Methodology: Data Analysis
Once data have been collected, the empirical researcher must make sense of those data. This can involve simply summarizing (describing) the data or it can involve drawing inferences, either from a sample to a population or about whether apparent relationships reflect more than random patterns. Although it may appear that data collection and data analysis are distinct stages in research projects, that is not necessarily true. With quantitative data, some preliminary analyses can be conducted after an initial subset of the data has been collected in order to determine if some adjustment to the data collection process might be needed. With qualitative data, the researcher is likely to start thinking about possible patterns in the data as the collection proceeds; however, the researcher will ultimately need to go back and systematically analyze the data to insure that tricks of human perception have not led to perceiving a pattern that is not actually present. As this brief introduction suggests, the broad goal of systematic data analysis, whether quantitative or qualitative, is a combination of pattern identification and pattern matching. More formal tools exist for the analysis of quantitative data than for qualitative data, but the goals of analysis are fundamentally the same.
The goals of statistical analysis of quantitative data are to describe the data and to draw inferences from the data. The inferences sought may be to a full population from which a sample was drawn, or to the nature of the relationships that exist either within the...
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