Qualitative research

1. Presented By: GHULAM QAMBAR
PhD Education (Scholar)
E.Mail.: qambareducator@gmail.com
Contact: +923454702209

2. Outline
1. Code, category, theme
2. Text analysis process (A brief overview)
3. Where do themes come from
4. Themes identification techniques
5. Selecting among themes (considerations)
6. Validity and relability

3. Codes
 A code is a word phrase or a sentence that represents
aspect of a data or captures the essence of features of a
data
 Priori codes: can be identified from a range of source
 Previous research or theory
 Research questions
 Questions and topics from your interview
 Your gut feelings about the data
 Grounded cades: emerge from the data

4. What can be coded?
 Behaviors
 Events
 Activities
 Strategies
 Stages
 Meanings
 Symbols
 Participations
 Relationships/interactions
 conditions./constraints
 Consequences
 Settings
 Norms, values etc.

5. Category
 Grouping of codes
 Purpose is to reduce numbers
 For example people in public life covering codes as
politicians, celebrities, sports people etc.
 Should be conceptual

6. Themes
 A higher level of categorization
 Usually used to identify a major element of your entire
text analysis
 Usually research questions, hypotheses and objectives
form the main themes of the study

7. Coding Process
SortingCoding Synthesizing Theorizing
Codes Categories Themes Theory
Real
or
Particular
Abstract
or
General

8. Text Analysis Involves:
1. Discovering themes and subthemes
2. Describing the core and peripheral elements of
themes
3. Building hierarchies and codebooks
4. Applying themes ( Attaching to the chunks of actual
data)
5. Linking themes into theoretical model

9. Different terms for themes
 Categories
 Codes
 Labels
 Expressions
 Incidents
 Segments
 Thematic units
 Data-bits
 Chunks
 Units concepts

10. Where do themes come from
 From data (an inductive
approach)
 From priori theoretical
understanding which
comes from
a) Characteristics of
phenomenon under study
b) Already agreed upon
professional definitions

11. Theme identifying techniques in
qualitative data
 Widely used 12 techniques can be classified under four
categories
1. Analysis of words
1. Word repetition
2. Indigenous categories
3. KWIC
2. A careful reading of large blocks of text
4. Compare and contras
5. Social science queries
6. Missing information

12. Theme identifying techniques in
qualitative data (cont.)
3. An intentional analysis of linguistic features
7. Metaphors
8. Transitions
9. connectors
4. The physical manipulation of the text
10. Un marked text
11. Pawing (Handling)
12. Cutting and sorting

13. Words repetitions
 Words that occurs a lot are consider relevant in the
mind of respondent
 Formal mode: counting frequency of words
 Informal mode: synonyms, ideas behind words (love,
greed, money etc.)

14. Indigenous categories
 Local terms
 Terms with a particular meaning in respondents
setting (specialized vocabulary)

15. KWIC
 Search key words then corpus of text for further
illustration
 Search range of sentences in which key words occure

16. Compare and contrast
 Constant comparison to find similarities and
differences in the text.
 What are similarities and differences?
 How is similar?
 How is different?
 Compare answers to questions across people space and
time.

17. Social science queries
 How textual data illuminate questions of importance
to social science.
 Search specific social and cultural themes.
 Explain conditions, actions, interactions and
consequences of phenomenon

18. Missing information
 Get an idea of what is not being done or talked about
 Searching for themes that are missing
 Reasons:
 Avoid sensitive issues
 Assume that researcher already knows
 May not speak in the presence of others
 Do not understand the question

19. Metaphors
 The way people feel about things
 Metaphors
 Similes
 analogies

20. Transitions
 Look for naturally occurring shifts in thematic
content.
 Oral speech: pause, change in tune, particular phrase
(then, now then, now again)
 Written speech: therefore, however, similarly, lastly,
next, in brief, in short, conclusion etc.

21. Connectors
 Indicate relationship
 Causal (because, since, as a result)
 Conditional (if, then, rather than, as a result)
 Time oriented (before, after, next)
 Logical (implies, mean, is one of)
 Negative (not, no, none)
 Attribute (X is Y)

22. Connectors (cont.)
 Contingencies (if X then Y)
 Function (X is a mean of affecting Y)
 Spatial (X is close to Y)
 Operational (X is a tool for doing Y)
 Comparison (X resembles Y)
 Class (X is a member of Y)
 Provenience ( X is source of Y) etc.

23. Un marked text
 Examine for the text which has not been coded before
for themes.
 Require multiple reading
 Initially mark obvious themes with colored pencils
 Secondly search for new less obvious themes

24. Pawing
 Marking the text and eyeballing the text.
 Circle words, underline, use color highlighters, run
lines down to the margin indicate meaning and
coding.
 Then look for patterns and significance

25. Cutting and sorting
 Traditional techniques of cutting up transcripts
 Coded them into piles, envelops or folders
 Or past them on cards
 Finally read and process

26. Selection among techniques
 Required following considerations.
1. Kind of data you have
2. How much skill is required
3. How much labor is required ( cost, time, effort)
4. Number and types of themes to be generated
5. Reliability and validity of the theme

27. Kinds of data
 Lengthy narratives: All techniques
 Audio and video: Repetitions, similarities and
differences, missing data
 Shorter and less complex data: transitions,
metaphors and connectors are inappropriate
 Short response to open ended questions: missing
information is not appropriate

28. Skills
 Language skills: metaphors, connectors, indigenous
typologies, missing data
 Other language: repetitions, transitions, similarities
and differences, world list, co-occurance, metacoding
 With less Computational skills: cutting and sorting,
world list, KWIC

29. labor
 Use of computer software is easy in word count and co-
occurance. Learning software require time and effort.
 Observation based techniques are more labor oriented

30. Number and kinds of themes
 More is better. All themes are not equally important
 More themes are generated by repetitions,
similarities and differences, transitions, connectors,
cutting and sorting, world lists and KWIC
 Less themes generated by metaphors, indigenous
categories and missing data.

32. Validity and Reliability

33. Internal Validity/Credibility
 It is achieved when the results are seen as believable by
the participants.
 Techniques involved
1. Prolong engagement
2. Persistent observation
3. Triangulation
4. Peer debriefing
5. Negative case analysis
6. Referential adequacy
7. Member checking

34. External Validity/ Transferability
 It is exist when the results can be applied to other
contexts.
 The researcher should decide and explain the results
in detail

35. Reliability/Dependability
 Emphasize the stability of data over time
 The researcher should describe the changes in the
context of research and how these changes affect the
research.

36. Objectivity/Conformity
 Demonstrates that enquiry is free of bias, Values and
prejudice.
 Research results should not be the imagination of
researcher.
 Procedures should be documented for other researchers to
verify.
 Techniques involve:
1. Prolong engagement
2. Persistent observation
3. Triangulation
4. Peer debriefing
5. Negative case analysis