Unit 13 : Computing Research Project
Analysing data
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Learning Outcomes
▪ By the end of this unit students will be able to:
▪ LO1 Examine appropriate research methodologies and
approaches as part of the research process.
▪ LO2 Conduct and analyse research relevant to a
computing research project.
▪ LO3 Communicate the outcomes of a research project to
identified stakeholders.
▪ LO4 Reflect on the application of research methodologies
and concepts.
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Pass , Merit and Distinction criteria For this section
P3 Conduct primary and secondary research using appropriate methods for a
computing research project that consider costs access and ethical issues.
P4 Apply appropriate analytical tools, analyse research findings and data.
M2 Discuss merits, limitations and pitfalls of approaches to data collection and analysis
D1 Critically evaluate research methodologies and processes in application to a
business research project to justify chosen research methods and analysis.
Analysing data
USING DATA COLLECTION
TOOLS
USING ANALYTICAL
TECHNIQUES
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Using data collection tools
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Questionnaire
Nowadays questionnaire is widely used for data collection in social research.
It is a reasonably fair tool for gathering data from large, diverse, varied and scattered
social groups.
The questionnaire is the media of communication between the investigator and the
respondents.
A questionnaire is a list of questions sent to a number of persons for their answers and
which obtains standardized results that can be tabulated and treated statistically
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Framing a Questionnaire
Size of The Questionnaire Should Be Small:
The Questions Should Be Clear:
The Questions Should Be Arranged in A Logical Sequence:
Questions Should Be Simple To Understand:
Questions Should Be Comprehensive & Easily Answerable:
Questions of Personal & Sensitive Nature Should Not Be Asked:
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Types of Questions:
Shut Questions: Shut questions are those where possible answers are suggested
by the framers of the questionnaire and the respondent is required to tick one of
them. Shut questions can further be subdivided into the following forms:
▪ Simple Alternate Questions: In this type of questions, the respondent has to
choose from the two clear cut alternatives like ‘Yes’ or ‘No’, ‘Right or Wrong’ etc.
▪ Multiple Choice Questions: Many times it becomes difficult to define a clearcut alternative and accordingly in such a situation additional answers between
Yes and No, like Do not know, No opinion, Occasionally, Casually, Seldom etc.,
are added.
▪ Do you have coffee?
(a) Yes regularly (b) No never (c) Occasionally
(d) Seldom
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Types of Questions:
Leading Questions Should Be Avoided:
Question that prompts or encourages the answer
wanted
▪ ‘Why do you use a particular Laptop, say Asus or Dell’
should preferably be framed into two questions(i) Which Laptop do you use?
(ii) Why do you prefer it?
▪ It gives smooth operations
▪ It is cheaper
▪ It has more options
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Types of Questions:
▪ Cross Checks:
▪ The questionnaire should be so designed as to provide internal checks
on the accuracy of the information supplied by the respondents by
including some connected questions at least with respect to matters
which are fundamental to the enquiry.
▪ Pre-Testing the Questionnaire:
▪ It would be practical in every sense to try out the questionnaire on a
small scale before using it for the given enquiry on a large scale.
▪ This has been found extremely useful in practice.
▪ The given questionnaire can be improved or modified in the light of the
drawbacks, shortcomings and problems faced by the investigator in the
pretest.
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Types of Qualitative Research
Interviews
Structured Interviews
▪ The main purpose of structured interviews is asking the same set of questions from every
participants. This makes it easier to compare data between participants or groups later. To
maintain consistency across interviews, it’s important to follow these guidelines:
▪ All questions should be written in advance (including probes)
▪ Questions should be written in great detail so that they can be used verbatim during interviews.
▪ The sequence of questions should be pre-decided and consistent across interviews.
Example of a Structured Interview Question:
▪ Thinking back to your childhood days in Kandy, can you remember what kind of local
music was popular at the time?
▪ Probes:
▪ Why do you think it was so popular?
▪ Where was it played?
▪ Were there other popular genres?
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Types of Qualitative Research
Interviews
Semi-structured Interviews
▪ In semi-structured interviews, you prepare an interview guide that
describes which topics will be explored during the interview, but the
actual questions are not pre-written.
▪ Interviewer has the freedom to word their questions spontaneously and
explore topics in more detail.
Example of a Semi-Structured Interview Question
▪ What problems did the participant face growing up in the community?
▪
▪
▪
▪
Personal.
Education-related.
Related to their immediate family.
Related to the community in general.
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Types of Qualitative Research
Interviews
Unstructured Interviews
▪ In this type of interview, neither the questions nor the topics are
pre-decided.
▪ Questions are formulated during the interview based on what the
interviewee observes or hears during the conversation.
▪ As a result, each unstructured interview is different, and the questions
change over time.
▪ You may choose to use one or all of these interview methods in
your research. Once you’ve decided, you’re ready for the next
step.
▪ Deciding What Information You Need
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Using analytical techniques such as trend
analysis, coding or typologies
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Qualitative Data Analysis methods
▪ Qualitative research is about putting oneself in another
person’s shoes and seeing the world from that person’s
perspective, the most important part of data analysis
and management is to be true to the participants.
▪ It is their voices that the researcher is trying to hear, so
that they can be interpreted and reported on for others
to read and learn from.
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Coding
Step 1
Developing and Applying Codes: Once all of the research interviews have been
transcribed and checked, it is time to begin coding.
Step 2
Identifying themes, patterns and relationships. Unlike quantitative methods, in
qualitative data analysis there are no universally applicable techniques that can
be applied to generate findings.
Step 3
Summarizing the data. At this last stage you need to link research findings to
hypotheses or research aim and objectives
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Quantitative Data Analysis Methods
▪ Quantitative studies result in data that provides quantifiable,
objective, and easy to interpret results. The data can typically be
summarized in a way that allows for generalizations that can be
applied to the greater population and the results can be
reproduced.
▪ The first step in quantitative data analysis is to identify the levels
or scales of measurement as nominal, ordinal, interval or ratio.
▪ The next step would be to use descriptive statistics to summarize
or “describe” the data.
▪ It can be difficult to identify patterns or visualize what the data is
showing if you are just looking at raw data.
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Commonly used descriptive statistics
FREQUENCIES
PERCENTAGES
MEAN
MEDIAN
MODE
A COUNT OF THE
NUMBER OF
TIMES A
PARTICULAR
SCORE OR VALUE
IS FOUND IN THE
DATA SET
USED TO EXPRESS
A SET OF SCORES
OR VALUES AS A
PERCENTAGE OF
THE WHOLE
NUMERICAL
AVERAGE OF THE
SCORES OR
VALUES FOR A
PARTICULAR
VARIABLE
THE NUMERICAL
MIDPOINT OF THE
SCORES OR
VALUES THAT IS
AT THE CENTER
OF THE
DISTRIBUTION OF
THE SCORES
THE MOST
COMMON SCORE
OR VALUE FOR A
PARTICULAR
VARIABLE
MINIMUM AND
MAXIMUM
VALUES (RANGE)
THE HIGHEST AND
LOWEST VALUES
OR SCORES FOR
ANY VARIABLE
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Inferential Statistics
▪ Inferential statistics examine the differences and
relationships between two or more samples of the
population.
▪ These are more complex analyses and are looking for
significant differences between variables and the
sample groups of the population.
▪ Inferential statistics allow you test hypotheses and
generalize results to population as whole.
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List of basic inferential statistical tests
▪ Correlation
▪ such as strong, negative positive, weak, or statistically significant.
▪ If a correlation is found, it indicates a relationship or pattern, but keep in
mind that it does indicate or imply causation
▪ Analysis of Variance (ANOVA)
▪ tries to determine whether or not the means of two sampled groups is
statistically significant or due to random chance.
▪ The ANOVA will tell you if the difference is significant, but it does not
speculate regarding “why”.
▪ Regression
▪ used to determine whether one variable is a predictor of another
variable.
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