Research 1

1. RESEARCH ETHICS & INTREGRITY
G L E N N T I C M A N V I L L A N U E V A
B S E D – E N G L I S H I I I

2. What are research ethics and
research integrity and why are
they important?
Research ethics and integrity practices
make sure that research is conducted
according to the highest standards of
practice, and with the minimal risk of
adverse or harmful outcomes or
consequences.
The research community and a wider public
will have confidence in the outcomes of
your research and the quality of your
research output will be enhanced.

3. Why are they
important?
Research is conducted honestly
Provides confidence that
conclusions drawn from research can
be relied upon to be accurate
Minimizes potential risks to
researchers and participants of
research, protecting the vulnerable
and ensuring their safety and
wellbeing.

4. Why are they
important?
Safeguards data collected during the
course of research, particularly sensitive
data, respecting confidentiality
Avoids unfair allegations of
misconduct, whilst ensuring that
genuine concerns are appropriately
investigated
Prevents people being drawn into
terrorism Ensures conflicts of interest
are identified and avoided

5. Ethical Standards of
Research
GLENN TICMAN VILLANUEVA
BSED – ENGLISH III

6. Ethical Standards of Research
The checklist lists the key points of good practice in research for a research project and
is applicable to all subject areas.
1. Does the proposed research address pertinent question(s) and is it designed either to
add to
existing knowledge about the subject in question or to develop methods for research
into it?
2. Is your research design appropriate for the question(s) being asked?
3. Will you have access to all necessary skills and resources to conduct the research?

7. Ethical Standards of Research
4. Have you conducted a risk assessment to determine:
- whether there are any ethical issues and whether ethics review is
required;
- the potential for risks to the organization, the research, or the health,
safety and well-being of
researchers and research participants; and
- what legal requirements govern the research?

8. Ethical Standards of Research
5. Will your research comply with all legal and ethical requirements and other
applicable guidelines,
including those from other organizations and/or countries if relevant?
6. Will your research comply with all requirements of legislation and good practice
relating to
health and safety?
7. Has your research undergone any necessary ethics review (see 4(a) above), especially
if it
involves animals, human participants, human material or personal data?

9. Ethical Standards of Research
8. Will your research comply with any monitoring and audit
requirements?
9. Are you in compliance with any contracts and financial guidelines
relating to the project?
10. Have you reached an agreement relating to intellectual property,
publication and authorship?
11. Have you reached an agreement relating to collaborative
working, if applicable?

10. Ethical Standards of Research
12. Have you agreed the roles of researchers and
responsibilities for management and supervision?
13. Have all conflicts of interest relating to your research
been identified, declared and addressed?
14. Are you aware of the guidance from all applicable
organizations on misconduct in research?

11. Ethical Standards of Research
When conducting your research:
1. Are you following the agreed research design for the project?
2. Have any changes to the agreed research design been reviewed and approved if
applicable?
3. Are you following best practice for the collection, storage and management of data?
4. Are agreed roles and responsibilities for management and supervision being fulfilled?
5. Is your research complying with any monitoring and audit requirements?

12. Ethical Standards of Research
When finishing your research:
1. Will your research and its findings be reported accurately, honestly and within a
reasonable time frame?
2. Will all contributions to the research be acknowledged?
3. Are agreements relating to intellectual property, publication and authorship being complied
with?
4. Will research data be retained in a secure and accessible form and for the required duration?
5. Will your research comply with all legal, ethical and contractual requirements?

13. DATA MINING
GLENN TICMAN VILLANUEVA
BSED – ENGLISH III

14. Data Mining
Data mining is looking for hidden, valid, and potentially useful patterns in
huge data sets. Data Mining is all about discovering unsuspected/
previously unknown relationships amongst the data.
It is a multi-disciplinary skill that uses machine learning, statistics, A.I. and
database technology.
The insights derived via Data Mining can be used for marketing, fraud
detection, and scientific discovery, etc.
Data mining is also called as Knowledge discovery, Knowledge extraction,
data/pattern analysis, information harvesting, etc.

16. Data Mining Techniques
1.Classification:
This analysis is used to retrieve important and relevant information about data,
and metadata. This data mining method helps to classify data in different
classes.
2. Clustering:
Clustering analysis is a data mining technique to identify data that are like each
other. This process helps to understand the differences and similarities between
the data.

17. Data Mining Techniques
3. Regression:
Regression analysis is the data mining method of identifying and
analyzing the relationship between variables. It is used to identify
the likelihood of a specific variable, given the presence of other
variables.
4. Association Rules:
This data mining technique helps to find the association between
two or more Items. It discovers a hidden pattern in the data set.

18. Data Mining Techniques
5. Outer detection:
This type of data mining technique refers to observation of data
items in the dataset which do not match an expected pattern or
expected behavior. This technique can be used in a variety of
domains, such as intrusion, detection, fraud or fault detection, etc.
Outer detection is also called Outlier Analysis or Outlier mining.

19. Data Mining Techniques
6. Sequential Patterns:
This data mining technique helps to discover or identify similar
patterns or trends in transaction data for certain period.
7. Prediction:
Prediction has used a combination of the other data mining
techniques like trends, sequential patterns, clustering, classification,
etc. It analyzes past events or instances in a right sequence for
predicting a future event.

20. Data Mining Tools
R-language:
R language is an open source tool for statistical computing and graphics. R has a wide variety of
statistical, classical statistical tests, time-series analysis, classification and graphical techniques.
It offers effective data handing and storage facility.
Oracle Data Mining:
Oracle Data Mining popularly knowns as ODM is a module of the Oracle Advanced Analytics
Database. This Data mining tool allows data analysts to generate detailed insights and makes
predictions. It helps predict customer behavior, develops customer profiles, identifies cross-
selling opportunities.

21. Benefits of Data Mining
- Data mining technique helps companies to get knowledge-based information.
- Data mining helps organizations to make the profitable adjustments in operation and production.
- The data mining is a cost-effective and efficient solution compared to other statistical data
applications.
- Data mining helps with the decision-making process.
- Facilitates automated prediction of trends and behaviors as well as automated discovery of hidden
patterns.
- It can be implemented in new systems as well as existing platforms
- It is the speedy process which makes it easy for the users to analyze huge amount of data in less
time.

22. Disadvantages of Data Mining
- There are chances of companies may sell useful information of their customers to
other companies for money. For example, American Express has sold credit card
purchases of their customers to the other companies.
- Many data mining analytics software is difficult to operate and requires advance
training to work on.
- Different data mining tools work in different manners due to different algorithms
employed in their design. Therefore, the selection of correct data mining tool is a very
difficult task.
- The data mining techniques are not accurate, and so it can cause serious
consequences in certain conditions.

23. Summary:
• Data Mining is all about explaining the past and predicting the future for
analysis.
• Data mining helps to extract information from huge sets of data. It is the
procedure of mining knowledge from data.
• Data mining process includes business understanding, Data Understanding,
Data Preparation, Modelling, Evolution, Deployment.
• Important Data mining techniques are Classification, clustering, Regression,
Association rules, Outer detection, Sequential Patterns, and prediction

24. Summary:
• R-language and Oracle Data mining are prominent data mining tools.
• Data mining technique helps companies to get knowledge-based information.
•The main drawback of data mining is that many analytics software is difficult to
operate and requires advance training to work on.
• Data mining is used in diverse industries such as Communications, Insurance,
Education, Manufacturing, Banking, Retail, Service providers, eCommerce,
Supermarkets Bioinformatics.

25. MIXED METHOD
RESEARCH
GLENN TICMAN VILLANUEVA
BSED – ENGLISH III

26. Mixed Method Research
Mixed methods research is a methodology for conducting research
that involves collecting, analyzing and integrating quantitative (e.g.,
experiments, surveys) and qualitative (e.g., focus groups,
interviews) research.
This approach to research is used when this integration provides a
better understanding of the research problem than either of each
alone.

27. Quantitative Data
Quantitative data includes close-ended information such as
that found to measure attitudes (e.g., rating scales),
behaviours (e.g., observation checklists), and performance
instruments. The analysis of this type of data consists of
statistically analysing scores collected on instruments (e.g.,
questionnaires) or checklists to answer research questions
or to test hypotheses.

28. Qualitative Data
Qualitative data consists of open-ended information
that the researcher usually gathers through
interviews, focus groups and observations. The
analysis of the qualitative data (words, text or
behaviours) typically follows the path of aggregating
it into categories of information and presenting the
diversity of ideas gathered during data collection.

29. Advantages:
Provides strengths that offset the weaknesses of both
quantitative and qualitative research.
Provides a more complete and comprehensive understanding
of the research problem than either quantitative or qualitative
approaches alone.
Provides an approach for developing better, more context
specific instruments.
Helps to explain findings or how causal processes work.

30. Disadvantages and Limitations:
The research design can be very complex.
Takes much more time and resources to plan and
implement this type of research.
It may be difficult to plan and implement one method by
drawing on the findings of another.
It may be unclear how to resolve discrepancies that
arise in the interpretation of the findings.

31. Types of mixed methods research designs
1. Sequential explanatory design
2. Sequential exploratory design
3. Concurrent triangulation
4. Concurrent nested

32. Sequential Explanatory Design
This design involves the collection and analysis
of quantitative data followed by the collection
and analysis of qualitative data. The priority is
given to the quantitative data, and the findings
are integrated during the interpretation phase of
the study.

33. Sequential Explanatory Design
When to use it?
To help explain, interpret or contextualize quantitative
findings.
To examine in more detail unexpected results from a
quantitative study.

34. Sequential Explanatory Design
Strengths:
Easy to implement because the steps fall into clear separate stages.
The design is easy to describe and the results easy to report.
Weaknesses:
Requires a substantial length of time to complete all data collection
given the two separate phases.

35. Sequential Explanatory Design
Example:
The researcher collects data about people’s risk and benefit
perceptions of red meat using a survey and follows up with
interviews with a few individuals who participated in the
survey to learn in more detail about their survey responses
(e.g., to understand the thought process of people with low
risk perceptions).

36. Sequential Exploratory Design
In this design, qualitative data collection
and analysis is followed by quantitative
data collection and analysis. The priority is
given to the qualitative aspect of the study,
and the findings are integrated during the
interpretation phase of the study.

37. Sequential Exploratory Design
When to use it?
To explore a phenomenon and to expand on qualitative findings.
To test elements of an emergent theory resulting from the qualitative
research.
To generalize qualitative findings to different samples in order to
determine the distribution of a phenomenon within a chosen population.
To develop and test a new instrument

38. Sequential Exploratory Design
Strengths:
Easy to implement because the steps fall into clear, separate stages.
The design is easy to describe and the results easy to report.
Weaknesses:
Requires a substantial length of time to complete all data collection given the two
separate phases.
It may be difficult to build from the qualitative analysis to the subsequent data
collection.

39. Sequential Exploratory Design
Example:
The researcher explores people's beliefs and
knowledge regarding nutritional information by
starting with in-store interviews and then uses an
analysis of the information to develop a survey
instrument that is administered later to a sample
from a population.

40. Concurrent triangulation
In this design only one data collection phase is
used, during which quantitative and qualitative
data collection and analysis are conducted
separately yet concurrently. The findings are
integrated during the interpretation phase of the
study. Usually, equal priority is given to both types
of research.

41. Concurrent triangulation
When to use it?
To develop a more complete understanding
of a topic or phenomenon.
To cross-validate or corroborate findings.

42. Concurrent triangulation
Strengths:
Provides well-validated and substantiated findings.
Compared to sequential designs, data collection takes less time.
Weaknesses:
Requires great effort and expertise to adequately use two separate methods at the same time.
It can be difficult to compare the results of two analysis using data of different forms.
It may be unclear how to resolve discrepancies that arise while comparing the results.
Given that data collection is conducted concurrently, results of one method (e.g., interview) cannot be integrated
in the other method (e.g., survey).

43. Concurrent triangulation
Example:
The researcher uses a survey to assess people’s self-
reported food safety practices and also observes those
practices in their natural environment. By comparing the
two types of data, the researcher can see if there is a
match between what people think they are doing and
what they are actually doing in terms of food safety
practices.

44. Concurrent Nested
In this design only one data collection phase is used,
during which a predominant method (quantitative or
qualitative) nests or embeds the other less priority
method (qualitative or quantitative, respectively). This
nesting may mean that the embedded method addresses
a different question than the dominant method or seeks
information from different levels. The data collected from
the two methods are mixed during the analysis phase of
the project.

45. Concurrent Nested
When to use it?
To gain broader and in-depth perspectives on a topic.
To offset possible weaknesses inherent to the predominant method.
Strengths:
Two types of data are collected simultaneously, reducing time and resources (e.g.,
number of participants).
Provides a study with the advantages of both quantitative and qualitative data.

46. Concurrent Nested
When to use it?
To gain broader and in-depth perspectives
on a topic.
To offset possible weaknesses inherent to
the predominant method.

47. Concurrent Nested
Strengths:
Two types of data are collected simultaneously, reducing time and resources (e.g.,
number of participants).
Provides a study with the advantages of both quantitative and qualitative data.
Weaknesses:
The data needs to be transformed in some way so that both types of data can be
integrated during the analysis, which can be difficult.
Inequality between different methods may result in unequal evidence within the study,
which can be a disadvantage when interpreting the results.

48. Concurrent Nested
Example:
The researcher collects data to assess people’s
knowledge and risk perceptions about genetically
modified food by using a survey instrument that
mixes qualitative (open-ended) and quantitative
(closed-ended) questions, and both forms of data are
integrated and analysed.

49. IMPORTANT!
Once a mixed methods research design has been
selected, one has to decide which specific research
methods and instruments/measures should be
incorporated/mixed in the research program.