The process of analyzing qualitative data is vital in social research just as that of quantitative data analysis. Qualitative data according to Grinnell, Williams and Unrau (2012), is that data that measure a quality or kind. This type of data is generally descriptive and non-numeric in nature and it is based on spoken or written words and on behaviors that can be observed. Field notes, interview transcripts and audio and video recordings are a few examples of qualitative data.
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Grinnell et al break down the process of qualitative data analysis into three major phases namely: defining of the analysis, classifying of data and making connections. During the phase of defining of the analysis, the researcher needs to set the goals of his analysis, identify suitable and adequate information and then settle on the best method of capturing, recording, interpreting and expressing that information.
The phase of data classification involves assigning labels or tags to the data and then grouping the data according to the similarity of traits of items in the dataset. The third and final phase of analyzing qualitative data is relating the resulting concepts to one another or to an existing theory. After making these connections, the analysts are in a position to comprehend and explain their daily experiences.
After having discussed the key phases in analyzing qualitative data, the authors identify two major steps that are involved in this process. The two steps are transcription and coding.
Transcription, as defined by Grinnell et al, is the act of converting interview data or interpretive research data into written, printed or typed form by omitting non-verbal data. Transcription also includes the act of converting audible and visual data to written data. Coding is the process of searching and for similar words or phrases brought up by the respondents.
The similar words or phrases are then categorized accordingly. Qualitative analysts use coding to develop themes of the data. According to the authors, there are two levels of coding namely open coding and axial coding. Open coding is concerned with the descriptive coding of narrations while axial involves condensing open codes through connecting categories and subcategories.
Another key process in analyzing qualitative data is formulating a conceptual outline from the coded data. This process helps the analyst to link up the data together using a given set of rules. A good example of a conceptual outline is creating an acronym or a name for the steps in a given process. The analyst needs to use the already existing conceptual frameworks to support the process of linking up the data of the phenomenon under investigation.
The conceptual frameworks are vital in determining the validity and reliability of the data link up. Valid qualitative data can be believed from the perspective of another researcher. Qualitative data is transferable to other contexts and settings since the researcher has conducted an exhaustive analysis. Other researchers can rely on the analyzed quantitative data if the researcher is able to account for the constantly changing context of a research process.
In conclusion, analyzing qualitative data is aimed at creating change in the way of thinking. The change, according to Grinnell et al (2012), occurs when qualitative data analysts understand existing phenomena using the process of qualitative data analysis and then develop a new school of thought or confirm an existing theory.
Grinnell, R.M., Williams, M., & Unrau, Y. A. (2012). Research Methods for Social Workers. An Introduction (9th ed.) Oak Highlands, Pair Bond Publications