Data Analysis – Analyzing Data in Qualitative Research

What is Data Analysis?

Analysis is More than Coding, Sorting and Sifting.

Although some researchers suggest that disassembling, coding, and then sorting and sifting through your data, is the primary path to analyzing data / data analysis. But as other rightly caution, intensive data coding, disassembly, sorting, and sifting, is neither the only way to analyze your data nor is it necessarily the most appropriate strategy. It has been argued that they also fit the notice, collect, and think process invariably also belonging to the data analysis process.

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In the thinking process the researcher examines the things that have been collected. The goals of the data analysis are:

    1. To make some type of sense out of each data collection
    2. To look for patterns and relationships both within a collection, and also across collections, and
    3. To make general discoveries about the phenomena you are researching.

To use an analogy: After sorting the pieces of a jigsaw puzzle into groups, it is important to inspect individual pieces to determine how they fit together and form smaller parts of the picture (e.g., the tree part or the house part). This is a labor intensive process that usually involves a lot of trial and error and frustration. A similar process takes place in the qualitative data analysis. When analyzing data, one compares and contrasts each of the things that have been noticed in order to discover similarities and differences, build typologies, or find sequences and patterns. In the process one might also stumble across both “wholes” and, quite literally, holes in the data.

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While the jigsaw puzzle approach to analyzing data is frequently productive and fruitful, it also entails some risks and problems that also translate to qualitative data analysis. Experienced qualitative social scientists have always been aware of the potential problems, and organize their work to minimize the adverse effects. For example, when coding data, the simple act of breaking down data into its constituent parts can distort and mislead the analyst and distort the final data analysis. A serious problem is sometimes created by the very fact of organizing the material through coding or breaking it up into segments in that this destroys the totality of philosophy as expressed by the interviewee-which is closely related to the major goal of the study that informs the data analysis. A proper data analysis acknowledges this problem and, in fact, takes precautions already when first analyzing data.