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Analysis of Data Collected
Data analysis is the process of developing responses to questions by reviewing and interpreting the data. The basic steps of the analysis process are to identify problems, determine the availability of appropriate data, decide on appropriate methods for answering questions of interest to apply the methods, and evaluate, synthesize and communicate the results.
Data analysis is essential to understand the results of surveys, administrative sources and pilot studies; provide information on data gaps; to design and redesign surveys; for the planning of new statistical activities; and for the formulation of quality objectives.
Main Points of Analysis
Results and Analysis section of dissertation has four purposes, it should:
- Interpret and explain results
- Answer the research question
- Justify approach for data collection
- Critically evaluate study
Dissertation Data Analysis Help
Dissertation Educators ensure that results are clearly established, support assertions with arguments and strongly support empirical support. We have qualified experts to analyse and evaluate the data collected. Dissertation Educators assure that data is well presented in tables, charts and diagrams, but must also use words to guide readers through your data:
- Explain the tests performed & their reasons.
- Explain the tactics and tools used to gather the data.
- Present results in appropriate formats
- Indicate significance of the results
- Make meaningful comparisons & draw conclusions.
Dissertation Educators can help you with data analysis using SPSS, Nvivo and Excel. We can apply all the tests and conduct all formats of data analysis. When discussing your data, our experts demonstrate an ability to identify trends, patterns and themes in the data. We think of various theoretical interpretations and balance the advantages and disadvantages of these different views.
Dissertation Educators manage its experts to discuss anomalies and consistencies, assessing the importance and impact of each. The results and discussion are probably the most important sections of dissertation. Once completed, you can begin to relax a little: You are in the final stage of writing!
You can call Dissertation Educators for your dissertation help. We are always available at [email protected]
Dissertation Result Chapter
We have assembled this exceptionally complete, extremely helpful guide on the most proficient method to review the outcomes area of your paper. To enable you to further, we’ve separated the data into both quantitative and subjective outcomes, so you can revolve around what applies to you most.The qualitative results have one independent and one dependent variable. An independent variable is the one that is under control and a dependent variable is the result variable. You likewise need to think about how best to show your outcomes: tables, figures, charts, or content. Attempt to utilize a wide range of techniques for introduction, and think about your reader: 20 pages of thick tables are difficult to comprehend, as are five pages of diagrams, yet a solitary table and well-picked chart that represent your general discoveries will make things much clearer.
Dissertation Data Analysis
For writing the dissertation data analysis you should not blindly follow the research work and data gathered. It is critical that you utilize strategies fitting both to the sort of information gathered and the points of your research. Quantitative data that is simply the research (technical and scientific) usually requires statistical data analysis. With quantitative data, you can easily help to come to conclusion and generalize. Qualitative data is normally text-based and not numerical but it also needs to be analyzed thoroughly. Make sure that the data is hundred percent authentic and is free of all errors in such cases a careful action is required. It can be hard to speak to extensive volumes of information incomprehensible ways. Keeping in mind the end goal to address this issue, consider every conceivable mean of showing what you have gathered. Outlines, charts, graphs, statements and formulae all give novel points of interest in specific circumstances .information is important however difficult to sort out inside of the content, you should need to move it to an index.In discussing your data you need to understand the trends and patterns within the theme of your data.
Statistics is the numerical or the mathematical way of presenting all the interpretations and explanation of your data. A dissertation statistics is the facility that can make sure that you are going in the right direction. It helps to make sure that your dissertation is right on point and accurate. It ensures the quality of your dissertation. A dissertation statistics benefit gives significant input, help, and help to students as they take a shot at their papers. Since students are understanding that it is smarter to get help than to battle alone, thesis measurements administrations are ending up increasingly well known. It is along these lines less demanding to get the assistance of a paper measurements benefit. In this way, a thesis statistics will help with the get-together of information, with the translating of that information, with the investigating of that information, and with the use of that information to the exposition. Every single one of these means is fairly troublesome and tedious, however with the assistance of thesis measurements.
Qualitative & Quantitative Analysis
Qualitative analysis is already complete. This kind of analysis included the larger picture of the issue in order to go deeper and understand the problem easier. It is the social interaction and it allows fine distinction and at the same time, it is also not necessary to crowd the data into further classification. In this type of analysis, researchers are known as participants. Whereas quantitative analysis is completely different in quantitative we count the feature classify them and turn them into more complex numerical data or models. However, the picture of data obtained attained from quantitative is less rich then qualitative. In order to ensure statistical test, it is very important that minimum frequencies are obtained. Such kind of approach gives the idea to generalize the examples easily. Dissertation quantitative data analysis helps us to know that which phenomenon is the decent reflection of the behavior of language. The more essential undertaking of simply taking a gander at a solitary dialect assortment enables one to get an exact photo of the recurrence and uncommonness of specific wonders and in this manner their relative ordinariness or abnormality.
Dissertation Data Analysis Help
Data forms the major part of your dissertation so it has to errorless. Once you have gathered all the data without any loophole next all you need to do is go for data analysis. Analyst suggests that data makes the dissertation more understandable and practical and it is also very important that how you can gather and analyze the data collected from sources. Our dissertation data analysis services specialization contains:
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How to write your dissertation data analysis chapters.
Qualitative data analysis
by Tilahun Nigatu Ha…
How to write your dissertation data analysis chapters.
The Free School
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PhD Candidate at University of Cape Town
University of Cape Town
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Cooperative Agreement Specialist at Centers for Disease Control and Prevention
Cooperative Agreement Specialist
Strategy | Demography | Research Consulting
How to write your dissertation data analysis chapters.
How to write a (winner) doctoral thesis series
Writing your data analysis chapters
This presentation aids you to write your data analysis
chapters for a thesis or dissertation.
● This presentation is useful for candidates who have written one or
more chapters and want to improve these drafts.
● This presentation contains information that is useful for your
introductory chapters (e.g. literature review)
How data analysis chapters differ from your
Your introductory chapters set up the
thesis. The data analysis chapters are
where you advance scholarly
knowledge by interpreting evidence.
Your data analysis chapters connect
your primary and secondary data to
your research question.
2012 2013 2014 2015
This dissertation is a story
about structural racism that
exists in society at large.
Each data analysis chapter is
a separate argument that
seeks to prove that the
underlying social problem
which the thesis explores is a
story about structural racism.
Data chapter one:
Data chapter two:
Data chapter three
Go solo, it’s
Most doctoral thesis examiners expect
you to bring your voice to the forefront of
discussion because a doctoral student is
a serious scholar. You should avoid
quoting other theorists directly too often.
Jones (1960, p. 8) argues that racial
health inequalities exist on three main
There are three core domains of racial
inequality that exist in the healthcare
system (Jones, 1960, p. 8).
Your data analysis should synthesise the corpus of literature.
Your theoretical discussion should explicitly advance your field.
Camara Phyllis Jones
Edward Said Iris Marion Young
Orientalism (1979) Justice and the Politics of
a liberal of minority rights
Add your name to this wall of scholars. Your thesis makes a substantial contribution to your field.
Plan your data analysis chapter before writing it
Writing without a plan might waste time, e.g. you might write thousands of excess words.
3. Ask yourself: how does
the conceptual literature
aid you to understand your
data and to pursue your
4. Plan a
5. Write your
6. Edit your
Planning the structure of each data analysis chapter
As a general rule, each data analysis chapter requires at least three core elements:
(1) An introduction
An opening sub-section that introduces the core theoretical argument of the chapter and explains how
this links to the research objective of your thesis. You may outline the chapter structure and volunteer
other details such as the names of key theorists and theoretical works and briefly state how these
shape discussion. Link this opening to the closing section of the previous chapter.
(2) A middle section which is your analytical argument.
In this section you analyse data using conceptual theory to advance the literature to achieve your
(3) A conclusion section.
Parts (1) and (3) are easiest as they normally are brief. Part (2) is more difficult part to plan.
These structures are acceptable if the scholar writes
cumulatively to advance a core theoretical argument.
Write paragraphs cumulatively. Your chapter
is the largest circle. It has one core message.
Chapter argument 1
Some people ostracize
speakers of foreign languages
merely because they do not
understand the other person’s
Chapter argument 2
Some people reject speakers of
a foreign language for reasons
that concern socio-linguistic
differences between the
foreigner’s culture and their
Core chapter argument
Theories of xenophobia Theories of xenophobia
Theories of xenophobia (e.g.
Hjerm, 1998) are the
common thread throughout
the entire chapter.
(2) Data analysis section – substructure (continued)
Your conceptual data analysis requires at least one theoretical discussion per chapter
that uses evidence to advance your argument. There is no maximum number of
theoretical arguments you may put forward per chapter. The ‘golden’ rules of this
(i) There must be one overarching theoretical argument that runs through each data
analysis chapter – the ‘core message’ you wish to communicate in that chapter.
(ii) If you have two more more sub-arguments, then these arguments must be
interconnected to the overall theoretical argument that shapes the chapter.
Always write cumulatively within a chapter and between chapters.
Open your paragraphs with an argument
When writing the body of your chapter, i.e., the sections where you analyse data,
always write the first sentence as an argument that you plan to explore in that
paragraph. Do not open your paragraph with a narrative statement.
Flawed opening (narrative)
Jones’s (1960) research examines aspects of inequality in the healthcare system.
Good opening (argumentative)
The three dimensions of racism that Jones (2002, p.9) argues comprise
“institutionalized racism” are explicit in Immigration policy documents published the
Australian Government during the 1950s. For example, a document titled …
‘Sandwich’ paragraphs in data analysis chapters
You should aim to write paragraphs using the ‘sandwich’ style when analysing data.
The bread is your theory. The filling is your data analysis.
1. Open each paragraph with a conceptual argument that cites a theorist.
2. Next, analyse data using conceptual principles to illustrate your theoretical argument.
3. Close the paragraph by showing how your analysis proves your opening argument.
Point 3 :
(a) Do not repeat point 1 (opening). Find an inventive/fresh way to restate your point.
(b) Connect this statement to next paragraph so that you advance your argument cumulatively.
Analysing qualitative data in a sandwich
When analysing qualitative data you normally do not need to list every piece of evidence that fits a
pattern if this body of evidence is large. It is usually acceptable to cite a representative piece of data
and explicitly inform you audience that this evidence is a representative sample. For example:
It is evident that xenophobia, which is rooted in linguistic differences (Hjerm, 1998, p. 340) exists in
regional Australia. This theme recurs in free-flowing speech extracted from the semi-structured
interview. Of the 30 volunteers who participated in the interview, the vast majority (28 of 30) stated
that they are wary of befriending persons who speak English as a foreign language. This statement
by participant number 17, ‘Maria’ is representative of those made by these participants:
I avoid making friends with people who don’t speak English as a first language, as
I just assume we don’t have too much in common (Maria, 2014, NP).
This evidence supports Hjerm’s (1998) theory because these volunteers show no evidence that they
might change their mind in future or engage with the ‘other’ on rare occasion.
Analysing quantitative data in a sandwich
Quantitative data should likewise be analysed within a theoretical sandwich. If you use a diagram
such as a table, graph or chart, do not use this diagram to complete the paragraph. You must
complete the paragraph by inserting a theoretical discussion under the diagram. For example:
Institutionalised racism (Jones, 1960) within the healthcare system is evident from data captured from
national healthcare databases. Table 5 illustrates this argument with reference to colon cancer rates:
Table 5: Newly diagnosed colon cancer rates by racial group (2000 to 2015)
(Source: National Health System, 2016)*
This data is indicative of institutionalised racism, because the disadvantage of the minority group is
disproportionate to their representation in the population.
* Fictitious data and reference for illustration
Racial group % of the population % of colon cancer rates
White 70% 20%
Other 30% 80%
Weigh up the evidence in the conclusion section of each
data analysis chapter
This data analysis
chapter is about a
principle known as
all discussion in this
chapter must connect
Xenophobia based on linguistic
factors is one explanation for
why youth from non-English
experience social exclusion in
England. When weighing up the
evidence, it is clear that the fear
of not understanding a foreign
language is the dominant factor
that explains xenophobia in the
differences between cultures are
also a factor.
Further reading, open access (yes, free!)
Becker, H. (2002). Terrorized by the literature (Chapter 8, pp. 135-149), in Writing
for social scientists: How to start and finish your thesis or article, Chicago, Illinois:
University of Chicago Press. <http://users.soc.umn.edu/~uggen/Becker_86.pdf>.
This chapter gives good insight into how to use scholarly theory and principles to
shape the ways that you conceptualise your discussion and analysis of data.
Murray, R. (2011), How to write a thesis, New York, USA: McGraw Hill/Open
is a 340 page book. I suggest you access it now before it is removed..
See chapter 8 (pp. 238-257) – structuring your chapters: planning and writing.
Hjerm, M. (1998). National identities, national pride and xenophobia: A comparison of four Western
countries, Acta Sociologica, 41(4), 335-347.
Jones, C. (2002). Confronting institutionalized racism, Phylon , 7-22.
Kymlicka, W. (1995). Multicultural citizenship: A liberal theory of minority rights. Oxford, England:
Said, E. (1979), Orientalism. New York, USA: Vintage.
Young, I. M. (1990), Justice and the politics of difference, New Jersey, USA: Princeton University Press.
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