Study Design and Logistics - Wetland Techniques - page 34

Description; (3) Responsible Agency and Participating Organizations; (4) Project
Organization Roles and Responsibilities; (5) Permits for Collection of Environ-
mental Measures; and (6) History, Previous Studies, and Regulatory Involvement.
The Project Data Quality Objectives section ensures that data quality and data
management are sufficient to achieve the objectives of the study. The section Field
Study Design/Measurement Protocols details how data are to be collected (i.e.,
variables measured) for a suite of abiotic and biotic features. Included in the section
Field Preparation and Documentation are details related to data management such
as (1) Field Preparation; (2) Field Notes (e.g., logbooks, data sheets and forms, and
photographs); (3) Documentation of Sample Collections; (4) Labeling of Sample
Collections; and (5) Field Variances. The section Quality Control for Samples
Collected for Off-Site Analysis details handling of samples to prevent contamina-
tion and confirmation of lab analyses (i.e., collection of field samples and transport
to a laboratory for analyses). Details related to field samples are provided in the
section Field Sample Collection Protocols for Off-Site Analyses , which can be the
most important section for wetland studies. Details related to laboratories are found
in the sections Laboratory Analyses and Section and Sample Shipment of Off-Site
Laboratory .
Quality Control is practiced by any entity producing a product. Industry has
quality control guidelines and practices to ensure products are functional and within
a margin of acceptable variation. That is, identification of defects in products after
development but before release. Quality Control is a system of routine technical
activities that measures and controls the quality of the inventory as it is being
developed. Most Quality Control systems are designed to: (1) provide routine and
consistent checks to ensure data integrity, correctness, and completeness; (2) iden-
tify and address errors and omissions; and (3) document and archive inventory
material and record all QC activities (Penman et al. 2006 ).
In scientific investigations, Quality Control is project- and method-specific such
that development of a Quality Control plan is difficult to generalize. Examples of
items to include in a Quality Control plan are (1) equipment monitoring and
recalibration, (2) periodic checks for data errors and transcription accuracy,
(3) ensuring software and hardware are working correctly, (4) checking integrity
of stored data, (5) retraining of technicians and anyone handling or analyzing
samples, and (6) confirming that safety protocols are being followed. Therefore,
one must identify all fundamental components of a study design and produce a
Quality Control plan that addresses each and maintains the highest possible stan-
dard of data integrity and accuracy while maintaining a safe environment.
As a final check of the accuracy of the data prior to analyses, one should
calculate descriptive statistics (e.g., mean, range, minimum value, maximum
value, and variance) or conduct outlier analysis (Barnett and Lewis 1994 )to
identify extreme values that are inconsistent with the other data and likely to be a
result of an error in transcribing data. However, one must have a prepared approach
to statistical analyses prior to checking the data to ensure that perceived patterns in
the descriptive analyses do not influence subsequent analyses, which can produce
spurious conclusions. There is a simple web-based application for the Grubbs' test