This document provides guidance for students on data analysis and presentation for a research project. It discusses inductive and deductive reasoning approaches. Students are encouraged to code interview transcripts and other data sources to identify themes. When presenting findings, students should integrate different data sources and use tables, figures and quotes to illustrate key points while explaining the significance of the data. Limitations of the study and implications for practice should also be discussed. The document outlines the expected structure for research chapters and timescales for completing tasks like data coding and analysis.

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
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
(Bassey, 1981, p.85)

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 charts; histograms
• Think about how you will weave quotations into
the narrative

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 presented

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

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
Appendices

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?
August

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).