Transcript APPROACHES TO QUALITATIVE DATA ANALYSIS © LOUIS COHEN, LAWRENCE MANION & KEITH MORRISON.

APPROACHES TO
QUALITATIVE DATA ANALYSIS
© LOUIS COHEN, LAWRENCE
MANION & KEITH MORRISON
STRUCTURE OF THE CHAPTER
• Data analysis, thick description and reflexivity
• Ethics in qualitative data analysis
• Computer assisted qualitative data analysis
(CAQDAS)
QUALITATIVE DATA
• There is no one single or correct way to analyze
and present qualitative data
– Abide by fitness for purpose.
• Qualitative data analysis is often heavy on
interpretation, with multiple interpretations
possible.
• Data analysis and interpretation may often merge.
• Data analysis often commences early.
• Results of the analysis also constitute data for
further analysis.
TO TRANSCRIBE OR NOT TO
TRANSCRIBE INTERVIEWS
• Transcriptions can provide important detail
and an accurate verbatim record of the
interview.
• Transcriptions may omit non-verbal aspects,
and contextual features of the interview.
Transcriptions are very time consuming to
prepare.
• Transcriptions must clarify conventions used.
DATA ANALYSIS, THICK
DESCRIPTION AND REFLEXIVITY
• Fitness for purpose:
– To describe;
– To portray;
– to summarize;
– to interpret;
– to discover patterns;
– to generate themes;
– to understand individuals and idiographic
features;
– to understand groups and nomothetic features.
DATA ANALYSIS, THICK
DESCRIPTION AND REFLEXIVITY
• Fitness for purpose:
– to raise issues;
– to prove or demonstrate;
– to explain and seek causality;
– to explore;
– to test;
– to discover commonalities, differences and
similarities;
– to examine the application and operation of
the same issues in different contexts.
QUALITATIVE DATA ANALYSIS
• The movement is from description to
explanation and theory generation.
• Problems of data overload: data reduction and
display become important.
• Double hermeneutic: the researcher interprets
and already-interpreted world.
• The researcher is part of the world that is being
interpreted, therefore reflexivity is required.
• Subjectivity is inescapable.
• The researcher’s own memory may be fallible,
selective and over-interpreting a situation.
QUALITATIVE DATA ANALYSIS
• Use a range of data and to ensure that these
data include the views of other participants in a
situation.
• Address reflexivity.
• The analysis becomes data in itself, for further
analysis (e.g. for reflexivity).
RESPONDENT VALIDATION
• Respondent validation may be problematic as participants:
– May change their minds as to what they wished to say,
or meant, or meant to say but did not say, or wished to
have included or made public;
– May have faulty memories and recall events overselectively, or incorrectly, or not at all;
– May disagree with the researcher’s interpretations;
– May wish to withdraw comments made in light of
subsequent events in their lives;
– May have said what they said in the heat of the moment
or because of peer pressure or authority pressure;
– May feel embarrassed by, or nervous about, what they
said.
ETHICS IN QUALITATIVE DATA
ANALYSIS
• Identifiability, confidentiality and privacy of
individuals
• Non-maleficence, loyalties (and to whom),
and beneficence
COMPUTER ASSISTED QUALITATIVE
DATA ANALYSIS (CAQDAS)
• To make notes
• To transcribe field notes and audio data
• To manage and store data in an ordered and
organized way
• For search and retrieval of text, data and categories
• To edit, extend or revise field notes
• To code and arrange codes into hierarchies (trees)
and nodes (key codes)
• To conduct content analysis
• To store and check data
• To collate, segment and copy data
COMPUTER ASSISTED QUALITATIVE
DATA ANALYSIS (CAQDAS)
• To enable memoing, with details of the
circumstances in which the memos were written
• To attach identification labels to units of text
• To annotate and append text
• To partition data into units
• To sort, re-sort, collate, classify and reclassify pieces
of data to facilitate constant comparison and to refine
schemas of classification
• To assemble, re-assemble, recall data into categories
• To display data in different ways
• To undertake frequency counts
COMPUTER ASSISTED QUALITATIVE
DATA ANALYSIS (CAQDAS)
• To cross-check data to see if they can be coded
into more than one category, enabling linkages
between categories and data to be found
• To establish the incidence of data that are
contained in more than one category
• To search for pieces of data which appear in a
certain sequence
• To filter, assemble and relate data according to
preferred criteria
• To establish linkages between coding categories
COMPUTER ASSISTED QUALITATIVE
DATA ANALYSIS (CAQDAS)
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To display relationships of categories
To draw and verify conclusions and hypotheses
To quote data in the final report
To generate and test theory
To communicate with other researchers or
participants
TYPES OF CAQDAS SOFTWARE
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Those that act as word processors
Those that code and retrieve text
Those that manage text
Those that enable theory building
Those that enable conceptual networks to be
plotted and visualized
• Those that work with text only
• Those that work with images, video and sound
SOFTWARE FUNCTIONS
• Search for and return text, codes, nodes and
categories;
• Search for specific terms and codes, singly or
in combination;
• Filter text;
• Return counts;
• Present the grouped data according to the
selection criterion desired, both within and
across texts;
SOFTWARE FUNCTIONS
• Perform the qualitative equivalent of statistical
analyzes, such as:
─ Boolean searches
─ Proximity searches
─ Restrictions, trees, crosstabs
• Construct dendrograms of related nodes and
codes;
• Present data in sequences and locate the text
in surrounding material in order to provide the
necessary context;
SOFTWARE FUNCTIONS
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Locate and return similar passages of text;
Look for negative cases;
Look for terms in context (lexical searching);
Select text on combined criteria;
Enable analyzes of similarities, differences
and relationships between texts and passages
of text;
• Annotate text and enable memos to be written
about text.
CONCERNS ABOUT CAQDAS
• Researchers may feel distanced from their data
• Software is too strongly linked to grounded theory
rather than other forms of qualitative data analysis
• Software is best suited to data which require coding
and categorization for developing grounded theory
• Too heavy a focus on coding and retrieving
• Removes data from context
• The software drives the analysis rather than vice
versa
• Relegates the real task of hermeneutic understanding