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Present
Relevant
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COMPETENCY BASED LEARNING MATERIAL
Sector:
AUTOMOTIVE AND LAND TRANSPORT(21ST
CENTURY)
Qualification:
DRIVING NCII
Unit of Competency:
PRESENT RELEVANT INFORMATION
Module Title:
PRESENTING RELEVANT INFORMATION
CC

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TABLE OF CONTENT
 Front Page 1
 Table of Contents 2
 How to use this Competency Based – Learning Material 3
 List Competencies 4-5
 Module Content 6
 Learning Outcome Summary 1 7
 Learning Outcome Summary 2 8
 Learning Outcome Summary 3 9
 Learning Experiences Learning Outcome 1 10
 Learning Experiences Learning Outcome 2 11
 Learning Experiences Learning Outcome 3 12
 Information Sheet 6.1.1 13-14
 Self – Check 6.1.1 15
 Answer key 6.1.1 16
 Information Sheet 6.1.1a 17-19
 Self – Check 6.1.1 a 20
 Answer key 6.1.1a 21
 Information Sheet 6.2.1 22-27
 Self – Check 6.2.1 28
 Answer key 6.2.1 29
 Information Sheet 6.3.1 30-32
 Self – Check 6.3.1 33
 Answer key 6.3-1 44

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HOW TO USE THIS COMPETENCY BASED LEARNING MATERIAL
Welcome to the module in Presenting Relevant Information. This
module contains training materials and activities for you to complete.
You are required to go through a series of learning activities in order to
complete each learning outcome of the module. In each learning outcome are
Information Sheets, Self-Checks, Operation Sheets and Job Sheet. Follow these
activities on your own. If you have questions, don’t hesitate to ask your trainer
for assistance.
The goal of this course is the development of practical skills. To gain these
skills, you must learn basic components and terminology. For the most part,
you’ll get this information from the Information Sheets.
This module was prepared to help you achieve the required competency,
in “Present Relevant Information”.
This will be the source of information for you to acquire knowledge and
skills in this particular competency independently and at your own pace, with
minimum supervision or help from your instructor.
Remember to:
Work through all the information and complete the activities in each
section.
Read information sheets and complete the self-check. Suggested
references are included to supplement the materials provided in this module.
Most probably your trainer will also be your supervisor or manager.
He/she is there to support you and show you the correct way to do things.
You will be given plenty of opportunity to ask questions and practice on
the job. Make sure you practice your new skills during regular work shifts. This
way you will improve both your speed and memory and also your confidence.
Use Self-Checks, Operation Sheets or Job Sheets at the end of each section
to test your own progress.
When you feel confident that you have had sufficient practice, ask your
trainer to evaluate you. The results of your assessment will be recorded in your
Progress Chart and Achievement Chart.

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List of Competencies
No. Unit of
Competency
Module Title Code
1 Participate in
workplace
communication
Providing housekeeping
services to guest
2 Work in a team
environment
Working in a team
environment
3 Solve/address
general workplace
problems
Solving/addressing
general workplace
problems
4 Develop career and life
decisions
Developing career
and life decisions
5 Contribute to
workplace innovation
Contributing to
workplace innovation
6 Present relevant
information
Presenting relevant
information
7 Practice occupational
safetyand health
policiesand
procedures
Practicing
occupational safety
and health policies
and procedures
8 Exercise efficient
and effective
sustainable
practices in the
workplace
Exercising efficientand
effective sustainable
practices in the
workplace
9 Practice
Entrepreneurial skills in
the workplace.

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MODULE CONTENT
UNIT OF COMPETENCY : Present Relevant Information
MODULE TITLE : Presenting Relevant Information
UNIT DESCRIPTOR : This unit of covers the knowledge, skills
and attitudes required to presentdata/information appropriately.
NOMINAL DURATION : 4 hours
LEARNING OUTCOMES:
At the end of this module the students/ trainees will be able to:
LO I: Gather data information
LO 2: Assess gathered data/information
LO 3: Record and present information
ASSESSMENT CRITERIA:
LO 1
Evidence, facts and information are collected
Evaluation, terms of reference and conditions are reviewed to determine
whether data/information falls within project scope
LO 2
Validity of data/ information is assessed
Analysis techniques are applied to assess data/ information.
Trends and anomalies are identified
Data analysis techniques and procedures are documented
Recommendation s are made on areas of possible improvement.
LO 3
Studied data/information are recorded.
Recommendation s are analyzed for action to ensure they are compatible
with the project’s scope and terms of reference.
Interim and final reports are analyzed and outcomes are compared to the
criteria established at the outset.
Findings are presented to stakeholders.

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LEARNING OUTCOME SUMMARY
LEARNING OUTCOME # 1 Gather data/information
CONTENTS:
 Confidentiality
 Accuracy
ASSESSMENT CRITERIA:
 Evidence, facts and information are collected
 Evaluation, terms of reference and conditions are
reviewed to determine whether data/information falls
within project scope
CONDITIONS:
Student/ trainee must be provided with the following:
1. WORKPLACE LOCATION
2. EQUIPMENT
3. TOOLS
4. MATERIALS
 Writing materials
 References
 Handouts
Assessment methods:
 Written Test
 Interview
 Portfolio

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LEARNING OUTCOME SUMMARY
LEARNING OUTCOME # 2
Assess gathered
data/information
CONTENTS:
 Data Analysis techniques/procedures
ASSESSMENT CRITERIA:
 Validity of data/ information is assessed
 Analysis techniques are applied to assess data/ information.
 Trends and anomalies are identified
 Data analysis techniques and procedures are documented
 Recommendation s are made on areas of possible
improvement.
CONDITIONS:
Student/ trainee must be provided with the following:
1. WORKPLACE LOCATION
2. EQUIPMENT
3. TOOLS
4. MATERIALS
 Writing materials
 References
 Handouts

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Assessment methods:
 Written Test
 Interview
 Portfolio

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LEARNING OUTCOME SUMMARY
LEARNING OUTCOME # 3
Record and present
information
CONTENTS:
 Reporting requirements to a range of audience
ASSESSMENT CRITERIA:
 Studied data/information are recorded.
 Recommendation s are analyzed for action to ensure
they arecompatible with the project’s scope and terms
of reference.
 Interim and final reports are analyzed and outcomes are
compared tothe criteria established at the outset.
 Findings are presented to stakeholders.
CONDITIONS:
Student/ trainee must be provided with the following:
1. WORKPLACE LOCATION
2. EQUIPMENT
3. TOOLS
4. MATERIALS
 Writing materials
 References
 Handouts

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Assessment methods:
 Written Test
 Interview
 Portfolio

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LEARNING EXPERIENCES
LO1 Gather data/information
Learning Activities Special Instructions
1. Read Information Sheet #
6.1.1
Confidentiality
After reading the information sheet
you are required to answer the
self-check # 6. 1.1.
2. Answer Self Check # 6.1.1 Compare answers with the answer
key to be given to you by your
facilitator. If you got a perfect
score, continue reading on the
next information sheet. If not,
review by reading Information sheet
# 6.1.1 and try to answer the self-
check again.
1. Read Information Sheet #
6.1.1a
Accuracy
After reading the information sheet
you are required to answer the
self-check # 6. 1.1a.
2. Answer Self Check # 6.1.1a Compare answers with the
answer keyto be given to you by
your facilitator. If you got a perfect
score, continue reading on the
next information sheet. If not,
review by reading Information
sheet # 6.1.1a and try to answer
the
self-check again.

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LEARNING EXPERIENCES
LO2 Assess gathered data/information
Learning Activities Special Instructions
1. Read Information Sheet #
6.2.1Data Analysis
techniques/procedures
After reading the information sheet
you are required to answer the
self-check # 6. 2.1.
2. Answer Self Check # 6.2.1 Compare answers with the answer
key to be given to you by your
facilitator. If you got a perfect
score, continue reading on the
next information sheet. If not,
review by reading Information sheet
# 6.2.1 and try to answer the self-
check again.

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LEARNING EXPERIENCES
LO3 Record and present information
Learning Activities Special Instructions
1. Read Information Sheet #
6.3.1Reporting requirements
to a range of audiences
After reading the information sheet
you are required to answer the
self-check # 6. 3.1.
2. Answer Self Check # 6.3.1 Compare answers with the answer
key to be given to you by your
facilitator. If you got a perfect
score, continue reading on the
next information sheet. If not,
review by reading Information sheet
# 6.3.1 and try to answer the self-
check again.

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INFORMATION SHEET 6.1.1
Confidentiality
Learning Objective:
After reading this information sheet, you must be able to
understand Confidentiality in gathering data.
Introduction:
Confidentiality refers to the researcher's agreement to handle,
store, and share research data to ensure that information obtained
from and about research participants is not improperly divulged.
Individuals may only be willing to share information for research
purposes with an understanding that the information will remain
protected from disclosure outside of the research setting or to
unauthorized persons.
Data confidentiality is about protecting data against
unintentional, unlawful, or unauthorized access, disclosure, or
theft.
Confidentiality has to do with the privacy of information, including
authorizations to view, share, and use it. Information with low
confidentiality concerns may be considered "public" or otherwise not
threatening if exposed beyond its intended audience. Information with
high confidentiality concerns is considered secret and must be kept
confidential to prevent identity theft, compromise of accounts and
systems, legal or reputational damage, and othersevere consequences.
When managing data confidentiality, follow these guidelines:
 Encrypt sensitive files.
Encryption is a process that renders data unreadable to
anyone except those who have the appropriate password or key. By
encrypting sensitive files (by using file passwords, for example), you
can protect them from being read orused by those who are not
entitled to do either.

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 Manage data access.
Controlling confidentiality is, in large part, about
controlling who has access to data. Ensuring that access is only
authorized and granted to those who have a "need to know" goes
a long way in limiting unnecessary exposure. Users should also
authenticate their access with strong passwords and, where
practical, two-factor authentication. Periodically
review access lists and promptly revoke access when it is no longer
necessary.
 Physically secure devices and paper documents.
 Controlling access to data includes controlling access of all
kinds, both digital and physical. Protect devices and paper
documents from misuse ortheft by storing them in locked areas.
Never leave devices or sensitive documents unattented in public
locations.
 Securely dispose of data, devices, and paper records.
When data is no longer necessary for University-related purposes,
it mustbe disposed of appropriately.
 Sensitive data, such as Social Security numbers, must
be securelyerased to ensure that it cannot be recovered
and misused.
 Devices that were used for University-related purposes or
that were otherwise used to store sensitive information
should be destroyed orsecurely erased to ensure that their
previous contents cannot be recovered and misused.
 Paper documents containing sensitive information should be
shreddedrather than dumped into trash or recycling bins.
 Manage data acquisition.
When collecting sensitive data, be conscious of how much data is
actuallyneeded and carefully consider privacy and confidentiality in
the acquisition process. Avoid acquiring sensitive data unless
absolutely necessary; one of the best ways to reduce confidentiality
risk is to reduce theamount of sensitive data being collected in the
first place.

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 Manage data utilization.
Confidentiality risk can be further reduced by using sensitive data
only as approved and as necessary. Misusing sensitive data violates
the privacy andconfidentiality of that data and of the individuals or
groups the data represents.
 Manage devices.
Computer management is a broad topic that includes many
essential securitypractices. By protecting devices, you can also
protect the data they contain.
Follow basic cybersecurity hygiene by using anti-virus
software,routinely patching software, whitelisting
applications, using
device passcodes, suspending inactive sessions, enabling
firewalls, andusing whole-disk encryption.

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Self-check 6.1.1
Direction: Answer the following questions. Write your answer on a
separatesheet.
What are the guidelines to follow when managing data confidentiality?
1.
2.
3.
4.
5.
6.

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Answer key 6.1.1
1. Encrypt sensitive files
2. Manage data access
3. Physically secure devices and paper documents
4. Securely dispose of data, devices, and paper records
5. Manage data acquisition.
6. Manage data utilization
7. Manage devices.

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INFORMATION SHEET 6.1.1a
Accuracy
Learning Objective:
After reading this information sheet, you must be able to
understand how important accuracy is in data analysis
Introduction:
Accuracy is to be ensuring that the information is correct and
without any mistake. Information accuracy is important because may
the life of people depend in it like the medical information at the
hospitals, so the information must be accurate.
The quality of information measured by accuracy, timeliness,
completeness, relevance and if it is easy to understood by the users,
so the accuracy important for quality of information. And the
accuracy represents all organization actions. To get accurate
information we need the right value.
If someone gave inaccurate information, it is difficult to find who
made the mistake. There are many reasons for inaccurate information.
The most common case is when the user enters wrong value. Also
inaccurate information may accrue by typographical mistake. To avoid
this mistakes, the organization must find who has experience and skills
for data entry and it must use the programs which discover the
typographical mistake.
Inaccurate information with the passage of time it be hard to
avoided if no one update it(like the address, phone numbers)and the
information going to be more inaccurate.
Data analysis is only as good as the quality of data obtained
during the data collection process. How can you ensure data
accuracy and integrity? Here are three pointers.

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Data analysis is a very important part of the research process.
Before performing data analysis, researchers must make sure that
numbers in their data are as accurate as possible. Clicking the
menus and buttons of statistical software applications like SPSS,
Microstat, Statistica, Statview among others is easy, but if the data
used in such automated data analysis is faulty, the results are
nothing more than just plain rubbish. Garbage in, garbage out
(GIGO).
For many students who just want to comply with their thesis
requirement, rigorous and critical data analysis are almost always given
much less attention than the other parts of the thesis. At other times, data
accuracy is deliberately compromised because of the apparent inconsistency
of findings with expectedresults.
Data should be as accurate, truthful or reliable as possible for if
there are doubts about their collection, data analysis is compromised.
Interpretation of results will be faulty that will lead to wrong
conclusions.
How can you make sure that your data is ready or suitable for data
analysis?
Here are three pointers to remember to ensure data integrity and
accuracy. Thefollowing points focus on data collection during interviews.
3 Points to Remember to: Ensure Data Integrity and Accuracy
1. Review data entries
Be meticulous about overlooked items in data collection. When
dealing with numbers, ensure that the results are within sensible limits.
Omitting a zero hereor adding a number there can compromise the
accuracy of your data.
Watch out for outliers, or those data that seems to be out-of-bounds or
at the extremes of the scale of measurement. Verify if the outlier is truly an
originalrecord of data collected during the interview. Outliers may be just
typographical errors.

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2. Verify the manner of data collection
Cross-examine the data collector. If you asked somebody to gather
data for you, throw him some questions to find out if the data was
collected systematically or truthfully. For paid enumerators, there
is a tendency to administer questionnaires in a hurry. In the
process, many things will be missed and they will just have to fill-out
missing items. To filter out this possibility, the information gathered
should be cross-checked.
Interview
The following questions may be asked to ensure data quality.

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How much time did you spend in interviewing the respondent of the
study?
Is the respondent alone or with a group of people when you did the
interview?
To reduce cheating in doing the interview, it will help if you tell your
enumerators to have the interviewees sign the interview schedule right after
they were interviewed. Ask the enumerators to write the duration of the
interview, taking note of the start and end time of the interview.
1. Avoid biased results
Watch out for the so-called ‘wildfire effect’ in data gathering. This happens
when you are dealing with sensitive issues like fisherfolk’s compliance to
ordinances, rules and regulations or laws of the land. Rumors on the
issues raised by the interviewer during the interview will prevent other
people from answering the questionnaire. Respondents may become
apprehensive if answers to questions intrude into their privacy or threaten
them in some way.
Thus, questionnaire administration must be done simultaneously
within, say, a day in a given group of interviewees in a particular place.
If some of the respondents were interviewed the next day, chances are
they have already gossiped among themselves and become wary of
someone asking them about sensitive issues that may incriminate
them.
Wildfire effect is analogous to a small spark of a match that can ignite
dry grass leaves and cause an uncontrollable forest fire. This is the
power of the tongue. Hence, the term wildfire effect.
There are many other sources of bias that impact negatively on data
quality. These are described in greater detail in another post titled How
to Reduce Researcher Bias in Social Research.
Data analysis may then be employed once data accuracy and integrity
are ensured.

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Self – check 6.1.1a
Directions: Answer the following questions. Write your answer on a
separatesheet.
1. What is accuracy?
2-4 What are the 3 Points to Remember to Ensure Data Integrity and
Accuracy?

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Answer key 6.1.1a
1. Accuracy is to be ensuring that the information is correct and
without anymistake.
2. Review data entries
3. Verify the manner of data collection
4. Avoid biased results

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INFORMATION SHEET 6.2.1
Data Analysis
techniques/procedures Learning Objective:
After reading this information sheet, you must be able to
understand techniques and procedures in Data Analysis
Introduction:
In our data-rich age, understanding how to analyze and extract
true meaning from the digital insights available is one of the primary
drivers of success.
What is Data Analysis?
Data analysis is a process that relies on methods and techniques
to taking raw data, mining for insights that are relevant to the
business’s primary goals, and drilling down into this information to
transform metrics, facts, and figuresinto initiatives for improvement.
Types of Data Analysis
Domain Analysis
In software engineering, domain analysis, or product line analysis, is the
process of analyzing related software systems in a domain to find their common

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and variable parts. It is a model of wider business context for the system. The
term was coined in the early 1980s by James Neighbors. Domain

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analysis is the first phase of domain engineering. It is a key method for
realizingsystematic software reuse.
Domain analysis produces domain models using methodologies such
as domain specific languages, feature tables, facet tables, facet
templates, and generic architectures, which describe all of the systems in
a domain. Severalmethodologies for domain analysis have been
proposed.

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The products, or "artifacts", of a domain analysis are sometimes
object- oriented models (e.g. represented with the Unified Modeling
Language (UML)) or data models represented with entity-relationship
diagrams (ERD). Software developers can use these models as a basis
for the implementation of software architectures and applications. This
approach to domain analysis is sometimes called model-driven
engineering.
In information science, the term "domain analysis" was
suggested in 1995by Birger Hjørland and H. Albrechtsen.
Several domain analysis techniques have been identified, proposed
and developed due to the diversity of goals, domains, and involved
processes.
 DARE: Domain Analysis and Reuse Environment
 Feature-Oriented Domain Analysis (FODA)
 IDEF0 for Domain Analysis
 Model Oriented Domain Analysis and Engineering
To perform domain analysis, you gather information from whatever
sources of information are available: these include the domain
experts; any books about the domain; any existing software and its
documentation, and any other documents he or she can find. The
interviewing, brainstorming and use case analysis techniques
discussed later in this chapter can help with domain analysis. Object
oriented modelling, discussed in the next chapter, can also be of
assistance.
As a software engineer, you are not expected to become an expert in
the domain; nevertheless, domain analysis can involve considerable
work.
The following benefits will make this work worthwhile:

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• Faster development: You will be able to communicate with the
stakeholders more effectively, hence you will be able to establish
requirements more rapidly. Having performed domain analysis will
help you to focus on the most important issues.
• Better system: Knowing the subtleties of the domain will help
ensure that the solutions you adopt will more effectively solve the
customer’s problem. You will make fewer mistakes, and will know
which procedures and standards to follow. The analysis will give you
a global picture of the domain of application; this will lead to better
abstractions and hence improved designs.
• Anticipation of extensions: Armed with domain knowledge, you will
obtain insights into emerging trends and you will notice opportunities
for future development. This will allow you to build a more adaptable
system.
It is useful to write a summary of the information found during domain
analysis. The process of organizing and writing this summary can help
you gain a better grasp of the knowledge; the resulting document can
help educate other software engineers who join the team later.
We suggest that a domain analysis document should be divided into
sections such as the following:
A.Introduction: Name the domain, and give the motivation for
performing the analysis. The motivation normally is that you are
preparing to solve a particular problem by development or extension
of a software system.
B.Glossary: Describe the meanings of all terms used in the domain that
are either not part of everyday language or else have special
meanings. You must master this terminology if you want to be able
to communicate with your customers and users. The terminology
will appear in the user interface of the software as well as in the
documentation. You may be able to refer to an existing glossary in
some other document, rather than writing a new glossary. The
section is best placed at the start of the domain analysis document
so you can subsequently can use the defined terms.

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C. General knowledge about the domain: Summarize important facts
or rules that are widely known by the domain experts and which
would normally be learned as part of their education. Such
knowledge includes scientific principles, business processes,
analysis techniques, and how any technology works. This is an
excellent place to use diagrams; however, where possible point the
reader for details to any readily accessible books or other
documents. This general knowledge will help you acquire an
understanding of the data you may have to process and
computations you may have to perform.
D.Customers and users: Describe who will or might buy the software,
and in what industrial sectors they operate. Also, describe the other
people who work in the domain, even peripherally. Mention their
background and attitude as well as how they fit into the organization
chart, and relate to each other. These people may become users.
E. The environment: Describe the equipment and systems used. The
new system or extensions will have to work in the context of this
environment.
F. Tasks and procedures currently performed: Make a list of what
the various people do as they go about their work. It is important to
understand both the procedures people are supposed to follow as well
as the shortcuts they tend to take. For example, if people are supposed
to enter certain information on a form, but rarely do, this suggests the
information is not useful. Tasks listed in this section may be
candidates for automation.
G. Competing software: Describe what software is available to assist
the usersand customers, including software that is already in use,
and software on the market. Discuss its advantages and disadvantages.
This information suggests ideas for requirements, and highlights mistakes to
avoid.
H.Similarities across domains and organizations: Understanding what
is generic versus what is specific will help you to create software that
might be more reusable or more widely marketable. Therefore,
determine what distinguishes this domain and the customer’s
organization from others, as well as what they have in common.

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Content Analysis
Content analysis is a research tool used to determine the presence of
certain words, themes, or concepts within some given qualitative data
(i.e. text).Using content analysis, researchers can quantify and analyze
the presence, meanings and relationships of such certain words,
themes, or concepts.
Uses of Content Analysis
Identify the intentions, focus or communication trends of an
individual,group or institution
Describe attitudinal and behavioral responses to communications
Determine psychological or emotional state of persons or groups
Reveal international differences in communication content
Reveal patterns in communication content
Pre-test and improve an intervention or survey prior to launch
Analyze focus group interviews and open-ended questions to complement
quantitative data
Types of Content Analysis
There are two general types of content analysis: conceptual analysis
and relational analysis. Conceptual analysis determines the existence
and frequency of concepts in a text. Relational analysis develops the
conceptual analysis further by examining the relationships among
concepts in a text. Eachtype of analysis may lead to different results,
conclusions, interpretations andmeanings.

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Conceptual Analysis
Typically people think of conceptual analysis when they
think of content analysis. In conceptual analysis, a concept is chosen
for examination and the analysis involves quantifying and counting
its presence. The main goal is to examine the occurrence of selected
terms in the data. Terms may be explicit or implicit. Explicit terms
are easy to identify. Coding of implicit terms is more complicated: you
need to decide the level of implication and base judgments on
subjectivity (issue for reliability and validity). Therefore, coding of
implicit terms involves using a dictionary or contextual translation
rules or both.
To begin a conceptual content analysis, first identify the
research question and choose a sample or samples for analysis.
Next, the text must becoded into manageable content categories.
This is basically a process of selective reduction. By reducing the
text to categories, the researcher can focus on and code for specific
words or patterns that inform the research question.
Relational Analysis
Relational analysis begins like conceptual analysis, where a
concept ischosen for examination. However, the analysis involves
exploring the relationships between concepts. Individual concepts are
viewed as having noinherent meaning and rather the meaning is a
product of the relationships among concepts.
To begin a relational content analysis, first identify a research
questionand choose a sample or samples for analysis. The research
question must be focused so the concept types are not open to
interpretation and can be summarized. Next, select text for analysis.
Select text for analysis carefully bybalancing having enough
information for a thorough analysis so results are notlimited with
having information that is too extensive so that the coding process
becomes too arduous and heavy to supply meaningful and

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worthwhile results.

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Comparison/Comparative Technique
According to Pickvance (2005), comparative analysis is
conducted mainly to explain and gain a better understanding of the
causal processes involved in the creation of an event, feature or
relationship usually by bringing together variations in the
explanatory variable or variables. Comparative research can betraced
to a long history that has gained much attention in current research
due to globalization, technological advances, etc. on cross-
national platforms (Azarian, 2011). Conventionally, comparative
analysis emphasized on the “explanation of differences, and the
explanation of similarities” (p.2). This helps to establish relationships
between two or more phenomena and provide valid reasons.
Comparisons are now carried out on various levels being regional,
national or wider geographical boundaries based on specific subject
or area of interest.
Types of Comparative Research
There are several methods of doing comparative analysis and Tilly
(1984) distinguishes four types of comparative analysis namely:
individualizing, universalizing, variation-finding and encompassing
(p.82). Adding to the types of comparative analysis, May (1993, as cited
in Azarian 2011, p. 117) offers a four- folded typology, including the
import-mirror view, the difference view, the theory- development view
and, finally, the prediction view. These types are similar to that
suggested by Tilly (1984) which are elaborated below.

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a. Individualizing comparison contrasts ‘a small number of cases in order
to grasp the peculiarities of each case’ (1984, p. 82). This basically involves
describing fully the characteristics or features each of the cases being
studied. This helps to broaden our knowledge and gives insight to see
cases in-depth. This method cannot be said to be truly comparative but
makes use of comparison in a small aspect of the research (Fredrickson,
1997).
b. Universalizing comparison ‘aims to establish that every instance of a
phenomenon follows essentially the same rule’ (1984, p. 82). This involves
the use of comparison to develop fundamental theories with significant
generality and relevance; goes to further to provide theories which explain
the cases being studied. E.g. development of theories of industrialism,
social revolutions etc.
c. Variation-finding comparison seeks to ‘establish a principle of variation
in the character or intensity of a phenomenon by examining systematic
differences between instances’ (1984, p. 82). That is, comparing numerous
forms of a single phenomenon to discover logical differences among
instances and establish a standard of variation in the character or
intensity of that phenomenon. E.g. Green (1997) study of the modern
Jewish Diaspora and Moore (1966) study on Social Origins of
Dictatorshipand Democracy.
d. Encompassing comparison ‘places different instances at various
locations within the same system, on the way to explaining their
characteristics as a function of their varying relationships to the system
as a whole’ (1984, p. 83). E.g. explaining the difference between two
children’s behavior by their orders of birth, attributing the
characteristicsof rural communities to their varying connections with a
nearby city or urban area.

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Self – Check 6.2.1
Direction: Answer the following questions. Write your answer on a separate
sheet.
1-3 What are the examples of Data Analysis
4-5 Types of Content Analysis
6-9 Types of Comparative Research10 – What is Data Analysis?
10 – What is Data Analysis?

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Answer Key 6.2.1
1. Domain Analysis
2. Content Analysis
3. Comparison/Comparative Technique
4. Conceptual Analysis
5. Rational Analysis
6. Individualizing comparison
7. Universalizing comparison
8. Variation-finding comparison
9. Encompassing comparison
10. Data analysis is a process that relies on methods and
techniques to taking raw data, mining for insights that are relevant
to the business’sprimary goals, and drilling down into this
information to transform metrics, facts, and figures into initiatives
for improvement.

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INFORMATION SHEET 6.3.1
Reporting requirements to a range of audiences
Learning Objective:
After reading this information sheet, you must be able to
understand important is reporting to a range of audiences in data
analysis
Introduction:
At the start of the writing or communication process, carefully
consider your “audience”—the person or persons who actually be
reading your document, receiving your email, or watching your
presentation. As a communicator, your primary goal is to transmit
information efficiently and effectively. How you choose to transmit
that information—the way it’s written and the form it takes— must
depend to a large extent on who your audience is and what their goals
are.
1. As a communicator, your primary goal is to transmit information
efficientlyand effectively.
Good communication is the result of a complex process that
factors in your reader and their goals. Think of it as an act of
translation—you possess information and knowledge, and you
need to deliver that information to your audience in a way they
will understand.

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This process asks you to consider the language you use, but
also the wayyou deliver the information (a document, a presentation,
a diagram, a phone call). The same information might be
communicated in multiple ways, depending on your audience and
the context.
2. Audience and Purpose Affect Your Choices as a Communicator
Even in a technical communication style, there are multiple ways
to presentthe same information based on audience and
purpose.
Be prepared to communicate to many different types of audiences—
youraudience must affect your decisions as a communicator. Factors
that define audiences are:
 their level of expertise or familiarity with the subject (e.g., a fellow
engineer or aperson in a non-technical position in the company or
a layperson),
 their role or goals (what they will do with the information),
 their position relative to the organization (internal or external), and
 their position relative to you (peer, superior, or subordinate).
A clearly stated purpose is a key feature in most technical
and professional communications because it improves
efficiency—your readershould know right away why they are
reading your document. Some common purposes for technical
communication are

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 to inform
 to recommend
 to request
 to propose
 to persuade
 to record
 to instruct
Most Important Things to Remember About Report Writing
1. Follow the report writing outline in your manual.
Feel free to besomewhat flexible with the order, but don’t
leave out whole sections.
2. Make your own internal outline including who is
responsible for which sections. Be sure that you leave time for
stakeholders to help you with editing/making revisions.
3. Be economical in your decisions about what to include in
your report. Shorter is better.
4. Avoid excessive use of jargon.

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3. Read your work – if you can’t understand it, chances are others
won’t be able to either. Think, in simple terms, about what you
are trying to say, and then write that. Use complete sentences
and standard English grammar conventions. You can rely some
on bullets and be limited in your transitions,but be sure your
reader can follow your logic.
4. Formatting is your friend. Use headers and sections to help your
reader know what is happening in your report. Be consistent about
where and how they appear (centered, bold, underlined, side
headings etc.). NUMBER THE PAGES. IF YOU’RE GENERATING A
DRAFT THINK ABOUT DOUBLE-SPACING.
5. Use tables and Graphs to help illustrate findings. ALL
TABLES AND GRAPHS MUST HAVE TITLES, LABELS AND
LEGENDS OR FOOTNOTES SO THAT THEY STAND ALONE.
6. Use quotes and vignettes or snippets from field notes to
illustrate yourfindings. Remember quotes should have quote marks
around them and be attributed to the speaker or writer. If you are
presenting field notes, be sure they are clearly identified and in
context.
7. Be consistent in your use of language, capitalization,
punctuation etc. For the most part, evaluation reports should be
written in the past tense – onlyreport what you actually did and
what you found. The action steps or Issues for Further
Consideration sections can include references to future actions.

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8. Do not introduce totally new topics into your report in the final
sections. Do not use the report to explain why you changed your
design, whatyou didn’t do, and what should be happening with a
program regardless of the findings presented in the report.

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Self – check 6.3.1
Directions: Answer the following. Write your answer on a separate
sheet.
A. What are the Factors that define audiences?
1.
2.
3.
4.
B. What are the common purposes for technical communication?
1.
2.
3.
4.
5.
6.
7.

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Answer key 6.3.1
A. What are the Factors that define audiences?
1. their level of expertise or familiarity with the subject (e.g., a
fellow engineer or a person in a non-technical position in
the company or alayperson),
2. their role or goals (what they will do with the information),
3. their position relative to the organization (internal or external),
and
4. their position relative to you (peer, superior, or subordinate).
B. What are the common purposes for technical communication?
1. to inform
2. to recommend
3. to request
4. to propose
5. to persuade
6. to record
7. to instruct

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References
1. https://www.betterevaluation.org/en/resources/guides/collect_retri
eve_dat a/collection_methods
2. http://www.omgcenter.org/pew-
fund/downloads/pcbp- ReportWritingTips.pdf
3. https://ohiostate.pressbooks.pub/feptechcomm/chapter/2-
audience/
4. https://support.google.com/analytics/answer/7162572?hl=en
5. https://simplyeducate.me/2012/12/06/the-importance-of-data-
accuracy- and-integrity-for-data-
analysis/#:~:text=Data%20analysis%20is%20only%20as,during%20
the%20
data%20collection%20process.&text=Data%20should%20be%20as%
20accu rate,will%20lead%20to%20wrong%20conclusions.
6. https://www.unr.edu/research-integrity/human-research/human-
research-protection-policy-manual/410-maintaining-data-
confidentiality#:~:text=Confidentiality%20refers%20to%20the%20resear
cher 's,participants%20is%20not%20improperly%20divulged.
7. https://www1.udel.edu/security/data/confidentiality.html

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