Quality Progress - January 2016 - 17
All About Data
e can't be certain, but Lt.
Commander Data, a character on "Star Trek: The Next
Generation," may have derived his
name from his ability to acquire and
process data critical to the mission of
the United Star Ship Enterprise. Clearly,
the series' production staff believed the
importance of data would continue
well into the future.
As a quality practitioner, you owe it
to yourself to gain a better understanding of the types of data, data stratification and data collection.
Types of Data
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-never a decimal fraction.
Typically, the question of whether
something has a particular attribute
can be answered with either a yes or
no. In working with products, services
and processes, items are often classified as good vs. bad, accept vs. reject
or go vs. no-go. When you are dealing
with defects or parts returned for
rework or scrap, you are dealing with
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, 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.
Now you need to consider whether
the data should be stratified. The purpose of data stratification is to convert a
heterogeneous population into a collec96
I JANUARY 2006 I www.asq.org
by Jack B. ReVelle
tion of homogeneous subpopulations.
This separation process facilitates those
studies or analyses of the heterogeneous population from which statistical
samples may be drawn.
Data stratification can include the
analysis of a population of machines to
Attribute and variable
data, data stratification
and data collection-
it's best to know them
determine which types create specific
kinds of defects or excessive variation
and studies of a population of employees to identify the needs and expectations of each category of employees.
Once you identify the population of
concern, determine the various types
of categories that exist in it, such as
size, age, supplier(s), color, weight,
distance, gender and cost. Next, divide
the population according to the pertinent categories. Then, as you collect
data regarding the population, record
the categorical information about the
sampled units using a tally sheet or
some other type of data table.
Data Collection Strategy
We collect data to help make better
decisions. Better decisions are made by
reducing uncertainty instead of making
guesses, going with a gut feeling or
even using common sense. Data are
facts, but they are not information
ready to be used in making decisions.
For data to become ready for use,
they must lead to understanding. To
correct a problem, you need to understand its nature and causes. As data
are collected and compared with
desired performance levels, you will
learn more about the causes of a problem, what should be measured and
how it should be measured.
The following steps should be part
of your data collection strategy:
* Determine the purpose of the data
to be collected. Will they be used
to assess the status of a process or
a product? Will they provide a
basis for decisions about process
or product quality?
Determine the nature of the data
to be collected. Are they measurable (variable or continuous) or
are they counted (attribute or discrete)?
Determine the characteristics of
the data to be collected. Can the
data be easily understood by people who will evaluate product and
process improvement, including
Determine whether the data can
be expressed in terms that invite
comparisons with similar processes. Can the performance metric be
expressed as parts per million,
defects per million opportunities,
Cp or Cpk or 6 sigma?
Determine whether the data place
priority on the most important
quality influences and whether
the data are economical and easy
Determine the best type of data
gathering check sheet to use:
checklists, tally sheets or defect
Determine whether it will be possible to use random sampling or
necessary to use 100% data collection.
This column is adapted from pp. 33-35 of
Quality Essentials: A Reference Guide From A to Z,
published by ASQ Quality Press in 2004.
JACK B. REVELLE is a consulting statistician
at ReVelle Solutions LLC in Santa Ana, CA.
He earned a doctorate in industrial engineering
and management from Oklahoma State
University, Stillwater, and is an ASQ Fellow.
If you would like to comment on
this article, please post your remarks
on the Quality Progress Discussion
Board at www.asq.org, or e-mail
them to firstname.lastname@example.org.
January 2016 * QP 17
Table of Contents for the Digital Edition of Quality Progress - January 2016
According to Plan
Use Your Head
Stakeholder Management 101
All About Data
Eight Simple Steps
Which Six Sigma Metric Should I Use?
Turning ‘Who’ Into ‘How’
In the Beginning
Outputs and Outcomes
That’s So Random—Or Is It?
Improving a System
Putting It All on the Table
Know the Drill
It’s Fun To Work With an F-M-E-A
Solve Problems With Open Communication
Tell Me About It
Separate the Vital Few From the Trivial Many
To DMAIC or Not to DMAIC?
Breaking It Down
1 + 1 = Zero Defects
Curve Your Enthusiasm
Make a Choice
What Is a Fault Tree Analysis?
Successful Relationship Diagrams
The Benefits of PDCA
Return on Investment
The Art of Root Cause Analysis
Why Ask Why?
Get to the Root of It
Checks and Balances
Clearing SPC Hurdles
Supplier Selection and Maintenance
Building a Quality Team
Plan Experiments to Prevent Problems
Quality Progress - January 2016