Nursing Research Analyzing Qualitative Data Qualitative Data, Essay

Nursing Research Analyzing Qualitative Data

Qualitative data, which usually consists of narrative materials (Polit & Beck, 2008, p. 507), is analyzed by three major styles. Template analysis involves development of a guide to which gathered data is applied and adjustment of the guide as more data is applied. This method tends to be interpretive rather than statistical. Editing analysis involves interpretation of data for "meaningful segments," then development of a category scheme with codes according to those segments, then a search for patterns that may connect the categories. Immersion/crystallization analysis is a reflective, subjective interpretation of data by the researcher's immersion in the data, and is rarely used in nursing research (Polit & Beck, 2008, p. 508).

Quality and integrity in qualitative studies are significant issues for the health care industry and have been addressed by numerous theorists. One synthesized method to probe quality and integrity employs primary and secondary criteria for scrutiny during studies and during post hoc assessments of studies. Primary criteria during both phases consist of probing questions regarding: credibility, authenticity, criticality and integrity. Secondary Criteria during both phases involves probing questions regarding: explicitness; vividness; creativity; thoroughness; congruence and sensitivity (Polit & Beck, 2008, p. 541).

Three statistical processes for use with data are categorizations of data according to Nominal, Ordinal and Interval measurements. Nominal measurement, the...

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556-557). Ordinal measurement sorts and ranks objects according to set criteria but does not meaningfully measure differences between different rankings, for example, the Shin study's arrangement of data according to rankings of "precontemplation, contemplation and active" criteria. Interval Measurement also uses rank-ordering and additionally employs "equivalent distance" between the ranks, allowing measurement of the differences between ranks (Polit & Beck, 2008, pp. 557-558).
Two ways to determine the effectiveness of statistics are Absolute Risk Reduction and Relative Risk Reduction. Absolute Risk Reduction essentially compares two risks by subtracting the absolute risk ("the proportion of people who experienced an undesirable outcome in each group" (Polit & Beck, 2008, p. 572)) for a treated group from the absolute risk for an untreated group. For example, David A. Alter's Therapeutic lifestyle and disease-management interventions: pushing the scientific envelope (Alter, 2007) measured the difference in global health risk between cigarette smokers who received smoking cessation intervention vs. cigarette smokers who received no such intervention. Relative Risk Reduction shows the proportion of untreated risk reduced through intervention and uses a formula whereby the Absolute Risk Reduction is divided by the absolute risk of the control group. For example, Can a statin neutralize…

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Multivariate Statistics is an area of statistics concerned with the collection, analysis and interpretation of several statistical variables at once. While statistics may be artificially confined for convenience sake, health care actually involves complex relationships of variables for patients themselves, within a single health care institution, within a group of health care institutions, and within the entire health care system. Multivariate statistics observes and analyzes several of these variables at once using several types of tests for various purposes.

Multivariate Statistics analysis is integrated in quantitative analysis through a number of tests to compare a number of variables in complex relationships. Tests used in multivariate statistics include: multiple regression/correlation tests, used to understand the effects of at least 2 independent variables on one continuous dependent variable (Polit & Beck, 2008, p. 614); analysis of covariance (ANCOVA), which compares the means of at least two groups with a single central question (Polit & Beck, 2008, p. 624); multivariate analysis of covariance (MANCOVA), which involves controlling covariates -- or extraneous variables -- when the analysis involves at least two dependent variables (Polit & Beck, 2008, p. 627); discriminant function analysis, which involves using a known group to predict an unknown group with independent variables (Polit & Beck, 2008, p. 628); canonical correlation, which involves testing one or more relationships between two sets of variables (Polit & Beck, 2008, p. 638); logistic regression, which predicts the probability of an outcome based on an odds ratio (Polit & Beck, 2008, p. 640).

Inferential Statistics assists in


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