting a lot of data,
  • 03:55 some of them will be attribute and some will be variable, both will be very useful

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