This document provides an overview of key concepts and processes in text analysis, including identifying themes, building codebooks, coding data, describing codes, making comparisons, and building and testing models. Some of the main points covered include defining what a theme is, different approaches to identifying themes (inductive vs. deductive), tips for building codebooks, the open and axial coding process, using code descriptions to help coders, comparing findings to prior literature, and testing conceptual models that are developed.

1. Text Analysisby:
Muhammad Shahid Zulfiqar Ali
(MPhil Scholar)

2. Five building blocks of text analysis
Identifying themes
Building and applying codebooks
Describing the codes
Making comparisons
Building and testing models

3. Themes
WHAT IS A THEME?
• A theme in a text, is an important
talk, discussion, idea or a subject which
runs through the text.
• A theme is an underlying dimension
of meaning that cuts across a variety of
texts:
• gender; temporality…
• Themes can answer the question:
• “what is this an example of?”
WHERE DO THEMES COME FROM?
• Inductive search:
• open coding
• in vivo coding
• Deductive search:
• a priori coding
• structural coding

4. • A Priori
• Codes derived from literature, theoretical frames
• In Vivo (inductive or grounded)
• Codes derived from the data by using code names drawn from participant
quotes or interpretation of the data
• “Its like magic” is a phrase that could form the basis for a code category
A Priori or In Vivo Codes
Felice D. Billups, EdD., NERA Webinar Presentation

5. How do we identify themes?
BY TEXT OBSERVATION
•Word repetition
•Indigenous categories
•Metaphors and analogies
•Transitions
•Similarities and differences
•Linguistic connectors
•Missing data
•Theory-related material
BY USING THE SOFTWARE
•Glossary search
•Word frequency list
•Word frequency with stemming
•Word co-occurrence
•Meta coding

6. Naming the Themes or Categories
The names can come from at least three sources:
• The researcher
• The participants
• The literature
• Most common: when the researcher comes up with
terms, concepts, and categories that reflect what he
or she sees in the data

7. The ordinary wood… has the
ultimate feel, it feels like it’s a
golf club that you're very
much in control of, rather than
its in control of you.
The whole club swung very
well, it felt nice. You felt as if
you were in control.
… just feels as though I'm in
control of the clubhead right
throughout the shot.
I feel that I've no control over
that clubhead at all.
This feels much more difficult
to control…
…but I could not control it due
to the length and the flex of
the shaft.
Controllable
feel
Uncontrollable
feel
Club
control
Raw data
themes
Higher order
themes
General
dimensions

8. Types of themes
• Ordinary: Themes a researcher expects
• Unexpected: Themes that are surprises and not expected to surface
• Hard-to-classify: Themes that contain ideas that do not easily fit into
one theme or that overlap with several themes
• Major & minor themes: Themes that represent the major ideas, or
minor, secondary ideas in a database. Minor themes fit under major
themes in the write up

9. Themes should…
• Reflect the purpose of the research
• Be exhaustive--you must place all data in a category
• Be sensitizing--should be sensitive to what is in the data
• i.e., “leadership” vs. “charismatic leadership”
• Be conceptually congruent--the same level of abstraction
should characterize all categories at the same level
• For instance, you wouldn’t have produce, canned goods, and fruit

10. Codebooks
WHAT ARE CODES?
• Short-hand notations
(symbolizations) that refer
to themes
• Code books are organized
lists of codes
• 30 / 50 codes
• organized hierarchically or
not
• no more than 3-level deep
WHAT ARE CODES FOR?
• Data-reduction
• Linking themes to chunks of
qualitative data

11. How do we build codebooks?
STRUCTURAL CODES
•Describes characteristics of the
data
• Who / where / …
• Document variables
• Socio-economic data…
THEMATIC CODES
•Link themes to instances of data
• Can be inductive
• Can be deductive
OTHER MARKUP TOOLS
•Memo
• used to interpret the text with
running thoughts of the analyst
•Hyperlinks
• Used to link one instance of data to
another

12. Coding Data
Open Coding
• Assign a code word or
phrase that accurately
describes the meaning of
the text segment
• Line-by-line coding is done
first in theoretical research
• More general coding
involving larger segments
of text is adequate for
practical research (action
research)
Axial Coding
• The process of looking for
categories that cut across all
data sets
• After this type of coding, you
have identified your themes
• You can’t classify something as
a theme unless it cuts across
the preponderance of the data

13. Clustering
• After open coding an entire text, make a list of all
code words
• Cluster together similar codes and look for redundant
codes
• Objective: reduce the long list of codes to a smaller,
more manageable number (25 or 30)

16. Tips preparing the data
• Text cleanup:
• Typos
• Capitals have no importance
• Decide how you break up text:
• Sentences & paragraph
• Translation:
• Decide on the relevance of the original text

17. Tips for Coding
Solo coder
• Beware of coding drift.
• Use memos and code book as
conceptual work.
Team coding
• Work-structure:
• Decide how you code as a team: at
level of sentence, at level of
paragraph…
• Get people to work with the
entire codebook gets best
reliability.

18. Benefits of team coding
• Reliability.
• Validity / trustworthiness / emic validity.
• Construct definition.
• Identifying core-periphery.
• Identify exemplary quotes.

19. Coding sheet example
Coding
units
Coding categories
Length of
song (secs)
# different
words
Main
topical
focus
# instruments
played
Vocal
enhancement
Oops I did
it again
We are the
world
Stairway to
heaven
Unchained
melody

20. Describing your codes
HELP YOUR TEAM!
•Short description
•Detailed description
•Inclusion criteria
•Exclusion criteria
•Typical example
•Atypical example
•Close but no

21. EDUC 7741/Paris/Terry
A Description
•A detailed rendering of people, places, or events
in a setting in qualitative research
•Codes such as “seating arrangements,” “teaching
approach,” or “physical layout of the room,”
might all be used to describe a classroom where
instruction takes place

22. EDUC 7741/Paris/Terry
Narrative description
•From the coding and the themes, construct a
narrative description and possibly a visual
display of the findings for your research report
•Use the assigned format (see syllabus)

23. EDUC 7741/Paris/Terry
Constructing the narrative
• Identify dialogue that provides support for themes
• Look for dialogue in the participants’ own dialect
• Use metaphors and analogies
• Collect quotes from interview data or observations
• Locate multiple perspectives & contrary evidence
• Look for vivid detail
• Identify tensions and contradictions in individual experiences

24. EDUC 7741/Paris/Terry
Conveying personal reflections
Because qualitative researchers believe that personal views
can never be kept separate from interpretations, personal
reflections about the meaning of the data are included in the
research study
• “David had been diagnosed with AD/HD and also with mild Tourette
Syndrome. He took medication for AD/HD. He was selected to
participate in the project as a confirming participant because he was
so involved with the project and so intense during the first
observation. Unaware that he had AD/HD and Tourette Syndrome
until I interviewed his mother during the second year of the project, I
was surprised because he was the most focused student in the
classroom.”(Terry, 2003)

25. EDUC 7741/Paris/Terry
Providing Visual Data Displays
•Qualitative researchers often display their findings
visually
• Comparison table or matrix
• Hierarchical tree diagram that represents themes and their
connections
• Boxes that show connections between themes
• Physical layout of the setting
• Personal or demographic information for each person or
site

26. EDUC 7741/Paris/Terry
Enhances Commitment,
Attitudes,
and Student Development

27. EDUC 7741/Paris/Terry
Making comparisons with the Literature
• Interpret the data in view of past research
• Show how the findings both support and contradict prior
studies
• “These findings are consistent with other studies in regard to
duration. It has been found that the length or duration of service
learning projects has an impact on student outcomes, with the longer
duration projects having greater impacts. However, significant
differences are not found in projects lasting over 18 weeks (Conrad
& Hedin, 1981). The project on which this study focused was
examined over a year and a half period of time; thus it is considered
to be long in duration which helps to explain its impact on student
outcomes.”

28. Building Conceptual Models
• Once the researcher identifies a set of things (themes,
concepts, beliefs, behaviors), the next step is to
identify how these things are linked to each other in a
theoretical model.
• Models are sets of abstract constructs (concepts)
and the relationships among them.
• Grounded theory, schema analysis, ethnographic
decision modeling, and analytic induction all include
model-building phases.

29. Cont…
• Once a model starts to take shape, the researcher looks for negative
cases—cases that don’t fit the model. Negative cases either
disconfirm parts of a model or suggest new connections that need to
be made. In either instance, negative cases need to be
accommodated.
• Negative case analysis is used by the schema analysts, ethnographic
decision modelers, and scholars who use analytic induction.
• In ethnographic decision modeling and in classical content analysis,
models are built on one set of data and tested on another.

30. Testing Models
• Grounded theorists and schema analysts today are more likely to
validate their models by seeking confirmation from expert
informants than by analyzing a second set of data.
• For example, Kearney, Murphy, and Rosenbaum (1994) checked
the validity of their model of crack mothers’ experiences by
presenting it to knowledgeable respondents who were familiar
with the research. Regardless of the kind of reliability and validity
checks, models are interpretations of reality. They can be made
more or less complicated and may capture all or only a portion of
the variance in a given set of data. It is up to the investigator and
his or her peers to decide how much a particular model is
supposed to describe.

31. EDUC 7741/Paris/Terry
Limitations of the study
The researcher suggests possible limitations or weaknesses
of the study
• “This study focused on one rural middle school in an area in
Northeast Georgia, Hartwell. It documented the methodology
used in the service learning project and the effect of a certain
type of service learning model, Community Action. Therefore,
the study provides an in-depth look at a service learning project
carried out by gifted students in just one middle school in a rural
area situated in a Southern state. Transferability may be limited
as a result” (Terry, 2001).

32. EDUC 7741/Paris/Terry
Future Research Suggested
Researchers make recommendations for future
research
• “In addition, further research is needed to determine
outcomes for a diversified culture of students, including,
but not limited to African-American students and students
diagnosed with AD/HD. Research is also needed to
examine and validate existing frameworks before
professing any general claims concerning the outcomes for
students engaged in service learning activities” (Terry,
2003).

33. EDUC 7741/Paris/Terry
Validating the Accuracy of Findings
At the end, the qualitative researcher validates the
finding by determining the accuracy or credibility of the
findings. Methods include:
• Prolonged engagement & persistent observation in the field
• Triangulation
• Peer Review
• Clarifying researcher bias
• Member Checking
• Rich, thick description
• External Audit

34. EDUC 7741/Paris/Terry
Addressing Research Bias
“I am not an impartial bystander when it comes to service learning
so I knew I had to enhance internal validity at the outset of the
study. I have been involved with Community Action service
learning projects for over 16 years as a teacher. I have co-authored
a book on how to facilitate Community Action service learning
projects which I have used to implement service learning projects
in my own classroom. My students have been featured in Reader’s
Digest and have been guests on the Phil Donahue Show because of
their outstanding work in service learning. Being aware of this
bias, I took extreme precautions to maintain objectivity during both
the collection and analysis of the data thereby accurately
representing the project fairly and accurately” (Terry, 2001). a

35. EDUC 7741/Paris/Terry
Reliability or Dependability
• From a quantitative perspective, reliability refers to
the extent to which research findings can be
replicated
• From a qualitative perspective, dependability,
(reliability) in qualitative research is not based on
outsiders getting the same results, but that outsiders
concur that, given the data collected, the results make
sense. In other words, the results are dependable
and consistent (Lincoln & Guba, 1985).

36. EDUC 7741/Paris/Terry
External Validity
• Concerned with the extent to which the findings of one
study can be applied to other situations
• Quantitative studies enhance external validity using a priori
conditions which are limiting in conducting qualitative
research
• External validity is problematic in qualitative research
because “In qualitative research, a single case or small
nonrandom sample is selected precisely because the
researcher wishes to understand the particular in depth, not
to find out what is generally true of the many” (Merriam,
1998, p. 208).

37. EDUC 7741/Paris/Terry
Applying external validity to qualitative research
• Think in terms of the reader of the study
• What is the extent to which a study’s findings can apply to
other situations?
• This is referred to as Representativeness or Transferability
• Merriam (1998) suggests: rich, thick description and
typicality, modal category, or multisite designs.