For Qualitative Data Analysis

2. Qualitative Data Analysis
Dr. Senthilvel Vasudevan, M.Sc., M.Phil., DST., PGDBS., Ph. D,
Lecturer in Pharmacy (Biostatistics),
Dept. of Pharmacy Practice,
College of Pharmacy,
KSAU-HS,
Riyadh, Saudi Arabia.
(First Finding or creating and analyzing the texts)
Research Methodology II (CAMS 412)
Lecture: 9
Date: 20/11/2016

3. Qualitative Data Analysis
 Qualitative Data Analysis (QDA) is the
range of processes and procedures
whereby we move from the qualitative
data that have been collected into some
form of explanation, understanding or
interpretation of the people and situations
we are investigating.
 QDA is usually based on an interpretative
philosophy.

4. Qualitative Data Analysis (Contd…)
Coding Problem:
Coding of texts and finding the patterns.
The Coding turns qualitative data (ie., texts) into
quantitative data (ie., codes)
It Codes just as arbitrary as the codes we make up in.
e.g., the construction of questionnaires.

5. Qualitative Inquiry:
1. 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.
2. 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
3. Problem
For qualitative data analysis, in the sense of shared ground rules for drawing
conclusions and verifying sturdiness
( Source: Miles and Huberman, 1984 )
Qualitative Data Analysis (Contd…)

6. Critical Thinking:
 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. It means weighing up the arguments and evidence for and
against.
Key points when thinking critically are:
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.

7. 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.

8.  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 and attempts to lure the
reader into agreements

9. Credibility of Qualitative Analysis
It depends on three distinct elements:
1Rigorous techniques and methods for gathering high-quality data
that is carefully analysed, with attention to issues of validity,
reliability, and triangulation.
2The credibility of the researcher, which is dependent on training,
experience, track record, status, and presentation of self.
3Philosophical belief in the phenomenological paradigm.
ie., Qualitative methods, inductive analysis

10.
It 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"
Challenges:
 To place the raw data into logical, meaningful categories;
 To examine them in a holistic fashion;
 To communicate this interpretation to others.

11. Stages of Qualitative 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.

12.  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).
Stages of Qualitative Analysis (Contd…)

13. Levels of Analysis
 Simply count the number of times a particular word or
concept occurs in a narrative: The qualitative data can then
be categorized quantitatively and subjected to statistical
analysis.
 For a theoretical analysis such as grounded theory you
would want to go further still.

14. Considerations in the Analysis
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)

15. 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

16. Analysis in Quantitative Research
 Identification of the themes emerging from the raw data,
"open coding“
 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".

17. Stages of Qualitative 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.

18. Final Steps in Qualitative Analysis
 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.

19. Rules in Data Analysis
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.

20. 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 analyzed separately
c) Combination - when and how datasets may be
combined or separated
Rules in Data Analysis (Contd…)
Separability of Data:

21. Admissibility of Data:
a). Relative value or worth of different kinds of data
and how it is assessed.
b). Validation required or not,
e.g.,
by members, research participants, other
researchers, etc.
Rules in Data Analysis (Contd…)

22. Analytic Principles:
Coding data:
 Mark, corral, and reduce data.
 Start with codes a priori or allow to develop.
 Codes evolve with time and experience.
Rules in Data Analysis (Contd…)

23. Analyzing data and codes:
 Quantitative by (i). Counting and (2). Correlating.
 Reduce data and focus analysis.
 Proliferate codes to see layers of meaning.
Rules in Data Analysis (Contd…)

24. 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 atterns/relationships
in codes.
 Folio Views: huge datasets, focused coding, search & sort.
Rules in Data Analysis (Contd…)

25. Types:
 Word processors
 Word retrievers
 Text base managers
 Code & retrieve programs
 Code based theory builders
 Conceptual network builders
Rules in Data Analysis (Contd…)

26.  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
Practical Advice Qualitative Data Analysis

28. Qualitative Analytical Process
Components Procedures Outcomes
Data Reductions
Data Display
Conclusions &
Verification
Coding
Categorisation
Abstraction
Comparison
Dimensionalisation
Integration
Interpretation
Description
Explanation/
Interpretation
( Source: Adapted from descriptions of Strauss and Corbin, 1990, Spiggle 1994, Miles and Huberman, 1994)