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# Nursing Data Analysis In Research

**Data Analysis**

Data analysis

is a systematic method of examining data gathered for any research

investigation to support conclusions or interpretations about the data.

Although applicable to both qualitative and quantitative research data analysis

is more often associated with quantitative research.

Quantitative data analysis

involves the application of logic and reasoning through the use of statistics,

an applied branch of mathematics, to numeric data. Qualitative data analysis

involves the application of logic and reasoning, a branch of philosophy, to

nonnumeric data.

Both require careful execution and are intended to give

meaning to data by organizing disparate pieces of information into

understandable and useful aggregates, statements, or hypotheses.

## Specific Statistical Tests To Be Used

Statistical

data analysis is based in probability theory and involves using a number of

specific statistical tests, or measures of association between two or more

variables.

Each of these tests or statistics (e.g., t, F, B, z, o, 7, etc.) has

a known distribution that allows the calculation of probability levels for

different values of the statistic under different assumptions that is, the test

(or null) hypothesis and the sample size, or degrees of freedom. Specific tests

are selected because they provide the most meaningful representation of the

data in response to the research questions or hypotheses posed.

The selection

of specific tests, however, is restricted to those for which the available data

meet certain required assumptions of the tests. For example, some tests are

appropriate for (and assume) nominal data, others assume ordinal data, and

still others assume an interval level of measurement.

Although each test has

its own set of mathematical assumptions about the data, all statistical tests

assume random sampling.

## Computer Programs In Data Analysis

Several

statistical computer programs (e.g., SPSS, SAS, LISREL, EQS) can aid the

investigator with the tedious and complex mathematical operations necessary to

calculate these test statistics and their sampling distributions.

These

programs, however, serve only to expedite calculations and ensure accuracy.

Because the investigator must understand the computer programs to use them

appropriately, there is a hidden danger in the ease with which one may execute

such programs.

For valid data analysis, the investigator must fully

understand the underlying statistical procedures and the implied assumptions of

these tests in order to apply them appropriately.

## Hypothesis Statistical Data Analysis

The logic of

null hypothesis statistical data analysis is one of modustollens, denying the

antecedent by denying the consequent. That is, if the null hypothesis is

correct, our findings cannot occur but our findings did occur, so the null

hypothesis must be false.

However, J. Cohen (1994) and others have

convincingly argued that, by making this reasoning probabilistic for null

hypothesis statistical testing, the original syllogism is invalidated.

Moreover, for decades scientists from different disciplines have questioned the

usefulness and triviality of null hypothesis statistical testing (see Labovitz,

1970; LeFort, 1993; Loftus, 1993; Rozeboom, 1960; Walker, A. M., 1986, for

examples from sociology, psychology, public health, and nursing).

Consequently,

increased attention to the factors that contribute to findings of statistical

significance is warranted and power, effect sizes (for substantive

significance), sample sizes, and confidence intervals are receiving in- creased

attention in quantitative data analysis.

## Difference In Qualitative And Quantitative Data Analysis

In contrast to

quantitative data analysis, which requires that the investigator assign a

numeric code to all data prior to beginning the analyses, qualitative data

analysis consists of coding words, objects, or events into coherent or

meaningful categories or themes as part of the actual data analyses.

Also,

because qualitative data analysis involves nonnumeric data, there are no

statistical probabilistic tests to apply to their coding.

## Historical Perspective of Data Analysis

Historically,

qualitative data coding has been done manually, but more recently computer

programs (e.g., NUDIST) have been developed to aid the investigator in this

laborious effort.

However, as with the computer programs for quantitative

analyses, those for qualitative data analysis are merely aids for the tedious

and error prone tasks of analysis. Using them still requires that the investigator

make the relevant and substantive decisions and interpretations about codes,

categories, and themes.

Quantitative

data analysis allows for statistical probabilistic statements to support the

investigator’s interpretations and conclusions. Qualitative data analysis

depends more exclusively on the strength and logic of the investigator’s

arguments.

Nonetheless, both types of data analysis ultimately rest on the

strength of the original study design and the ability of the investigator to

appropriately and accurately execute the analytic method selected.