How to analyse data collected from interviews, observations and field notes and make it meaningful for readers

1. DATA ANALYSIS
Dr Ramakanta Mohalik

2. Points to be Discussed
• Data Analysis for Quantitative Research
• Data Analysis for Qualitative Research
• Data Analysis for Mixed Research

3. Research Continuum
Research Continuum
Quantitative Mixed Qualitative

4. A. Quantitative Research
• It relies on collection of quantitative data
(numerical data) from tests, scales and
checklist.
• The research method like experimental,
causal comparative, correlational,
• Examples: Impact of cooperative learning
on achievement in social science at
secondary level.

5. A. Quantitative Research
• There are various statistical techniques
such as Descriptive and Inferential are
used for analyzing data.
• The Computer software like MS EXCEL
and SPSS are useful for quantitative
analysis.

6. B. Qualitative Research
• It relies on the collection of qualitative data
(non numerical data) such as words and
pictures.
• Data are collected from interviews,
observation, focus group discussion,
documents, physical artifacts, field notes
etc .
• The research like phenomenological,
ethnography, case study, grounded
theory, historical

7. B. Process of Qualitative Data
Analysis
• Examples: Identification of professional needs
of teachers at secondary level. An analysis of in-
service teacher education programmes
organised by SCERT, Odisa
• Qualitative data can be analysed by use of
Computer Aided methods for Qualitative Data
Analysis (CAQDAS). The software like Lexical
analysis of SphinxSurvey, Non-Numerical
Unstructured Data Indexing Searching and
Theorising (NUD-IST), ATLAS.ti

8. Data collection
Constructing diagrams,
Tables graphs
Corroborating &
Validating result
Data entry & storage
Segmenting, coding &
Developing category system
Identify relationship
Themes, pattern

9. B1. Data collection
• It relies on the collection of qualitative data
(non numerical data) such as words and
pictures.
• Data are collected from interviews,
observation, focus group discussion,
documents, physical artifacts, field notes
etc

10. B2. Data entry & Storage
• The data collected by using different tools to be
Transcribe for analysis.
• Transcription is the process of transforming
qualitative data such as audio recordings of
interviews or field notes written from observation
into typed text. The typed text is known as
Transcript.
• The researcher can use Voice recognition
computer programme ( Via Voice and Dragons
Naturally Speaking) for transcription

11. B3. Segmenting, Coding and
Developing Category System
• The text data is to be segmented for
analysis. Segment is a meaningful unit,
may be a word, a single sentence or
several sentences or larger paragraph.
• Segmenting is process of dividing the text
data into meaningful analytical units.

12. B3. Coding and Developing
Category System
• Coding is the process of marking
segments of data with symbols,
descriptive words or category names.
• How to develop codes? There are two
kinds of codes-Inductive and A priori
codes.
• Inductive codes: are generated by the
researcher by directly examining the data
during the coding process.

13. B3. Coding and Developing
Category System
• A priori codes: are developed before
examining the current data may be before
data collection.
• Categories are basic building blocks of
qualitative data analysis. It is a point or
themes of researcher interest. Category
consist of many segments. Researcher
may think for sub-categories under each
category. For eg Teaching-qualities of
teacher, students, teaching aid

14. B4.Identifying Relationship among
Categories
• After categorizing the data, researcher
need to think for relationship among
different categories and sub-categories.
• For qualitative researcher relationship
refers to many kinds of relations or
connections including but not limited to
variables.
• Many kinds of relationship is possible like

15. B4.Different Relationship among
CategoriesTitle
• Strict inclusion
• Spatial
• Cause-effect
• Rationale
• Location for action
• Function
• Means and end
• Sequence
• Attribution
Form of relationship
• X is a kind of Y
• X is a place in Y, X is part
• X is result of Y
• X is reason for doing Y
• X is place for doing Y
• X is used for doing Y
• X is way to do Y
• X is a step in Y
• X is an attribute of Y

16. B5.Drawing Diagrams
• A useful tool for showing relationship
among categories is called Diagramming.
• Diagram is a plan, sketh,drawing, or
outline designed to demonstrate or explain
how something works or to clarify the
relationship between the parts of a whole.
• The diagrams such as Network diagram,
Flow charts

17. B6. Corroborating and Validating
Results
• As qualitative research is subjective,
influenced by researcher bias, its validity
must be established.
• There are different strategies to promote
validity.

18. B6.Validating Results
Strategy
1. Researcher as
detective
2. Extended field work
3. Low inference
descriptors
4. Triangulation
(data/method/investi
gator/theory)
Description
1. Careful consideration of
potential cause and effect
and systematically
eliminating rival explanation
until final case is made
2. Collect data over an
extended time period
3. Direct quotations/participants
account/field notes are
commonly used low
inference
4. Cross checking information
and conclusion through use
of procedures or sources

19. B6.Validating Results
Strategy
5. Participant feedback
6. Peer review
7. External audit
8. Reflexivity
Description
5. The feedback and discussion
of researchers interpretation
and conclusion with actual
participants
6. Discussion with peers who are
familiar/unfamiliar with
research help to provide check
7. Using outside expert to assess
study quality
8. Self awareness & critical
reflection by researcher on his
potential biases and
predispositions.

20. C.Data Analysis in Mixed Research
• Here researcher uses both quantitative
and qualitative analytical techniques in a
single study.
• Researcher may utilise both technique at
same time (concurrently) or different
times (sequentially)