The document discusses data analysis and the discussion of findings in research. It provides details on the differences between data analysis and discussion of findings. It explains that data analysis involves evaluating and analyzing collected data using logical reasoning to form findings or conclusions. It discusses important issues in data analysis like using sufficient datasets and samples and not delegating the analysis. It also discusses common components of qualitative data analysis such as data archiving, exploring cases, finding themes and categories. The discussion of findings section explains that this part involves interpreting results in relation to research questions and existing knowledge to demonstrate what is known, differences found, and how findings extend knowledge in the field.

1. DATA ANALYSIS AND DISCUSION OF THE
FINDINGS /INTERPRETATIONS OF THE FINDINGS
In this section/chapter there are some important
issues to consider in your research
There are differences between data analysis and
discussion of findings

2. DATA ANALYSIS
 The process of evaluating data using analytical and
logical reasoning to examine each component of the
data provided. For instance, may analyze using
various software like SPSS, STATA, EVIEWS and so
forth
 Data from various sources is gathered, reviewed, and
then analyzed to form some sort of finding or
conclusion

3. DATA ANALYSIS
 The process of evaluating data using analytical and
logical reasoning intends to brings the order, structure
and meaning to the mass of collected data.

4. DATA ANALYSIS
 This form of analysis is just one of the many steps that
must be completed when conducting a research
experiment.
 Data from various sources is gathered, reviewed, and
then analyzed to form some sort of finding or
conclusion.

5. DATA ANALYSIS
 Under qualitative analysis may involve various steps
like
 Using the tabulation process in analysing the collected
data
 Employ the percentage process in the analysis
 Using pie chart, bar graphs, line graphs in the analysis

6. Important issues in data analysis
 Use more datasets and samples
This implies that you should use the sufficient
datasets and representative samples in your study
 Do not delegate your data analysis
During the data analysis you should analyse your
findings and delegation is not accepted
 Keep in mind who will be reading your results and
present it in a way that they will understand it

7. Important issues in data analysis
 There are three vital aspects of qualitative data analysis
(1) What are the data “telling” you? What patterns,
themes, and concepts emerge from the data?
(2) Do the themes, patterns and concepts that emerge
from the data conform to what the theoretical
framework indicates should emerge?
Am I seeing what I “expected” to see –based on theory
–or are there unanticipated results? If so, what do
those results mean?

8. Important issues in data analysis
(3) How can I validate the conclusions that I draw from
the data? Have I made sure that I have taken
appropriate steps to ensure that the conclusions that I
draw from the data are justified?

9. Common components in
qualitative data analysis
 The following are some common components in
qualitative data analysis
 Data archiving: This is simply a process of
identifying the cases or respondent in some way and
then storing all of the information you got from that
case or respondent in a retrievable form.
This may be on paper, in a computer program, or in
recorded media.
 This refers as descriptive coding

10. Qualitative data analysis
 Exploring the case or respondent: Exploring and
extracting all of the useful information from each
case or response is a key step in qualitative data
analysis.
There are many ways to do this, but many researchers
find three processes very useful:

11. Qualitative data analysis
 Understanding the case(memos to self)
Memos are your comments about the data –your
initial attempt to understand what your data are
“telling” you. Memos are useful at every step of the
data process.

12. Qualitative data analysis
 Coding by topic
Virtually all data analysis, with or without the use of
statistics, requires coding. People may answer “yes”
or “no” to a question on a questionnaire, for
example, and you will code this as a “1” for yes and a
“0” for no in a spreadsheet.

13. Qualitative data analysis
Analytic coding
 to understand and start to analyze the data. This is
where you start to develop categories. Analytic coding
is the first step in understanding how the cases or
respondents are similar or different, in “making sense”
of the information you have

14. Qualitative data analysis
Finding similarities between cases:
Much more commonly, the researcher's objective
ultimately is to find the commonalities and differences
among the cases or respondents.
There are many ways to go about this, but will discuss
only two fairly common procedures
Finding themes
Creating categories

15. Qualitative data analysis
 Common Themes
Many researchers first search for common themes that
emerge from the data. This often emerges from the
topical and analytical coding process.
 Categories
Ultimately, just as statistical analysis collapses
individual cases or respondents into groups, most
qualitative analysis has the same goal. There are some
research projects where this does not occur.

16. Qualitative data analysis
Higher level abstraction
Essentially, you are looking for characteristics of the
respondents that co-vary or “go together” across
categories. At this point in your data analysis, you start
to concentrate on exploring the similarities and
differences between cases
Finding relationships between categories
Creating typologies: means to the study the different
types respondents/characteristics

17. Qualitative data analysis
Explaining and understanding
For many researchers (but not all), understanding is
the final goal of data analysis.
Seeking synthesis
For some researchers, the goal of the data analysis is to
“explain the big picture” –to synthesize. One way to
think about this is that you want to “tell a story.”

18. Qualitative data analysis
Understanding differences
For many researchers, understanding the differences
or how cases diverge can be more important and more
telling that understanding what they have in common.
The researcher wants to make sense of diversity, to
focus on the contrasts, not the commonalities.
Validating
All of the procedures and “self checks” described
above help make sure that you reach valid and reliable
conclusions.

19. Quantitative data analysis
 Should observe the model specifications understudy
and interpret accordingly otherwise misinterpreting
may spoil your whole research work
 A researcher has to describe the empirical
relationships amongst the variables (dependent and
independent variables ) obtained from the analysis
accordingly
 Should present the assumptions underlying the study
and its empirical evidences

20. Quantitative data analysis
 Should understand how to interpret based on the
nature of the model specified being time series, panel
data or logistic regression but to mention a few
 Remember to present the findings as per unit of
measurements employed in your study being it units,
dollars, TZS, tonnes, age, gender and so forth.
 In your analysis, you should fit in the model you
estimated in your study (research work)

21. Qualitative and Quantitative data
analysis
 NB:
 Every figures should have title on top corner left and at
the bottom left must indicate the source (s)
 Again, on every tables in the study should have the
title on top corner left and at the bottom left indicate
the source(s)

22. Discussion of the findings/interpretations
 This section of the Research Gateway shows you how
to discuss the results that you have found in relation to
both your research questions and existing knowledge.
 This is your opportunity to highlight how your
research reflects, differs from and extends current
knowledge of the area in which you have chosen to
carry out research.

23. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 This section is your chance to demonstrate exactly
what you know about this topic by interpreting your
findings and outlining what they mean.
 At the end of your discussion you should have
discussed all of the results that you found and provide
an explanation for your findings.

24. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 A Discussion section should not be simply a summary
of the results you have found and at this stage you will
have to demonstrate original thinking.
 First, you should highlight and discuss how your
research has reinforced what is already known about
the area.

25. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 Many students make the mistake of thinking that they
should have found something new; in fact, very few
research projects have findings that are unique.
 Instead, you are likely to have a number of findings
that reinforce what is already known about the field
and you need to highlight these, explaining why you
think this has occurred

26. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 Second, you may have discovered something different
and if this is the case, you will have plenty to discuss!
 You should outline what is new and how this compares
to what is already known.
 You should also attempt to provide an explanation as
to why your research identified these differences.

27. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 Third, you need to consider how your results extend
knowledge about the field.
 Even if you found similarities between your results
and the existing work of others, your research extends
knowledge of the area, by reinforcing current thinking.
 You should state how it does this as this is a legitimate
finding!

28. DISCUSION OF THE FINDINGS/INTERPRETATIONS
 It is important that this section is comprehensive and
well structured, making clear links back to the
literature you reviewed earlier in the project.
 This will allow you the opportunity to demonstrate the
value of your research and it is therefore very
important to discuss your work thoroughly.

29. Sources of secondary data
 Federal reserve bank of st. luois fred database (FRED:
ECONOMIC RESEARCH)
 Faostat.fao.org
 Ivan kushnir research centre data base
 World bank database
 BOT
 NBS
 REGULATORY AUTHORITIES/AGENCIES
 Social security database