This document provides guidance on conducting data analysis and presenting findings for a research project. It discusses inductive and deductive reasoning in data analysis. It also outlines various types of data that may be collected, including interviews, questionnaires, observations, and action research. Methods of coding and thematically analyzing qualitative data are described. The document emphasizes presenting data in a clear and relatable way through the use of sub-headings, integration of different data sources, and letting the data speak for itself. It provides recommendations for structuring chapters on findings, discussion, and conclusions in a research report.

1. Session 3
Keith Turvey and Irena Andrews
MA Education (Teaching Leaders)
KV713 Research Project
The Dissertation 2014/15

2. Data analysis: unraveling

3. Reasoning

4. Inductive and deductive reasoning
“Although both deduction and
induction have their weaknesses,
their contributions to the
development of science are
enormous and fall into three
categories:1 the suggestion of
hypotheses; 2 the logical
development of these hypotheses;
and 3 the clarification and
interpretation of scientific findings
and their synthesis into a
conceptual framework.”
Cohen, Manion & Morrison (2000, P.5)

5. Your data might be:
•Interview transcripts
•Questionnaire response
•Observation schedules
•Action research: baseline +
innovation + recordings of effect
•Content analysis

6. Activity: Coding and thematic analysis
Using the extract from a group
interview with student teachers:
Read through the transcript once
Read it again and this time
annotate the transcript to identify
any themes that emerge for you
Share your keywords/themes with
a partner. Are there any
similarities
Discuss with the rest of the group.
What themes/codes would you
identify in this transcript

7. Presentation of data
(Bassey 1981:85)
An important criterion for
judging the merit of a case
study is the extent to which the
details are sufficient and
appropriate for a teacher
working in a similar situation to
relate his decision- making to
that described in the case
study. The relatability of a case
study is more important than its
generalisability

8. In analysis, interpretation
and presentation of data
Do not attempt generalisations
based on insufficient data
Do not claim more for your results
than
the evidence warrants
Small scale studies can inform,
illuminate and provide a basis for
policy decisions within an
institution

9. Reporting the findings
• Look for similarities, groupings,
clusters and items of particular
significance
• You may have ideas about this before
you start collecting your data – a
balance of ‘informed hunch’ and the
influence of pre-conceived ideas
• The literature can provide helpful
guidance
• It is worthwhile thinking about which
types of data and how it can be
analysed at the start
• Experiment with different ways of
presenting findings: Bar charts; pie

10. Statement of Results
Text, supported by tables, figures,
quotations
Tables, charts, graphs and quotes
should illustrate and illuminate the text
and help the reader to understand
complex data
The text should not describe what the
data shows but draw attention to what
is most important
Number tables and figures
Be clear about what information is
needed in the text and what should be
in the appendices

11. Presentation of data
Use sub –headings – these could be
• Research questions
•Identified themes
Present data under each
• Integrate data – bring together data
from questionnaires, interviews,
observations etc
• Explain the data , be precise: ‘ A
majority (4 out of 5) of respondents
indicated that.....’
• Let the data do the talking

12. Sample chapters
•Look at the examples
provided
•Draw up a list of good
ideas/things that work well
•Questions coming out of this
•What will you do?

13. Analysis and Discussion
Re-state the research question –
remember context
• Synthesise the results in such a way
as to allow a new perspective to be
reached
• Make links, comparisons and
contrasts, juxtapose results with the
findings of others
• Consider the results in the light of
the literature
• And also in the light of the
methodological approach

14. Analysis and Discussion
•Explain limitations in
research design -suggest
more appropriate approaches
•Draw out implications for
improvement of practice
• What evidence do you have
now to support new
knowledge?
•Avoid speculation that goes
beyond the evidence

15. Relationship between literature and project
elements
The literature Elements of your project
Rationale
Research design
Topic literature
Methodological
literature
Findings
Discussion
Conclusions
Recommendations

16. Structure
Abstract
Acknowledgements
1. Introduction 10%
2. Literature review
25%
3. Methodology 20%
4. Findings and Analysis
35%
5. Conclusions and
recommendations 10%
References

17. Timescales
March 07 Data coding
and
presentation
DRAFT Methodology
Updated Lit Review?
April Skype/
telephone call
Planning fieldwork?
May 08 Skype/
telephone call
Progress review
Sample of data analysis?
20 June
08.45 –
15.30
Round table
discussion
Defend methodology
Ethical issues?
03 July Progress
review and
formative
assessment to
discuss:
• Data analysis and writing
up
• September 17 or
November 19 submission?

18. Round table discussion
• 10 minutes to introduce their
research problem and question and
justify their chosen research
methodology.
• Following this, the presenter can
choose to ask the audience for
comments on one or two
methodological issues or invite the
audience to ask questions (5
minutes).