Module 6 Overview: This module focuses on the analysis and interpretation phase of research. We begin with data management strategies and software. Then we explore quantitative data and inferential analysis. Topics covered include processing data, analysis of variance, and data cleaning. Missing data, regression analysis, and structured and semi-structured analysis are also covered.
Data management is an interim step between data collection and data analysis. Proactive data management can help you detect potential problems when they can still be corrected. It also allows for the preliminary analyses that are often required, either for reports or for data and safety monitoring. Beginning researchers are likely to need guidance from expert researchers, software tech support, tutorials, and manuals as well as their consultant statistician when setting up all but the simplest databases, but these tasks become much easier with practice.
The basic data cleanup, replacement of missing data, visual representations, descriptive statistics, and bivariate association statistics described in this chapter are useful in the analysis of virtually every nursing research study. For some studies, the analysis is complete after accomplishing these basic steps. For other, more complex studies, it is necessary to continue with the more advanced analytic techniques such as inferential analysis. Whether the analysis is basic or advanced, you will want to have a good statistics textbook, a guide to whatever statistical analysis software you are using, and guidance from an expert researcher and/or statistician to help you complete your quantitative data analysis correctly.
The most structured approach to the analysis of qualitative data uses coding and quantifying of the qualitative data. Content analysis is a specific case of the quantification of qualitative data. At the other end of the continuum are three of the great traditions in qualitative research: ethnography, grounded theory, and phenomenological analysis. In between lie a variety of coding and thematizing analyses that operate within a semi-structured and unstructured framework. Each of these reflects a very different tradition, which needs to be kept in mind as you match your data to the appropriate analytic framework. Data collection and data analysis may occur virtually simultaneously in the most unstructured of these approaches, while the more structured analyses are done after the data has been collected and processed.
Discussion Question:
Discuss the focus of ethnographic analysis. How do you plan to account for such issues as bias, subjectivity, and objectivity in order to provide a quality and ethical ethnographic analysis? Give a few examples.
Your initial posting should be 250 to 350 words in length and utilize at least one scholarly source other than the textbook.
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