Review Jurnal Secondary Data
Author : Paul D. Turner
Title :
“Secondary Analysis of Qualitative Data”
Pub. Date :
March. 1997
Descriptors : Classification; Meta analysis;
Qualitative Research; Research Metodology; Synthesis
Identifiers :
Secondary Analysis
1.
Abstract
The
reanalysis of data to answer the original research question with better
statistical techniques or to answer new questions with old data is not uncommon
in quantitative studies. Meta analysis and research syntheses have increased with
the increase in research using similar statistical analyses, refinements of
analytical techniques, and the advent of computerized literature searches. No
analogous definition of secondary data analysis from a qualitative point of
view has been proposed, but the primary component would include analysis by a
researcher removed from the process to continue the original analysis to
address different questions or to use different methods to address the original
research question. Discussion is just beginning about the possibilities of
secondary analysis of qualitative data. A typology of secondary analysis of
qualitative data is proposed that includes secondary analysis, meta-analysis,
and collaboration for qualitative inquiry. A classification of models for
research synthesis for qualitative study can be conceived of as a series of
cells that embody the time of the analysis, reanalysis, and the data set or
sets. Because qualitative analysis is very time intensive, considerable savings
might be realized with reanalysis of existing data sets. Issues involved in the
accessibility of research, its validation, and the education of researchers are
discussed, as are concerns about the limitations of reanalysis of qualitative
studies. (Contains 2 tables, 4 figures, 4 charts, and 62 references.)
2.
Theory
a. Quantitative Secondary Data Analysis
The reanalysis of data for the purpose of answering the
original research question with better statistical techniques or answering new
questions with old data (Glass 1976).
While no analogous definition of secondary data analysis has
been offered from the qualitative perspective, the primary component
would include the following:
Analysis of qualitative data by one
removed from the process with the purpose of either continuing the original
research analysis, addressing different questions not addressed in the original
research, or using different methods to address the original research questions.
b. Meta-Analysis
"The statistical analysis of a large collection of
analysis results from individual studies for the purpose of integrating the
findings" (p.3).
In essence, meta-analysis is a term describing a variety of
statistical procedures used to aggregate and quantitatively summarize the
results of multiple studies on a common topic. The technique summarizes a set
of empirical findings (usually in terms of a measured "effect size")
and tests their distribution for sampling error as an explanation for the inconsistency.
Some methods (i.e., Hunter and Schmidt 1990) furthe adjust for correction of
results due to statistical or methodological artifacts inherent to the studies.
c. Research Synthesis
Research synthesis (integrated research review) is a process
of combining and comparing empirical research for the purpose of creating
generalizations. This process includes: the a priori formulation of hypotheses
and problems; the search and evaluation of the primary studies involved, and;
the analysis and interpretation of the integrative studies.
3.
Rationale
a. Review of Literature
Researchers may wish to gain new knowldege by comparing
across studies within a specific subject area of interest, or even reanalyze a
specific study of interest. The knowledge and insight gained by many
qualitative studies is not static. The knowledge gleaned in the original
research may be progressed by the new insight or "lens" of other
researchers. Likewise, such progress of knowledge may be realized through the
passing of time. New insights, theories, and studies may evolve which are very
pertinent and give an added perspective useful to the original research. Secondary
analysis would provide the opportunity to re-enter the original research and
develop a new layer of analysis.
b. Economy
Qualitative research is very time
intensive, beginning well before the data collection (e.g., issues of gaining
access) and continuing after (e.g., analysis and verification). It is not
uncommon for a qualitative database to represent years of work by the primary
investigator and substantial funding through a variety of sources (e.g.,
universities, grant agencies). Considerable time, money, and personnel could be saved
through secondary analysis of an already existent data base or a research team
approach. This may becoming a critical and viable issue for qualitative
researchers. The
attenuation of federal funding for research appears inevitable. The implications,
according to some, is that the future for many researchers lies in private
industry funding (Morone and Belkin, 1995). Such funding frequently comes with
inherent agendas which most likely include the economy of resources and a
historical predominance of quantitative research. While qualitative research is
recognized as useful, it is often viewed as prohibitive in terms of time,
personnel and money. Future funding for research, regardless of methodology,
will most likely have a strong component based on economic restraint related to
what useful outcomes are generated by such research.
c. Accessiblity
Gaining access through gatekeepers
can be difficult, depending on the focus of the study. Analysis of an already existent
database which has addressed such an issue may be advantageous in such cases.
Likewise, collaborating with individuals with access to an otherwise
restrictive field of interest may be the only way of obtaining the data
necessary for a specific study. There are cases which involve the study of rare
events or infrequent cyclic occurrences in a life cycle of an individual or
sample of interest. Secondary analysis may provide a unique opportunity to
access otherwise unique and scarce data.
d. Verification (Validation) Through
Triangulation
Triangulation is not a tool or a
strategy of validation, but an alternative to validation (Fielding and Fielding
1986, Denzin 1989a, 1989b, Flick 1992). Denzin (1978) and Janesick (1994) identified
five types of triangulation: data, investigator, theory, methods, and
disciplinary. The combination of multiple methods, data types, theory,
perspectives and observers in a single study is best understood, then, as a
strategy that adds rigor, breadth, and depth to any investigation. Unfortunately,
many qualitative researchers primarily think of triangulation in terms of data triangulation
and more recently, secondarily as methodological triangulation. With regards to investigator and
interdisciplinary triangulation, such approaches are usually incorporated into
an audit as a "post hoc" form of verification. I believe that such
forms of triangulation when incorporated into the methods of a research
synthesis aid in verification by narrowing the distance between the field and
the analyst(s).
e. Education/Training
The reanalysis of data from original
investigations can be fundamental in the training of researchers, evaluators,
and practitioners. In fact, it may be more ubiquitous to training than to research per se. If such use of secondary analyses
are successful in educating others regarding the solutions, or possible
solutions, to the problems which arise in collecting, analyzing, and interpreting
data, a subsequent outcome may be the improvement in the quality of primary
research.
4. Review
What is the quality of the data? The data needs to be
carefully scrutinized for its representiveness of the particular qualitative strategy
and its overall quality for credibility reasons. What is the format in which
the data is stored? Data accurately transcribed and imported into a qualitative
software package is more accessible, organized, and easier to work with than
the researcher's original data and notes, or their remnants after the original
analysis. Through the increased use of computer software for data analysis,
secondary data analysis may already be currently used more than realized. Are
there supplementary data types which may be helpful? As identified by Denzin
(1978), there are four basic types of triangulation; data, investigator, theory,
and methodological triangulation. The importance of data triangulation cannot
be over emphasized with regards to secondary data analysis. The use of all types of data
collected, regardless of the intended or original use in the study, should be evaluated
for their usefulness in narrowing the distance between the data collection and
the analyst.
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