Qualitative data

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Qualitative data
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Qualitative data is defined as the data that approximates and characterizes. Qualitative data have to be observed and recorded. This data type is non-numerical also it is collected through methods of observations, one-to-one interviews, conducting focus groups and similar methods. Qualitative data in statistics is also considered as categorical data, that can be arranged categorically based on the attributes and properties of a thing or a phenomenon (D. Silverman 2011, p. 132-137).

Qualitative data is a source of well-grounded, good descriptions and explanations of a human process. With qualitative data, you can preserve chronological flow, see which incidents led to which consequences, and derive resultful explanations. It also leads to serendipitous findings and new integrations; it allows researchers to get beyond initial theories and generate or revise conceptual frameworks. Ultimately, the findings from well-analyzed qualitative research have an overwhelming value. Words, especially organized into events or stories, have a considered, vivid and meaningful flavor that often makes far more convincing to a reader-another researcher, a policymaker, or a practitioner-than pages of summarized numbers (M. Miles 2014, p.214).

Qualitative reasearch

Qualitative data is a part of qualitative research. Qualitative research main advantages (C. Grbich 2012, p. 37):

  • it provides detailed information and can expand knowledge in a variety of areas.
  • it can improve assess to the impact of policies on a population.
  • it gives insight into people's individual experiences.
  • it helps evaluate service provision.
  • it can enable deep exploration of little-known behaviors, attitudes, and values.

Qualitative research supports certain styles of design, selection, and analytic interpretation. The underpinning ideology or belfies system says that (C. Grbich 2012, p. 38):

  • subjectivity means that both the views of the participant and those of you the researcher are to be respected, acknowledged and integrated as data, and the interpretation of this analysis will be constructed by both of you
  • trustworthiness (validity) us seen as getting to the truth of the matter, reliability is seen as a sound research design and generalisability is local and theoretical only
  • a holistic view is important (so the structures affecting on the setting such as policies, culture, situation, and context need to be included
  • every study is time-bound and context-bound (replication and generalization are unlikely outcomes)

References

Author: Szymon Olejniczak