Page 136 - Encyclopedia of Nursing Research

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assume) nominal or categorical data, others
Data analysis assume ordinal data, and still others assume
an interval level of measurement. Although
each test has its own set of mathematical
Data analysis is a systematic method of assumptions about the data, all statistical
examining data gathered for a research tests assume random sampling.
investigation to support interpretations and Several statistical computer programs
conclusions about the data and inferences (e.g., SPSS, SAS) are available to aid the inves-
about the population. Although applicable tigator with the tedious and complex math-
to both qualitative and quantitative research, ematical operations necessary to calculate
data analysis is more often associated with these test statistics and their sampling distri-
quantitative research. Quantitative data butions. These programs, however, only serve
analysis involves the application of logic and to expedite calculations and ensure accuracy.
reasoning through the use of statistics, an There is a hidden danger in the ease with
applied branch of mathematics, to numeric which one may execute these computer pro-
data. Qualitative data analysis involves the grams, and the investigator must understand
application of logic and reasoning, a branch the computer programs to use them appropri-
of philosophy, to nonnumeric data. Both ately. To ensure that data analysis is valid and
require careful execution and are intended appropriate for the specific research question
to give meaning to data by organizing dis- or hypothesis, the investigator also must fully
parate pieces of information into under- understand the statistical procedures them-
standable and useful aggregates, statements, selves and the underlying assumptions of
or hypotheses. these tests.
Statistical data analysis is based on prob- Most quantitative data analysis uses a
ability theory and involves using specific null hypothesis statistical test approach. The
statistical tests or measures of association logic of null hypothesis statistical testing is
between two or more variables. Each of these one of modus tollens, denying the anteced-
2
tests or statistics (e.g., t, F, β, χ , φ, η, etc.) has a ent by denying the consequent. That is, if
known distribution that allows calculation of the null hypothesis is correct, our nonzero
probability levels for different values of the findings cannot occur, but because our find-
statistic under different assumptions—that ings did occur, the null hypothesis must be
is, the test (or null) hypothesis and the sam- false. Cohen (1994) and others, however, have
ple size or degrees of freedom. argued convincingly that by making this
Specific tests are selected because they reasoning probabilistic for null hypothesis
provide the most meaningful representation statistical testing, we invalidate the origi-
of the data in response to specific research nal syllogism. Despite decades of articles by
questions or hypotheses posed. The selec- scientists from different disciplines ques-
tion of specific tests, however, is restricted to tioning the usefulness and triviality of null
those for which the available data meet cer- hypothesis statistical testing (for examples
tain required assumptions of the tests. For from sociology, psychology, public health,
example, some tests are appropriate for (and and nursing, see Labovitz, 1970; LeFort, 1993;