How to Convert Attribute Data to Variable Data

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    Karim Mohsen


    I have a lot of Attribute data for my analysis (Pass/Fail), I want to do a data analysis for them , so I want to know how to change the attribute data to Variable data and which control chart do i use ?



    If the end goal is to put this into a control chart, many software will do this conversion for you.

    It’s down to what you want to measure. You can either measure a proportion % of defectives (fails) or a number of defectives.

    For proportion, looking at Defective data (Pass/Fail) – then you will state how many fails (defectives) you had, alongside the total sample size. I.e. You encountered 40 fails out of 300 total items. You would repeat this in subgroups until you have a sufficient total sample size. This can then be converted into a proportion (%). This would then go into a P chart.

    For Number of defectives, you can just number the amount of defectives over time and plot this into an Np chart.

    Usually P-chart is preferable as it puts it onto a completely variable scale that is also more representative in a proportion format.

    If you were looking at Defects instead of Defectives, you could use a C or U chart.


    Robert Butler

    When you say you “have a lot of Attribute data for my analysis (Pass/Fail)” there isn’t much anyone can offer because that statement doesn’t tell anyone what you mean by “a lot.”

    However, if we make the following assumptions:
    1. You take grab samples of a given size from your production process and you inspect each item in the sample and record the count of pass/fail for each sample.
    2. You want to use that data to construct predictive equations for various properties of your product.
    3. You want to use that data for process control.

    Then the situation is this
    1. For each sample you will have a measure of percent defective.
    2. The percentages are variable data
    a. You can build predictive models with this data with the outcome being percent defective (or, if you want, the reverse – percent accepted).
    3. Given that you have data in the form of the very first point under assumptions then the np chart would be a good choice for process control.

    …and now the yeah/buts

    Given that all of the above is true the big question is just what is your pass/fail data? That is are you just looking at something and saying pass/fail or are you looking at various attributes of a given sample and making a pass/fail judgement on each of the different attributes? If it is the latter then you are into questions concerning what it is that you want to do. If it is just a question of in or out of control then an aggregate of the pass/fails will work but it won’t be of much value with respect to process improvement.

    Given the above assumptions are all true and given that you want to do more than just look at in or out of control you will need to do some reading concerning np charts. I would recommend borrowing through inter-library loan a copy of Wheeler and Chambers – Understanding Statistical Process Control and reading the chapter Understanding Attribute Data Effectively.



    Thanks for this information.


    Chris Seider

    @rbutler brings up excellent points.

    See if you can quickly change your measurement system to a variable/continuous data system and confirm it’s performing with an MSA. You’ll solve your problem well and faster and learn more about the process if you are able.



    The above from Mr. Butler and Mr. Seider is on point. I would emphasize that pass/fail may mean that you have already converted variable data to an attribute, and thereby lost the variable measurement. The important question is how you determined pass/fail.

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