This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves organizing, accounting for, and making sense of data by noting patterns, themes, and regularities. There is no single correct way to analyze qualitative data, as the method should fit the purpose. The researcher must be clear on what the analysis aims to do, such as describe, interpret, discover patterns, or explain. How the data is analyzed and presented will depend on the type of qualitative study and number of data sources. Analysis involves coding, categorizing, and grouping data to identify relationships and themes in order to draw conclusions. Displays are used to help make sense of relationships between codes and build themes.
2. QUALITATIVE DATA
Qualitative data analysis involves organizing,
accounting for and explaining the data; in
short, making sense of data in terms of the
participants’ definitions of the situation, noting
patterns, themes, categories and regularities.
3. QUALITATIVE DATA
There is no one single or correct way to analyse and present qualitative data; how
one does it should abide by the issue of fitness for purpose. Further, qualitative
data analysis, is often heavy on interpretation, and one has to note that there are
frequently multiple interpretations to be made of qualitative data – that is their
glory and their headache! In abiding by the principle of fitness for purpose,
4. QUALITATVE ANALYSIS
the researcher must be clear what he or she wants the data analysis to do as this will determine the
kind of analysis that is undertaken
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 (e.g. frequencies, norms, patterns, ‘laws’)
to raise issues
to prove or demonstrate
to explain and seek causality
to explore
5. The significance of deciding the purpose is that it will determine the kind of
analysis performed on the data. This, in turn, will influence the way in which the
analysis is written up. The data analysis will also be influenced by the kind of
qualitative study that is being undertaken.
For example, a biography and a case study may be most suitably written as
descriptive narrative, often chronologically, with issues raised throughout. An
ethnography may be written as narrative or stories, with issues raised, but not
necessarily conforming to a chronology of events, and including description,
analysis, interpretation and explanation of the key features of a group or culture. A
grounded theory and content analysis will proceed through a systematic series of
analyses, including coding and categorization, until theory emerges that explains
the phenomena being studied or which can be used for predictive purposes.
6. QUALITATIVE DATA
The analysis will also be influenced by the number of data sets and people
from whom data have been collected. Qualitative data often focus on
smaller numbers of people than quantitative data, yet the data tend to be
detailed and rich. Researchers will need to decide, for example, whether to
present data individual by individual, and then, if desired, to amalgamate
key issues emerging across the individuals, or whether to proceed by
working within a largely predetermined analytical frame of issues that
crosses the individuals concerned qualitative studies deliberately focus on
individuals and the responses of significant players in a particular scenario,
often quoting verbatim responses in the final account; others are content to
summarize issues without necessarily identifying exactly from whom the
specific data were derived. Later on here we discuss methods to be used
with respect to people and issues
7. Qualitative analysis
Some studies include a lot of verbatim conversations; others
use fewer verbatim data. Some researchers feel that it is
important to keep the flavour of the original data, so they
report direct phrases and sentences, not only because they
are often more illuminative and direct than the researchers’
own words, but also because they feel that it is important to
be faithful to the exact words used.
8. Qualitative data
At a practical level, qualitative research rapidly amasses huge amounts of data, and
early analysis reduces the problem of data overload by selecting out significant
features for future focus.
careful data display is an important element of data reduction and selection.
‘Progressive focusing’ starts with the researcher taking a wide angle lens to gather
data, and then, by sifting, sorting, reviewing and reflecting on them, the salient
features of the situation emerge. These are then used as the agenda for
subsequent focusing.
10. ways of organizing and presenting data analysis
The methods are by people, and by issue, and the final method is by instrument.
11.
12.
13.
14. Data
Text is generally collected from or in the form of…
Field notes -- Newspaper or magazine stories
Interviews (recorded and transcribed)
Focus groups -- Web pages
Audio & video tapes (transcribed and described)
Copies of documents -- Photographs (described)
Narrative descriptions
Diaries
15. 1. Read Data, develop ideas and feelings
2. Code Data, tag items with same meaning using a unique
code
3. Search and extract instances of codes
4. Identify patterns among codes (pattern coding)
5. Create figures, tables, or descriptions of patterns
ANALYSIS
THEMES
16. Analysis
Process of Qualitative Analysis:
Data Reduction
Data Display
Conclusion Drawing and Verification
19. Coding
What is coding?
In qualitative analysis, coding is the process of
identifying categories and meanings in text, creating
and applying a name or code to each, and
systematically marking similar strings of text with the
same code name.
Coding permits systematic retrieval of categories and
meanings during analysis. Codes help researchers
identify patterns in data.
20. Coding
One codes only relevant data (Not all text must be
coded to complete the project)
Codes may be based on:
Actions, Behaviors,Topics, Ideas, Concepts,
Terms, Phrases, Keywords, and so forth
Coding is purposeful interpretation, with mindful
reflection on the meanings of the persons, context,
interactions, statements, assumptions, and so forth
22. Coding
Sources of codes (typically both):
1.A priori codes—expected, looked for
Previous research
Previous theory
Research question
Your intuition of the data or setting
2.Grounded codes—discovered
(suspend ideas about the subject and let the data determine codes)
23. Coding
It helps if code names are meaningful.
When new relevant content is discovered, a new code is created.
Codes may evolve
A string of text may contain more than one code.
24. Coding
Codes must be consistently applied
Keeping a list of codes helps to:
› Identify the content of each code, and
› Reveal the contents of the text.
Codes should be grouped in some form (e.g., related clusters) to advance
analysis
27. Displays
There are numerous legitimate ways to move from
codes to final narrative, but core among them is
systematic work and adherence to logic.
Systematic analysis is advanced when codes are put
into “data displays” which reflect the researcher’s
judgments about the data
Data displays link various codes and help to build
themes
29. Displays
Such arrangements help researchers:
1. “dimensionalize,” or recognize dimensions of similar
thoughts or
E.g., thoughts about how to appear masculine:
Clothes Presence
Short hair -- Confidence
Plain shoes -- Taking up space
Shirt with collar
2. Connect codes in more sophisticated ways
3. Document patterns in “user-friendly” ways (never
rely on memory)
30. Displays
Relationships between codes become more
apparent as codes are grouped
Themes should be explored
› Why do some codes co-occur?
› Why are some dimensions related to other codes while
others are not?
› Are some codes linked to particular emotions?
Exploration of themes is analysis. The discoveries
should be written down. These eventually (with very
heavy and serious editing) turn into your written
text.
31. Analysis
Process of Qualitative Analysis:
Data Reduction
Data Display
Conclusion Drawing and Verification
32. As one creates and views displays, the salient components
meaning and activities become apparent.
Research may be:
› Descriptive: Represents the data (meanings, observations) to
readers in such a way that they will “understand” what the
researcher “sees” in the data.
› Causal: Links concepts in the data together to explain observed
meanings or phenomena, and to write in such a way that readers
will “understand” what the researcher “sees.”
This stage relies very heavily on logical evaluation and
systematic description
Drawing Conclusions and Verification
33. The researcher WRITES what he or she sees as
logical descriptions of themes
The researcher always refers back to the data
displays and raw data as descriptions or causal
statements are made.
Systematic, organized, and good coding and notes
will really pay off at this point, allowing efficient,
accurate access to data
Conclusions are made through this process
Drawing Conclusions and Verification
34. Drawing Conclusions and Verification Articles and reports often include quotes. They are not the text “speaking for
itself.”
Quotes are used for:
Evidence
Explanation
Illustration
Deepening understanding
Giving participants a voice
Enhancing readability
35. Drawing Conclusions and Verification
In the end, like good quantitative research, good
qualitative research gives a portrayal of the human
experience that is as accurate as possible, but which
always has limitations.
36. Qualitative Methods
It is often difficult to plan qualitative research
Group Discussion:
Spend several minutes generating ideas for a qualitative
research study. What are you going to study and why?
Create a plan for:
Sampling
How will you determine whether your sample is representative of
a target group?
Data Collection
Data Analysis
How will you evaluate causality?
How will you write about or present your findings?
Introduction