QUALITY
ASSURANCE
and
QUALITY
CONTROL
DATA
STANDARD
Standard
No.:
1­
XXX
Version
1
 
FINAL
DRAFT
July
26,
2005
This
standard
has
been
produced
through
the
Environmental
Data
Standards
Council
(
EDSC).

The
Environmental
Data
Standards
Council
(
EDSC)
is
a
partnership
among
EPA,
States
and
Tribal
partners
to
develop
and
agree
upon
data
standards
for
environmental
information
collection
and
exchange.
More
information
about
the
EDSC
is
available
at
http://
www.
envdatastandards.
net.
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
2
Foreword
The
Environmental
Data
Standards
Council
identifies,
prioritizes
and
pursues
the
creation
of
data
standards
for
those
areas
where
information
exchange
standards
will
provide
the
most
value
in
achieving
environmental
results.
The
Council
involves
Tribes
and
Tribal
Nations,
state
and
federal
agencies
in
the
development
of
the
standards
and
then
provides
the
draft
materials
for
general
review.
Business
groups,
non­
governmental
organizations,
and
other
interested
parties
may
then
provide
input
and
comment
for
Council
consideration
and
standard
finalization.
Draft
and
final
standards
are
available
at
http://
www.
envdatastandards.
net.

1.0
INTRODUCTION
This
Quality
Assurance
and
Quality
Control
Data
Standard
(
QA/
QC)
identifies
quantitative
statistics
and
qualitative
descriptors
that
are
used
to
interpret
the
degree
of
acceptability
or
utility
of
data
to
the
user.
The
standard
provides
a
common
set
of
data
components
and
data
elements
to
specify
Quality
Control
elements
that
may
be
acquired
during
field
or
laboratory
analysis.
These
data
components
and
data
elements
provide
a
reusable
template
for
Quality
Control
Information.
This
standard
does
not
establish
a
new
reporting
requirement
for
the
regulated
community
or
new
data
collection
requirements
for
EPA
programs.
It
does
however
require
that
EPA
information
exchanges
of
quality
control
information
conform
to
the
standard
once
EPA
adopts
and
implements
the
standard.

1.1
Scope
This
standard
provides
and
describes
quantitative
statistics
and
qualitative
descriptors
that
are
used
to
interpret
the
degree
of
acceptability
or
utility
of
data
to
the
user.

1.2
References
to
Other
Data
Standards
None.

1.3
Terms
and
Definitions
For
the
purposes
of
this
document,
the
following
terms
and
definitions
apply.

Term
Definition
Quality
Assurance
(
QA)
The
system
implemented
by
an
organization
which
assures
outside
bodies
that
the
data
generated
is
of
proven
and
known
quality
and
meets
the
needs
of
the
end
user.
This
assurance
relies
heavily
on
documentation
of
processes,
procedures,
capabilities,
and
monitoring
of
such.

Quality
Control
(
QC)
Those
operations
undertaken
in
the
laboratory
or
field
to
ensure
that
the
data
produced
are
within
known
measures
of
accuracy
and
precision.
These
operations
may
include
calibration,
method
blanks,
matrix
spikes,
blank
spikes,
surrogates,
duplicates,
system
checks,
as
well
as
others.

1.4
Implementation
Users
are
encouraged
to
use
the
XML
registry
housed
on
the
Exchange
Network
Web
site
to
download
schema
components
for
the
construction
of
XML
schema
flows
(
http://
www.
exchangenetwork.
net).
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
3
1.5
Document
Structure
The
structure
of
this
document
is
briefly
described
below:
a.
Section
2.0
Diagram,
illustrates
the
principal
data
groupings
contained
within
this
standard.

b.
Section
3.0
Quality
Assurance
and
Quality
Control
Data
Standard
Table,
provides
information
on
the
high
level,
intermediate
and
elemental
Quality
Control
and
Quality
Assurance
data
groupings.
Where
applicable,
for
each
level
of
this
data
standard
a
definition,
XML
tag,
note(
s),
example
list
of
values
and
format
are
provided.
The
format
column
may
list
the
number
of
characters
for
the
associated
data
element,
where
"
A"
specifies
alphanumeric
and
"
N"
designates
numeric.

c.
Data
Standard
Numbering:
For
purposes
of
clarity
and
to
enhance
understanding
of
data
standard
hierarchy
and
relationships,
each
data
group
is
numerically
classified
from
the
primary
to
the
elemental
level.
d.
Code
and
Identifier
metadata:
Based
on
the
business
need,
additional
metadata
may
be
required
to
sufficiently
describe
an
identifier
or
a
code.
A
note
regarding
this
additional
metadata
is
included
in
the
notes
column
for
identifier
and
code
elements.
Additional
metadata
for
identifiers
may
include:

 
Code
List
Identifier,
which
is
a
standardized
reference
to
the
context
or
source
of
the
set
of
codes
Additional
metadata
for
codes
may
include:

 
Code
List
Identifier,
which
is
a
standardized
reference
to
the
context
or
source
of
the
set
of
codes
 
Code
List
Version
Identifier,
which
identifies
the
particular
version
of
the
set
of
codes.

 
Code
List
Version
Agency
Identifier,
which
identifies
the
agency
responsible
for
maintaining
the
set
of
codes
 
Code
List
Name,
which
describes
the
corresponding
name
for
which
the
code
represents
e.
Appendix
A,
Quality
Assurance
and
Quality
control
Data
Standard
Structure
Diagram
illustrates
the
hierarchical
classification
of
the
QA/
QC
data
standard.
This
diagram
enables
business
and
technical
users
of
this
standard
to
quickly
understand
its
general
content
and
complexity.
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
4
2.0
QUALITY
ASSURANCE
AND
QUALITY
CONTROL
DATA
STANDARD
DIAGRAM
This
diagram
specifies
the
major
data
groups
that
may
be
used
to
identify
the
characteristics
and/
or
to
catalog
quality
assurance
and
quality
control.

Quality
Assurance
and
Quality
Control
Data
Standard
1.0
Data
Quality
Indicator
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
5
3.0
QUALITY
ASSURANCE
AND
QUALITY
CONTOL
DATA
STANDARD
TABLE
1.0
Data
Quality
Indicator
Definition:
The
quantitative
statistics
and
qualitative
descriptors
that
are
used
to
interpret
the
degree
of
acceptability
or
utility
of
data
to
the
user.

Relationships:
None.

Notes:
None.

XML
Tag:
DataQualityIndicator
Data
Element
Name
Data
Element
Definitions
Notes
Format
XML
Tags
1.1
Precision
A
measure
of
mutual
agreement
among
individual
measurements
of
the
same
property
usually
under
prescribed
similar
conditions.
This
is
the
random
component
of
error.
Example:
Relative
Percent
Difference
(
RPD
or
di),
where
X
is
the
primary
value
and
Y
is
the
duplicate:
100
2
/

)

(
×
+
 

=
i
i
i
i
X
Y
X
Y
di
A
Measurement
Precision
1.2
Bias
The
systematic
or
persistent
distortion
of
a
measurement
process
which
causes
error
in
one
direction.
Bias
will
be
determined
by
estimating
the
positive
and
negative
deviation
from
the
true
value
as
a
percentage
of
the
true
value.
Example:
Percent
Difference,
where
X
is
the
known
or
spiked
amount,
and
Y
is
the
measured
concentration:

100
×
 

=
i
i
i
X
X
Y
di
A
Measurement
Bias
1.3
Confidence
Interval
A
range
of
values
for
a
variable
of
interest,
e.
g.,
a
rate,
constructed
so
that
this
range
has
a
specified
probability
of
including
the
true
value
of
the
variable.
The
specified
probability
is
called
the
confidence
level,
and
the
points
of
the
confidence
interval
are
called
the
confidence
limit.
Confidence
limit
may
be
calculated
as:

(
)

X
t
value
stdev
n
±
_
*
/
A
ConfidenceInt
erval
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
6
Data
Element
Name
Data
Element
Definitions
Notes
Format
XML
Tags
1.4
Upper
Control
Limit
Value
of
the
upper
end
of
acceptable
range.
See
1.3
Confidence
Interval
A
UpperControl
Limit
1.5
Lower
Control
Limit
Value
of
the
lower
end
of
acceptable
range.
See
1.3
Confidence
Interval
A
LowerControl
Limit
1.6
Upper
Probability
Limit
Upper
limit
on
a
probability
interval
(
instead
of
a
confidence
interval),

which
represents
the
expected
upper
limit,
which
should
include
95%
of
all
the
individual
measurements
of
a
population.
Probability
limit
is
calculated
as:

X
z
value
stdev
±
(
_
*
)

(
using
95%
z
value
which
is
1.96)

Example:
"
a
reporting
organization
is
said
to
be
outside
of
the
acceptable
quality
assurance
limits
if
either
of
the
upper
probability
limit
or
lower
probability
limit
is
outside
of
the
acceptable
quality
assurance
limit.
A
UpperProbabi
lityLimit
1.7
Lower
Probability
Limit
Lower
limit
on
a
probability
interval
(
instead
of
a
confidence
interval),

which
represents
the
expected
lower
limit,
which
should
include
95%
of
all
the
individual
measurements
of
a
population.
Probability
limit
is
calculated
as:

X
z
value
stdev
±
(
_
*
)

(
using
95%
z
value
which
is
1.96)

Example:
"
a
reporting
organization
is
said
to
be
outside
of
the
acceptable
quality
assurance
limits
if
either
of
the
upper
probability
limit
or
lower
probability
limit
is
outside
of
the
acceptable
quality
assurance
limit.
A
LowerProbabi
lityLimit
Quality
Assurance
and
Quality
Control
Data
Standard
Std
No.:
1­
XXX
Version
1
 
Final
Draft
July
26,
2005
Page
7
Appendix
A
Quality
Assurance
and
Quality
Control
Data
Standard
Structure
Diagram
1.0
Data
Quality
Indicator
1.1
Precision
1.2
Bias
1.3
Confidence
Interval
1.4
Upper
Control
Limit
1.5
Lower
Control
Limit
1.6
Upper
Probability
Limit
1.7
Lower
Probability
Limit
Quality
Assurance
and
Quality
Control
Data
Standard
