THE BUSINESS SCHOOL
24th March 2014
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Qualitative Data Analysis – Grounded Theory
As one of nowadays most widely active analytical tools, data analysis has been handled successfully in a widespread diversity of research applications in information and library science (Allen, 1990). Alike to other grounds, data analysis has been mainly applied in library science as a quantitative research method up until last decades. Numerous existing studies use qualitative data analysis, which addresses a number of the vulnerabilities of the quantitative approach. Qualitative data analysis has been identified as ‘a research method for the particular interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns’ (Hsieh and Shannon, 2005, p.1278). This definition demonstrates that qualitative data analysis highlights a combined view of speech/texts and their detailed circumstances. Qualitative data analysis moves away from purely counting words or getting objective subject matter from texts to examine senses, patterns and themes that may be noticeable or hidden in a specific text. It permits researchers to recognize social reality in a particular but scientific fashion. Comparing qualitative data analysis with its relatively familiar quantitative equivalent can improve our perception of the method. Primary, the research areas from which they resulted are dissimilar. Quantitative data analysis is operated broadly in mass communication as a way to count observable textual sections, a feature of this method that is frequently criticized for missing semantic and syntactical information set in the text (Weber, 1990). By difference, qualitative data analysis was expanded mainly in qualitative sociology, anthropology and psychology, in order to study the meanings underlying physical messages. Secondly, quantitative data analysis is deductive, aimed to test hypotheses or adopt questions engendered from theories or previous empirical exploration. By contrast, qualitative data analysis is predominantly inductive, grounding the analysis of themes and topics, as well as the interpretations obtained from them, in the data. Thirdly, the data sampling methods needed by the two approaches are poles apart. Quantitative data analysis requires the data to be chosen by means of arbitrary sampling or other probabilistic tactics, so as to ensure the authenticity...
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