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Qualitative Data Analysis (PowerPoint)

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					Qualitative Data
   Analysis
 Finding or creating and then
       analyzing texts
The Coding Problem

coding of texts and finding patterns.

Coding turns qualitative data (texts) into quantitative
  data (codes)

codes just as arbitrary as the codes we make up in
  e.g., the construction of questionnaires.
Qualitative Inquiry
Purpose
  - to produce findings. The Data Collection process is not an end in
  itself. The culminating activities of qualitative inquiry are analysis,
  interpretation, and presentation of findings.
Challenge
  To make sense of massive amounts of data, reduce the volume of
  information, identify significant patterns and construct a framework
  for communicating the essence of what the data reveal
Problem
  ‘…have few agreed-on canons for qualitative data analysis, in the
  sense of shared ground rules for drawing conclusions and verifying
  sturdiness’ Miles and Huberman, 1984)
Critical Thinking
   ‘calls for a persistent effort to examine any belief or supposed form of
   knowledge in the light of the evidence that supports it and the further
   conclusions to which it tends’ (Glaser, 1941)

   - means weighing up the arguments and evidence for and against.

Key points when thinking critically are (Glaser, 1941):
     Persistence: Considering an issue carefully and more than once

     Evidence: Evaluating the evidence put forward in support of the
       belief or viewpoint
     Implications: Considering where the belief or viewpoint leads; what
       conclusions would follow; are these suitable and rational; and if not,
       should the belief or viewpoint be reconsidered
Analytical Thinking
involves additional processes:

   Standing back from the information given
   Examining it in detail from many angles
   Checking closely whether each statement follows logically from
    what went before
   Looking for possible flaws in the reasoning, the evidence, or the
    way that conclusions are drawn
   Comparing the same issues from the point of view of other writers
   Being able to see and explain why different people arrived at
    different conclusions
   Being able to argue why one set of opinions, results or conclusions
    is preferable to another
   Being on guard for literary or statistical devices that encourage the
    reader to take questionable statements at face value
   Checking for hidden assumptions
   Checking for attempts to lure the reader into agreements
The Credibility of Qualitative Analysis
    depends on three distinct but related inquiry elements:

1   Rigorous techniques and methods for gathering high-quality
    data that is carefully analysed, with attention to issues of
    validity, reliability, and triangulation;
2   The credibility of the researcher, which is dependent on
    training, experience, track record, status, and presentation of
    self;
3   Philosophical belief in the phenomenological paradigm, that
    is, a fundamental appreciation of naturalistic inquiry,
    qualitative methods, inductive analysis and holistic thinking.
The Product of Qualitative Data
Analysis
"Naturalistic inquiry is always a matter of degree"

extent to which the researcher influences responses
  and imposes categories on the data.

The more "pure" the naturalistic inquiry, the less
  reduction of data into categories.
Bogdan and Biklen
"working with data, organizing it, breaking it into manageable
  units, synthesizing it, searching for patterns, discovering what
  is important and what is to be learned, and deciding what you
  will tell others" (1982:145)

challenge –
to place the raw data into logical, meaningful categories;
to examine them in a holistic fashion;
to communicate this interpretation to others.
Common stages of analysis
   Familiarisation with the data through review, reading, listening etc.
   Transcription of tape recorded material.
   Organisation and indexing of data for easy retrieval and identification.
   Anonymising of sensitive data.
   Coding (or indexing).
   Identification of themes.
   Re-coding.
   Development of provisional categories.
   Exploration of relationships between categories.
   Refinement of themes and categories.
   Development of theory and incorporation of pre-existing knowledge.
   Testing of theory against the data.
   Report writing, including excerpts from original data if appropriate (e.g.,
    quotes from interviews).
3 broad levels of analysis that could be
pursued
   Simply count the number of times a particular word or concept
    occurs (e.g., loneliness) in a narrative: The qualitative data can then
    be categorised quantitatively and subjected to statistical analysis.

   For a thematic analysis want to go deeper than this.
         All units of data (eg sentences or paragraphs) referring to loneliness could be
            given a particular code, extracted and examined in more detail. Do
            participants talk of being lonely even when others are present? Are there
            particular times of day or week when they experience loneliness? In what
            terms do they express loneliness? Do men and women talk of loneliness in
            different ways? Are those who speak of loneliness also those who
            experience depression? Themes could eventually be developed such as
            ‘lonely but never alone’ or ‘these four walls’.

   For a theoretical analysis such as grounded theory you would want
    to go further still.
1. Analysis Considerations
1   Words
2   Context (tone and inflection)
3   Internal consistency (opinion shifts during groups)
4   Frequency and intensity of comments (counting, content
    analysis)
5   Specificity
6   Trends/themes
7   Iteration (data collection and analysis is an iterative process
    moving back and forth)
Grounded theory – constant
comparative method
   open coding (initial familiarisation with the data)
   delineation of emergent concepts
   conceptual coding (using emergent concepts)
   refinement of conceptual coding schemes
   clustering of concepts to form analytical
    categories
   searching for core categories
   core categories lead to identification of core
    theory
Analysis begins:

   identification of the themes emerging from the raw data,
    "open coding" (Strauss & Corbin 1990)

   identify and tentatively name the conceptual categories into
    which the phenomena observed will be grouped.

   goal - to create descriptive, multi-dimensional categories
    which form a preliminary framework for analysis.

   Raw data are broken down into manageable chunks, and
    researcher devises an "audit trail".
Next stage of analysis:

   Re-examination of the categories identified to determine how
    they are linked: "axial coding“.

   Discrete categories identified in open coding are compared
    and combined in new ways as the researcher begins to
    assemble the "big picture."

   Purpose of coding not only to describe but to acquire new
    understanding of a phenomenon of interest.

   During axial coding the researcher is responsible for building
    a conceptual model and for determining whether sufficient
    data exists to support that interpretation.
Finally:

   Researcher translates the conceptual model into
    the story line that will be read by others.

   Research report should be a rich, tightly woven
    account that "closely approximates the reality it
    represents".

   Stages of analysis not necessarily linear, in
    practice occur simultaneously and repeatedly.
RULES OF DATA ANALYSIS

1 Timing of Analysis

a) in relation to data collection
       following data collection = linear
      continuing, interactive (e.g., constant comparative
       analysis) in a matrix

b) in relation to phases of study
       cyclical approach to data collection and analysis specified
       in some designs - (e.g., action research, case study, co-
       operative inquiry). Interim analysis.
2. Separability of Data

a) abstraction of ideas/concepts from 'raw data'
    during analysis

b) interaction between different datasets, e.g.,
     'melting pot' of all data vs. each tranche
     analysed separately

c) combination - when and how datasets may (or
    must) be combined or separated
3. Admissibility of Data

a) relative value or worth of different kinds
 of data and how it is assessed

b) validation required (and how) or not, e.g.,
  by members, research participants, other
  researchers, etc.
Analytic Principles

Coding data:

   Mark, corral, and reduce data.

   Start with codes a priori or allow to develop.

   Codes evolve with time and experience.
Analyzing data and codes:

   Mimic quantitative by counting, correlating.

   Reduce data and focus analysis.

   Proliferate codes to see layers of meaning.
Computer Assistance

   Does not alter analysis process.
   Usually not a shortcut or timesaver.
   Programs fit different data & needs.

Computer Software
 Atlas-ti: large datasets, unstructured coding, mimic paper
  code & sort.
 NUDIST: large datasets, structured coding, mimic quant
  analysis.
 NVivo: less data, unstructured coding, find
  patterns/relationships in codes.
 Folio Views: huge datasets, focused coding, search & sort.
6 types
   Word processors
   Word retrievers
   Textbase managers
   Code-&-retrieve programs
   Code-based theory builders
   Conceptual-network builders
Practical Advice
   Start the analysis right away and keep a running account
    of it in your notes

   Involve more than one person

   Leave enough time and money for analysis and writing

   Be selective when using computer software packages in
    qualitative analysis
  The Qualitative Analytical Process
  (Adapted from descriptions of Strauss and Corbin, 1990, Spiggle 1994,
  Miles and Huberman, 1994)



   Components                    Procedures                 Outcomes


Data Reductions                 Coding                   Description
                             Categorisation
                              Abstraction
 Data Display                 Comparison
                           Dimensionalisation
                              Integration
Conclusions &
                             Interpretation             Explanation/
 Verification
                                                        Interpretation

				
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posted:9/28/2011
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Lingjuan Ma Lingjuan Ma
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