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is defined as the value of data
in the form of counts or numbers
where each data-set has a unique
numerical value associated with it.
● This data is any quantifiable information
that can be used for mathematical
calculations and statistical analysis, such
that real-life decisions can be made based on
these mathematical derivations.
What is
Quantitative Data?
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■ Counter- Count equated with entities.
For example, the number of people
who download a particular application
from the App Store.
Types of Quantitative Data with Examples
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Types of Quantitative Data with
Examples
Measurement of physical objects:
Calculating measurement of any physical
thing.
Sensory calculation: Mechanism to
naturally “sense” the measured
parameters to create a constant source
of information.
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● Quantification of qualitative entities:
Identify numbers to qualitative
information.
● Projection of data:
Future projection of data can be
done using algorithms and other
mathematical analysis tools.
Types of Quantitative Data with Examples
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Quantitative Data: Analysis Methods
Cross-tabulation:
Cross-tabulation is the most widely
used quantitative data analysis
methods.
Surveys:
Traditionally, surveys were
conducted using paper-based
methods and have gradually evolved
into online mediums.
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To administer a Survey to collect quantitative
data, the below principles are to be followed
Fundamental Levels of Measurement –
Nominal, Ordinal, Interval and Ratio Scales
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Use of Different Question Types:
They can be a mix of multiple question types
including multiple-choice questions like
semantic differential scale questions, rating
scale questions etc. that can help collect data
that can be analyzed.
Survey Distribution and Survey Data Collection: In
the above, we have seen the process of building a
survey along with the survey design to collect
quantitative data. Survey distribution to collect
data is the other important aspect of the survey
process.
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To administer a Survey to collect quantitative data,
the below principles are to be followed
One-on-one Interviews: This quantitative data collection method was
also traditionally conducted face-to-face but has shifted to telephonic
and online platforms.
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Quantitative Data: Analysis Methods
Trend analysis: Trend analysis is a
statistical analysis method that
provides the ability to look at
quantitative data that has been
collected over a long period of time.
MaxDiff analysis: The MaxDiff analysis is a
quantitative data analysis method that
is used to gauge customer preferences
for a purchase and what parameters
rank higher than the others in this
process.
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Quantitative Data: Analysis Methods
• Conjoint analysis: Like in the above method,
conjoint analysis is a similar quantitative data
analysis method that analyzes parameters behind
a purchasing decision.
• This method possesses the ability to collect and
analyze advanced metrics which provide an in-
depth insight into purchasing decisions as well as
the parameters that rank the most important.
• TURF analysis: TURF analysis or Total Unduplicated
Reach and Frequency Analysis, is a quantitative data
analysis methodology that assesses the total market
reach of a product or service or a mix of both.
• This method is used by organizations to understand
the frequency and the avenues at which their
messaging reaches customers and prospective
customers which helps them tweak their go-to-market
strategies.
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Quantitative Data: Analysis Methods
● Gap analysis: Gap analysis uses a side-by-side
matrix to depict quantitative data that helps
measure the difference between expected
performance and actual performance.
● This data analysis helps measure gaps in
performance and the things that are required
to be done to bridge this gap.
SWOT analysis: SWOT analysis, is a
quantitative data analysis methods that
assigns numerical values to indicate strength,
weaknesses, opportunities and threats of an
organization or product or service which in
turn provides a holistic picture about
competition.
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Quantitative Data: Analysis Methods
● Text analysis: Text analysis is an advanced statistical method
where intelligent tools make sense of and quantify or fashion
qualitative and open-ended data into easily understandable
data.
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Steps to conduct Quantitative Data Analysis
Relate measurement scales with
variables: Associate measurement scales
such as Nominal, Ordinal, Interval and
Ratio with the variables.
Connect descriptive statistics with
data: Link descriptive statistics to
encapsulate available data. It can be
difficult to establish a pattern in the
raw data.
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Steps to Conduct Quantitative Data Analysis
Connect descriptive statistics with data:
Link descriptive statistics to encapsulate
available data. It can be difficult to establish
a pattern in the raw data.
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Advantages of Quantitative Data
● Conduct in-depth research: Since quantitative data can be
statistically analyzed, it is highly likely that the research will be
detailed.
● Minimum bias: There are instances in research, where
personal bias is involved which leads to incorrect results. Due
to the numerical nature of quantitative data, the personal bias
is reduced to a great extent.
● Accurate results: As the results obtained are objective in
nature, they are extremely accurate.
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Disadvantages of Quantitative Data
● Restricted information: Because quantitative data is not
descriptive, it becomes difficult for researchers to make decisions
based solely on the collected information.
● Depends on question types: Bias in results is dependent on the
question types included to collect quantitative data. The
researcher’s knowledge of questions and the objective of research
are exceedingly important while collecting quantitative data.
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What is Critical Appraisal Of Quantitative
Research?
● Critical appraisal describes the
process of analyzing a study in a
rigorous and methodical way.
Often, this process involves
working through a series of
questions to assess the “quality” of
a study by examining its strengths
and limitations.
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How to critically appraise a Research paper?
Is the study question relevant to my field?
Does the study add anything new to the evidence in
my field?
What type of research question is being
asked?
Was the study design appropriate for the
research question?
Did the methodology address important
potential sources of bias?
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Validity in Quantitative Research
Internal -
Does the
research
measure what
it is supposed
to be
measuring?
External -
Can the
results be
applied to
the wider
population?
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Reliability
Reliability concerned with random, one-off, errors whereas validity
concerned with systematic or constant error – e.g. improperly
calibrated scales might be reliable, but would be invalid.
Writing up the appraisal
1. Go through each element one at a time e.g. randomization or sampling
approach and directly compare all your articles under this.
2. Move onto the next element.
3. Are some better than others in terms of internal and external validity and
reliability?
4. After all the elements, can you say if any of your articles are better overall
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What is Qualitative Research?
• QUALITATIVE RESEARCH is the process of
collecting, analyzing, and interpreting non-
numerical data, such as language.
• QUALITATIVE RESEARCH can be used to
understand how an individual subjectively
perceives and gives meaning to their social
reality.
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• GROUNDED THEORY is a systematic procedure of data analysis,
typically associated with qualitative research that allows researchers
to develop a theory that explains a specific phenomenon.
• THE PRIMARY DATA COLLECTION METHOD is through
interviews of approximately 20 – 30 participants or until data
achieves saturation.
• ETHNOGRAPHY is used when a researcher wants to study a group
of people to gain a larger understanding of their lives or specific
aspects of their lives.
Types of Qualitative Research Designs / Methods
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• THE PRIMARY DATA COLLECTION METHOD is through observation
over an extended period of time. It would also be appropriate to
interview others who have studied the same cultures.
• PHENOMENOLOGY is used to identify phenomena and focus on
subjective experiences and understanding the structure of those lived
experiences.
• The primary data collection method is through in-depth interviews.
Types of Qualitative Research Designs / Methods
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Types of Qualitative Research Designs / Methods
• Case studies are to be used when (1) the researcher wants to focus on
how and why, (2) the behavior is to be observed, not manipulated, (3)
to further understand a given phenomenon, and (4) if the boundaries
between the context and phenomena are not clear.
• Multiple methods can be used to gather data, including interviews,
observation, and historical documentation.
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Qualitative Data Analysis
Qualitative data refers to non-numeric information such as interview
transcripts, notes, video and audio recordings, images and text
documents.
Analyzing your data is vital, as you have spent time and money collecting
it. It is an essential process because you don’t want to find yourself in the
dark even after putting in so much effort. However, there are no set ground
rules for analyzing qualitative data; it all begins with understanding the two
main approaches to qualitative data.
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1. CONTENT ANALYSIS. This refers to the process of categorizing verbal or
behavioral data to classify, summarize and tabulate the data.
2. NARRATIVE ANALYSIS. This method involves the reformulation of stories
presented by respondents taking into account context of each case and
different experiences of each respondent. In other words, narrative analysis
is the revision of primary qualitative data by researcher.
3. DISCOURSE ANALYSIS. A method of analysis of naturally occurring talk
and all types of written text.
4. FRAMEWORK ANALYSIS. This is more advanced method that consists of
several stages such as familiarization, identifying a thematic framework,
coding, charting, mapping and interpretation
5. GROUNDED THEORY. This method of qualitative data analysis starts with
an analysis of a single case to formulate a theory. Then, additional cases
are examined to see if they contribute to the theory..
Qualitative data analysis can be divided into the following five
categories:
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Qualitative data analysis can be conducted through the following
three steps:
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.
1. Open coding. The initial organization of raw data to try to make sense of it.
2. Axial coding. Interconnecting and linking the categories of codes.
3. Selective coding. Formulating the story through connecting the categories.
Example:
Research title Elements to be coded Codes
Born or bred: revising The Great
Man theory of leadership in the
21
st
century
Leadership practice
Born leaders
Made leaders
Leadership effectiveness
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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. Analytical and critical thinking skills of
researcher plays significant role in data analysis in qualitative studies.
• 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;
• 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
Qualitative data analysis can be conducted through the following
three steps:
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Step 3: Summarizing the data.
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.
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Two Main Approaches to Qualitative Data Analysis
Deductive Approach
The deductive approach
involves analyzing qualitative
data based on a structure that
is predetermined by the
researcher. A researcher can
use the questions as a guide
for analyzing the data. This
approach is quick and easy
and can be used when a
researcher has a fair idea
about the likely responses that
he/she is going to receive
from the sample population.
Inductive Approach
The inductive approach, on
the contrary, is not based on
a predetermined structure or
set ground rules/framework.
It is a more time-consuming
and thorough approach to
qualitative data analysis. An
inductive approach is often
used when a researcher has
very little or no idea of the
research phenomenon.
