Data Collection Techniques

Data Collection Techniques

Attribute and variable data, data stratification and data collection—

it’s best to know them all.

Data Collection Techniques

Attribute Data

Also known as discrete data are counted in whole numbers or integers.  An attribute is the presence or absence of a particular characteristic. The result will always be a whole number.  Attribute data are much easier to collect and record than are variable data but they don’t provide as much information about the subject items.

Variable Data

Variable data, also known as continuous data, are measurements from a continuous scale. They can be, and

frequently are, decimal fractions.  The accuracy of a measurement is a function of the level of sensitivity or  precision of the measuring instrument being used the more sensitive the instrumentation, the more precise the measurements. Variable data provide more information about product and process characteristics than attribute data, but they are more complex and time consuming to collect and record.

Data Stratification

This separation process facilitates those studies or analyses of the heterogeneous population from which statistical samples may be drawn.

Data Collection Techniques

Data Collection Strategy

We collect data to help make better decisions. For data to become ready for use, they must lead to understanding.

Data Collection Techniques

The following steps should be part of your data collection strategy:

1. Determine the purpose of the data to be collected. Will they be used
2. Determine the nature of the data to be collected.
3. Determine the characteristics of the data to be collected.
4. Determine whether the data can be expressed in terms that invite comparisons with similar processes.
5. Determine whether the data place priority on the most important quality influences and whether the data are economical and easy to collect.
6. Determine the best type of data gathering check sheet to use: checklists, tally sheets or defect concentration diagrams.
7. Determine whether it will be possible to use random sampling or necessary to use 100% data collection.

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