Page 140 - Encyclopedia of Nursing Research

DATA STEWARDShiP n 107

observational data collected in naturalistic replaced with correct values or assigned to
settings to achieve a more complete and holis- the missing values category. outliers must be
tic perspective on the phenomena in which investigated and dealt with. if a categorical D
they are interested. in quantitative research, variable is supposed to have four categories
especially in testing the effects of clinical but only three have adequate numbers of sub-
interventions, nurse researchers often trian- jects, one must decide about eliminating the
gulate biophysiological and self-report data. fourth category or combining it with one of
For the past two decades, momentum the others. if continuous variable are skewed,
has been gaining for mixed-method research, data transformations may be attempted or
which involves the triangulation of qualita- nonparametric statistics used.
tive and quantitative data in a single study or once each variable has been inspected
a coordinated set of studies. Mixed-method and corrected where necessary, new vari-
researchers often endorse a pragmatist ables may be created. This might include the
stance in which the research question drives development of total scores for a group of
the methods of data collection rather than items, subscores, and so forth. Each of these
the methods driving the question. it seems new variables also must be checked for outli-
likely that nurse researchers will continue ers, skewness, and out-of-range values. The
to expand their repertoire of data collection creation of some new variables may involve
methods, their use of supportive technologi- the use of sophisticated techniques such as
cal tools, and their blending of different types factor and reliability analyses.
of data as a means of strengthening evidence Before each statistical test, the assump-
to guide their practice. tions underlying the test must be checked.
if violated, alternative approaches must be
Denise F. Polit sought. Careful attention to data manage-
ment must underlie data analysis. it ensures
the validity of the data and the appropriate-
ness of the analyses.
Data ManageMent
Barbara Munro

Data management is generally defined as the
procedures taken to ensure the accuracy of
data, from data entry through data transfor- Data stewarDship
mations. Although often a tedious and time-
consuming process, data management is
absolutely essential for good science. Data stewardship refers to the responsibil-
The first step is data entry. Although this ity and the accountability to manage uses of
may occur in a variety of ways, from being data that include but are not limited to data
scanned in to being entered manually, the collection, viewing, storage, exchange, aggre-
crucial point is that the accuracy of the data gation, and analysis. health data steward-
be assessed before any manipulations are ship is a responsibility, guided by principles
performed or statistics produced. Frequency and practices, to ensure the knowledge-
distributions and descriptive statistics are able and appropriate use and reuse of data
generated. Then each variable is inspected, as derived from an individual’s personal health
appropriate, for out-of-range values, outliers, information. health data stewardship has
equality of groups, skewness, and missing become increasingly important because of
data. Decisions must be made about dealing the increased use and value of electronic
with each of these. incorrect values must be health data and information technology as