Lesson Objective: After completing this lesson students will be able to: a) learn the process of transcribing data; b) understand the data analysis process in qualitative research

1. DATA MANAGEMENT IN
QUALITATIVE LEGAL RESEARCH
Preeti Kana Sikder
Assistant Professor
Department of Law And Justice
Jahangirnagar University

2. LESSON OVERVIEW
 Transcription of Data
 Organising the Data
 Data Analysis
 Exploring the Data
 Coding the Data

3. TRANSCRIBING THE DATA
According to Bailey J (2008), transcribing is usually the first step in data analysis. It means change of
form or medium, usually changing recordings to written information. The researcher shall use his
judgment in deciding how detail the transcription should be. This process demands care; because in
interpreting the data, context, wider or overall view, depth and sequence shall be taken into
consideration. Transcription in research requires that is represents data.

4. WHY TRANSCRIBE?
 Going through transcripts is easier than watching or listening to
records or going through documents in different forms
 It allows researcher to navigate easily. Marking, linking, checking
and re-checking helps to find the right things the researcher would
like to have in a single medium.
 It would be an evidence of analysis and repository of findings.
 It may document untrustworthiness of the researcher and secure
accountability of their approaches.

5. WHAT TO TRANSCRIBE?
 Documents
 Field notes, Expanded notes
 Image or Video
 Recording of Interviews or Discussion
 Verbal Sound
 Non verbal Sound
 Filler words
 Indication of inaudible sounds

6. TYPES OF TRANSCRIPTION
Verbatim Approach
 Participant’s own words, language and
expressions.
 According to Hennink and Weber, 2013,
it is not only the words used by
participants that are of interest to
qualitative researchers, but perhaps more
importantly, the meanings and concepts
attached to the words, descriptions and
expressions that provide a deeper
understanding of the research issues
within the socio-cultural context of the
study participants.
Generalised Approach
 Contains the gist, the most relevant part
of the conversation.
 This type of transcription can be
grammatically modified, while
superfluous hesitations, filler words,
interjections, and interruptions are
omitted.
 It emphasizes more on the ‘meanings
and perceptions’ in the speech or
discussion as it represents the reality.

7. ORGANISING THE DATA

8. HOW TO ORGANIZE DATA
 Expand field notes or write from memory immediately after the formal or
informal interview, discussion or observation when it was not recorded onsite.
 If recorded, transcribe it as soon as possible after the interview, preferably
by a person who was present during the interview.
 Photographs, documents, leaflets, posters or other materials which is not in
soft form could be converted into soft copy.
 All hard copies should be kept in safe and secure place and all soft copies
should have several back ups.
 Construct a table for monitoring your work with true name of respondent
and status of progress in data management and keep it in a secure place.

9. HOW TO ORGANIZE DATA
Data collection
Method
Who
(Experience)
Date and Place Recorded or
Noted
Status File name
Key Informant
Interviews (KII)
Prof. XYZ – 57
years
29-2-2020, ZRA 02-290220-ZRA-
XYZ-KI.MP3
Transcribed 02-290220-
ZRA-XYZ-KI
In-depth
Interviews (IDI)
Group
Discussions
(GD)
Observations
Informal
Discussion (ID)
With a local
group
28-2-2020, at a
residential hall
Field Note Book –
2 (28 Feb)
Coding Done

10. HOW TO ORGANIZE DATA
 Expand field notes or write from memory immediately after the formal or informal
interview, discussion or observation when it was not recorded onsite.
 If recorded, transcribe it as soon as possible after the interview, preferably by a
person who was present during the interview.
 Photographs, documents, leaflets, posters or other materials which is not in soft
form could be converted into soft copy.
 All hard copies should be kept in safe and secure place and all soft copies should
have several back ups.
 Construct a table for monitoring your work with true name of respondent and
status of progress in data management and keep it in a secure place.

11. DATA ANALYSIS IN QUALITATIVE
LEGAL RESEARCH
After organising and transcribing the data it needs to be
analysed scientifically

12. WHY DATA ANALYSIS?
According to Bradley, Curry and Devers (2007), Data Analysis is for
‘generation of taxonomy, themes and theory’.
• Taxonomy: process of arranging things into groups and defining or
classifying multifaceted, complex phenomenon into a set of conceptual
domains or dimensions – enhances description
• Themes – elemental concept emerging from the data
• Theory – idea or opinion ‘that help explain, predict and interpret events
or phenomena of interest’

13. PURPOSE OF DATA ANALYSIS
 Understanding the phenomenon in their
natural context.
 Unearthing links among concepts and
behaviour
 Developing or generating theory

14. CAMPUS RESPONSE TO A
STUDENT GUNMAN,
ASMUSSEN AND CRESWELL,
JOURNAL OF HIGHER
EDUCATION, (1995)
 The researchers collected many
forms of data and displayed those
in a matrix of data-collection types
and sources.
 To begin making sense of this
diverse information, they spent
time reviewing all of the
documents and transcriptions.
 Although they might have
specifically reported this process,
they looked over field-notes from
observations, interview data,
newspapers, and videotapes of the
television coverage to obtain an
initial reading of the campus
reaction to the incident.
Abstract: This qualitative case analysis
describes a campus reaction to a gunman
incident in which a student attempted to fire a
gun at his classmates. Data were collected
through interviews with informants,
observations, documents, and audio-visual
materials. From the case emerged themes of
denial, fear, safety, retriggering, and campus
planning.

15. DATA ANALYSIS
Exploring Coding

16. EXPLORING DATA “You read the
transcripts in their
entirety several times.
Immerse yourself in
the details trying to
get a sense of the
interview as a whole
before breaking it into
parts”
Michael H. Agar
suggested

17. EXPLORING THE GENERAL SENSE OF THE DATA
 A preliminary exploratory analysis in legal research consists of
exploring the data to obtain a general sense of the data, memoing
ideas, thinking about the organisation of the data and considering
whether you need more data.
 Memos are short phrases, ideas, concepts or hunches that occur
to the researcher.
 Writing memos in the margins of field notes, transcripts, or under
photographs helps in this initial process of exploration.

18. CODING THE DATA
 Codes are labels used to describe a segment of a text or an image.
 According to Cresswell (2001), coding is an inductive process of narrowing
data into few themes. Alternatively, coding is the process of segmenting and
labeling text to form descriptions and broad themes in the data.
 Here the researcher selects specific data to use and disregard other data
that do not specifically provide evidence for the research themes.
 The object of Coding Process is to make sense out of text data, divide it into
text or image segments, label the segments with codes, examine codes for
overlap and redundancy, and collapse these codes into broad themes.

19. VISUAL MODEL OF THE CODING
PROCESS IN QLR

20. STEPS IN CODING THE DATA
 Researcher enjoys freedom in coding data, as only a few general
suggestive procedures exist:
Get a sense of the whole. Jot down in the margins some ideas as they come
to mind.
 Pick one document. (e.g. one interview, one field note). Choose the most
interesting, the shortest, or the one on the top of the pile. Go through it,
asking the question “What is this person talking about?” Consider the
underlying meaning and write it down in the margin in two or three words,
drawing a box around it.
Sentences or paragraphs that all relate to a single code are called Text
segments. The next step in coding involves identifying such text segments,
placing a bracket around them, and assigning a code word or phrase that
accurately describes the meaning of the text segment.

21. COMMON ANALYSIS STYLES
CONTENT ANALYSIS NARRATIVE
ANALYSIS
THEMATIC ANALYSIS

22. COMMON ANALYSIS STYLES
 Content – Researchers know what they would look for in the data and then
start with a code book and plough through the data identifying the themes or
patterns and their frequency and relationship
 Thematic – Tends to identify, analyse and report theme out of raw data.
Researcher reads and rereads data, writes down initial ideas and codes, looks
for themes, pulls together the codes, revises themes as reading continues,
defines and labels each of the themes.
 Narrative – Researcher reads participant’s stories, summarising key elements
and narrating in a chronological manner in terms of time and context so that it
is understood better. Here researcher identifies language that may express
emotion and comparison is done in a ‘case-by-case’ basis

23. DIFFERENT ANALYSIS STYLES
Miller and Crabtree present,
‘a continuum of analysis
strategies’ according to the
degree of predetermined or
theoretically founded
categories for interpretation
(Marshall, Rossman, and
Gretchen, 2011)
Quasi-
statistical
Template
Editing Immersion

24. DIFFERENT ANALYSIS STYLES
o This style is objective in nature
o The researcher starts with
predetermined categories or code
book
o Then he looks for connections
between categories
o There is no opportunity to explore
and discover

25. DIFFERENT ANALYSIS STYLES
o The researcher starts with
predetermined categories or ‘sets of
codes’ or templates
o Codes or categories can be revised
based on data as analysis goes on
o The researcher applies codes and
looks for connections interpretively
(based on meaning emerging from
data)

26. DIFFERENT ANALYSIS STYLES
o This style is less prefigured and
starts without a predetermined
category or ‘sets of codes’
o The researcher searches ‘segments
of text’ to develop categories or codes
based on underlying meanings
o Text is reorganised to make meaning
clear

27. DIFFERENT ANALYSIS STYLES
o No predetermined category is set
in this style
o The researcher examines the text
thoroughly and then crystallises
out the most important aspects
and their connection through
intuition-interpretation and
creativity

28. STEPS OF DATA ANALYSIS
Read, listen & watch you data (interviews, field notes or
images)
Select important units of text, audio or video to code
and write comments (memo)
Pull all the data together related to a code or relevant
codes based on research questions or objectives
Read, listen and watch-divide the content into coherent
categories, patterns and themes

29. STEPS OF DATA ANALYSIS
Two broad types of categories, patterns and themes
may emerge
Emic or insiders and analyst constructed or common
and exceptional views, typologies and categories
Label each with attributes and characteristics that
distinguish each from others (Taxonomy)
Describe and classify – move back and forth
between true data and logical constructions

30. STEPS OF DATA ANALYSIS
Analyze (think, compare and contrast) and interpret and explain
cause-consequence or other relationship (Theory)
Look for 1) rival or competing theme & explanation, 2)
Negative cases that does not fit the pattern (deviant cases)
Validate and verify – Triangulation with different perspective,
data, methods and research works
WRITE!