The document discusses various aspects of qualitative and quantitative data analysis methods. It provides details on 28 different steps, methods, and concepts for analyzing qualitative data, including developing codes, identifying themes, and summarizing data. It also outlines 4 steps for preparing quantitative data and describes common descriptive and inferential statistical analysis methods used. The document emphasizes intellectual honesty in research and analyzing data with suitable techniques.

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How Many Germs Are
on the Human Hand?
According to a study done at the
University of Colorado and
posted on Bacteriality, there are
nearly 332,000 genetically
distinct bacteria on the human
hand.

6. Chapter V
Learners Output:
1. Interpretation of Data
2. Data analysis method
3. Conceptualized Framework
for qualitative research
Finding the Answers to the Research
Questions.

7.  gathers and analyzes data
with intellectual honesty using
suitable techniques
Data Analysis Method
Objective: At the end of this lesson,
learners shall be able to…
[Duration: 1.5 weeks or 6 Hours]
Lesson 1.1

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I. Brainstorming
for Research
Topics

9. 9
Learners OUTPUT 1
Class
Research
Agenda

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II. identifying
the Problem and
Asking the
Question

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Learners OUTPUT 2
1. Background of the problem
2. Conceptual Framework
3. Research Hypothesis (for quantitative
research)
4. Statement of the problem
5. Definition of terms
6. Importance of the study
7. Scope and limitations of the study

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III. Reading
on Related
Studies

13. 13
Learners OUTPUT 3
 List of Related
Literature
Reviewed

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IV. Understanding
Ways to
Collect Data

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Learners OUTPUT 4
1. Research design
2. Population
3. Sampling method
4. Data collection
procedure

16. V. Finding the
Answers to the
Research
Questions.

17. Learners Output:
1. Interpretation of Data
2. Data analysis method
3. Conceptualized
Framework for qualitative
research
Finding the Answers to the Research Questions.

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Data Analysis Methods
 Analyzing Qualitative Data
Qualitative data analysis works a little
differently from quantitative data,
primarily because qualitative data is
made up of words, observations, images,
and even symbols. Deriving absolute
meaning from such data is nearly
impossible; hence, it is mostly used for
exploratory research.

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 Analyzing Qualitative Data
While in quantitative research
there is a clear distinction between
the data preparation and data
analysis stage, analysis for
qualitative research often begins
as soon as the data is available.

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 Data Preparation and Basic
Data Analysis
1. Getting familiar with
the data.
2. Revisiting research
objectives.

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3. Developing a
framework.
4. Identifying
patterns and
connections.

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 Qualitative Data Analysis Methods
1. Content analysis.
2. Narrative analysis.
3. Framework analysis.
4. Discourse analysis.
5. Grounded theory.

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1. Content analysis.
This is one of the most common
methods to analyze qualitative data. It
is used to analyze documented
information in the form of texts,
media, or even physical items. When to
use this method depends on the
research questions. Content analysis is
usually used to analyze responses from
interviewees.

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This method is used to analyze
content from various sources, such
as interviews of respondents,
observations from the field, or
surveys. It focuses on using the
stories and experiences shared by
people to answer the research
questions.
2. Narrative analysis.

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This is more advanced
method that consists of
several stages such as
familiarization, identifying a
thematic framework,
coding, charting, mapping
and interpretation.
3. Framework analysis.

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Like narrative analysis, discourse analysis is
used to analyze interactions with people.
However, it focuses on analyzing the social
context in which the communication
between the researcher and the
respondent occurred. Discourse analysis
also looks at the respondent’s day-today
environment and uses that information
during analysis.
4. Discourse analysis.

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This refers to using qualitative data to
explain why a certain phenomenon
happened. It does this by studying a
variety of similar cases in different settings
and using the data to derive causal
explanations. Researchers may alter the
explanations or create new ones as they
study more cases until they arrive at an
explanation that fits all cases.
5. Grounded theory.

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 Qualitative data analysis can also be
conducted through the following three
steps:
Step 1: Developing and Applying
Codes.
Step 2: Identifying themes, patterns
and relationships.
Step 3: Summarizing the data.

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Step 1: Developing and Applying Codes.
Coding can be explained as
categorization of data. A ‘code’ can be
a word or a short phrase that
represents a theme or an idea. All
codes need to be assigned meaningful
titles. A wide range of non-quantifiable
elements such as events, behaviors,
activities, meanings etc. can be coded.

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Developing and Applying Codes.

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Developing and Applying Codes.

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Unlike quantitative methods, in qualitative
data analysis there are no universally
applicable techniques that can be applied
to generate findings. Analytical and critical
thinking skills of researcher plays
significant role in data analysis in
qualitative studies. Therefore, no
qualitative study can be repeated to
generate the same results.
Step 2: Identifying themes, patterns
and relationships.

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Identifying themes, patterns and relationships.
Word and phrase repetitions – scanning
primary data for words and phrases
most commonly used by respondents, as
well as, words and phrases used with
unusual emotions;
Primary and secondary data
comparisons – comparing the findings of
interview/focus group/observation/any
other qualitative data collection method
with the findings of literature review and
discussing differences between them;

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Identifying themes, patterns and relationships.
Search for missing information –
discussions about which aspects of
the issue was not mentioned by
respondents, although you expected
them to be mentioned;
Metaphors and analogues –
comparing primary research findings
to phenomena from a different area
and discussing similarities and
differences.

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At this last stage you need to link
research findings to hypotheses or
research aim and objectives. When
writing data analysis chapter, you can
use noteworthy quotations from the
transcript in order to highlight major
themes within findings and possible
contradictions.
Step 3: Summarizing the data.

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 Analyzing Quantitative Data
Data Preparation
The first stage of analyzing
data is data preparation,
where the aim is to convert
raw data into something
meaningful and readable. It
includes four steps.

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Step 1: Data Validation
The purpose of data validation is to find out, as far as
possible, whether the data collection was done as per
the pre-set standards and without any bias. It is a four
step process, which includes…
1. Fraud
2. Screening
3. Procedure
4. Completeness

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Step 2: Data Editing
For example, an error could
be fields that were left
empty by respondents.
While editing the data, it is
important to make sure to
remove or fill all the empty
fields.

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Step 3: Data Coding
It refers to grouping and assigning values to
responses from the survey.
For example, if a researcher has
interviewed 1,000 people and now
wants to find the average age of
the respondents, the researcher
will create age buckets and
categorize the age of each of the
respondent as per these codes.

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For example,
respondents between
13-15 years old would
have their age coded as
0, 16-18 as 1,18-20 as 2,
etc.

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 Quantitative Data Analysis Methods
After these steps, the data is ready for
analysis. The two most commonly used
quantitative data analysis methods are
descriptive statistics and inferential
statistics.
Descriptive Statistics
Typically descriptive statistics (also known
as descriptive analysis) is the first
level of analysis. It helps researchers
summarize the data and find patterns.

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 Mean: numerical average
of a set of values.
 Median: midpoint of a set
of numerical values.
 Mode: most common
value among a set of
values.

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 Percentage: used to express
how a value or group of
respondents within the data
relates to a larger group of
respondents.
 Frequency: the number of
times a value is found.
 Range: the highest and lowest
value in a set of values.

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 Intellectual Honesty in Research
Intellectual Honesty is an applied
method of problem solving,
characterized by an
unbiased, honest attitude, which
can be demonstrated in a
number of different ways
including:

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 Intellectual Honesty in Research
Ensuring support for chosen
ideologies does not interfere with
the pursuit of truth;
 Relevant facts and information
are not purposefully omitted
even when such things may
contradict one's hypothesis;

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 Intellectual Honesty in Research
Facts are presented in an
unbiased manner, and not twisted
to give misleading impressions or
to support one view over
another;
 References, or earlier work, are
acknowledged where possible,
and plagiarism is avoided.

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 Ten Signs of Intellectual Honesty
1: Do not overstate the
power of your argument
2: Show willingness to
publicly acknowledge that
reasonable alternative
viewpoints exist.

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 Ten Signs of Intellectual Honesty
3: Be willing to
publicly acknowledge
and question one’s
own assumptions and
biases.

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 Ten Signs of Intellectual Honesty
4: Be willing to
publicly
acknowledge
where your
argument is weak.

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 Ten Signs of Intellectual Honesty
5: Be willing to
publicly
acknowledge when
you are wrong.

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 Ten Signs of Intellectual Honesty
6: Demonstrate
consistency.

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 Ten Signs of Intellectual Honesty
7: Address the
argument instead of
attacking the person
making the
argument.

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 Ten Signs of Intellectual Honesty
8: When addressing
an argument, do
not misrepresent
it.

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 Ten Signs of Intellectual Honesty
9: Show a
commitment to
critical thinking.

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 Ten Signs of Intellectual Honesty
10: Be willing to
publicly acknowledge
when a point or
criticism is good.

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1.

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The Venn Give the differences
and similarities of Qualitative
and Quantitative data analysis
using Venn diagram. Do this on
a separate sheet of paper.

59. The Venn Give the differences and
similarities of Qualitative and
Quantitative data analysis using Venn
diagram. Do this on a separate sheet
of paper.

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What is the
significance of data
analysis in research
writing?

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What is the
significance of
data analysis in
research
writing?

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Task 2: Analyzing Data
Analyze and evaluate the result of the
conducted survey of the researchers. From
51 Grades 11 and 12 informants’ various
responses, the problem is being answered
which aims to enumerate the teachers’
attitudes that are perceived by the students.
Based on the data gathered there are
favorable and unfavorable attitudes that the
informants perceived as they connected with
their teachers. Use a separate sheet of paper
in answering the activity.

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Timeline: To be submitted NEXT Session.

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 Data analysis is perhaps the most
important component of research.
Weak analysis produces inaccurate
results that not only hamper the
authenticity of the research but also
make the findings unusable. It’s
imperative to choose your data
analysis methods carefully to ensure
that your findings are insightful and
actionable.

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Exercise moral virtue, find the
facts, increase respect, seek
insights, and search for common
ground whenever you share
ideas with others. Because false
beliefs are often harmful, we
have moral obligation to seek
true beliefs. Challenge
dishonesty in yourself and other

67. End of Lesson 1
THANK YOU for your
60 minutes Dear
Students!
67
If you don’t fight for what you
want, don’t cry for what you
lost…

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Finding the Answers to the
Research Questions
(Interpretation and Presentation
of Results)
Module 1- Lesson 2

69. End of Lesson
69
Keep SAFE and ALWAYS WEAR
you Personal Protective
Equipment [PPE] …