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Methods Of Qualitative Data Analysis


Data is a source of great information which can enable informed decision making for businesses. Data is divided into Quantitative Data and Qualitative Data. – PowerPoint PPT presentation

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Title: Methods Of Qualitative Data Analysis

Methods Of Qualitative Data Analysis
Data is a source of great information which can
enable informed decision making for businesses.
Data is divided into Quantitative
Data and Qualitative Data. Qualitative data
refers to those non-numerical, explanatory data.
Herein, we will have a detailed look into the
various methods of qualitative data
analysis. What is Qualitative Data
Analysis? The process of Qualitative Data
Analysis includes analysing information or data
which aids in offering a certain level of
understanding, explanation, and aids in
interpreting the patterns and themes included in
textual data. To put it in general words,
qualitative data cannot be expressed in the form
of numbers and is often represented in words,
observations, pictures, and symbols.
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  • Now coming to the methods used for analyzing
    qualitative data. There exist numerous methods
    for analyzing and interpreting qualitative data.
    However, the most popular and viable methods are
    listed below which includes 4 prominent
    techniques viz.
  • Content Analysis
  • Narrative Analysis
  • Grounded Theory Analysis
  • Framework Analysis
  • Discourse Analysis
  • 1.Content Analysis
  • This is the most popular type of qualitative
    analysis technique used by researchers. It
    primarily includes interpreting the meaning of
    data and identifying the important points in the
    content. It popularly includes analyzing the data
    published in the social media field so as to
    understand and interpret the current patterns and
    trend in social media.

  • Key purpose includes
  • Finding the important aspects of social media
    content through emails, chats, comments, business
    emails, blogs, news, etc.
  • To present them in an effective manner.
  • The popular techniques under these are the
    conventional, summative and directive approach.
  • 2. Narrative Analysis
  • This type of data analysis is used for
    interpreting different types of stories. It
    primarily aids in understanding how individuals
    form meanings in their lives so as to explain
    their day to day workings and world. A narrative
    analysis uses units like personal accounts,
    journals, autobiographical writing, letter
    writing, etc.
  • Key purposes include
  • Aids in connecting past with the current
  • Understanding the interpretation of events.
  • Gain knowledge about people, their culture and
    changes happening.

  • 3. Grounded Theory Analysis
  • This method involves the generation of theory
    through the collection of data and its analysis.
    The primary feature of this analysis is that it
    helps in focusing on theory development. They are
    useful in interpreting a large quantity of data
    involving a large unstructured group. The key
    methods include
  • Interpretation of texts
  • Focus groups
  • Interview with open-ended questions
  • Participant observation
  • 4. Framework Analysis
  • This type of data analysis method is used to
    manage and organize information using the process
    of summarization. As the meaning of the word
    framework goes, it helps in categorizing the data
    into a matrix comprising of rows (cases) and
    columns (codes) and cells of summarized data.

  • Use
  • Helps in identifying similarities and differences
    in qualitative data
  • Aids in drawing descriptive and explanatory
  • 5. Discourse Analysis
  • It is widely in the field of linguistics that
    lays focus on the role of language in building
    social reality. It thus deals with interpreting
    language through communication and different
    types of texts.
  • Use
  • Helps in exploring different texts, discourses,
    and its context
  • Aids in drawing inferences

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Choosing the appropriate qualitative data
analysis technique will help achieve the desired
result along with gaining a deeper understanding
of the data, its explanation and interpretation.