How Quantitative and Qualitative analysis helps in Data Analytics?
by Priyanka sharma Priyanka Sharma is a bloggerLook at Data long
enough and you will find all sorts of insights, patterns, and relationships
some more obvious than others.
Data Analytics is
the systematic effort to uncover it all-to find information about the
information. The goal of Data Analytics is to utilize this insight to help a
person or computer program make an objective decision that will most likely
yield the desired outcome.
It all starts with
an analyst who must first decide what data is most relevant to the business
objective.
What data should
he/she collect to answer a given business question? Next, the analyst must
prepare it for analysis by eliminating duplicate data and making sure the
remaining data is properly formatted so that the analytics software can work
with it. Once this issue of data quality is addressed, the actual analytics
begin.
What comprises Quantitative Data?
Quantitative
data is interpreted as the value of data in the structure of counts or numbers
where each data-set has a different numerical value correlated with it. This
data is any quantifiable evidence that can be utilized for mathematical
calculations and statistical analysis, such that real-life decisions can be
made established on these mathematical derivations. Quantitative data is
utilized to resolve questions such as “How many?”, “How often?”, “How much?”.
This data can be assessed and can also be conveniently analyzed utilizing
mathematical procedures.
For instance,
there are quantities conforming to various parameters, for instance, “How much
did that laptop cost?” is a question that will collect quantitative data. There
are values attributed to most measuring parameters such as pounds or kilograms
for weight, dollars for cost, etc.
Quantitative Data: Collection
Methods
There are two
central Quantitative Data Collection Methods:
Surveys: Traditionally, surveys were administered utilizing
paper-based procedures and have deliberately unfolded into online fora.
Closed-ended questions constitute a crucial portion of these surveys as they
are more beneficial in compiling quantitative data. The survey includes answer
options that they believe are the most reasonable for a specific question.
Surveys are vital in amassing acknowledgment from an audience which is enormous
than the formal size.
One-on-one
Interviews: This quantitative data collection method
was also traditionally performed face-to-face but has changed positions to
telephonic and online outlets. Interviews propose a marketer the alternative to
compile comprehensive data from the participants. Quantitative interviews are
enormously structured and influence a pivotal function in obtaining evidence.
What
constitutes Qualitative Data?
Qualitative data is interpreted as the data that approximates
and characterizes. Qualitative data can be examined and documented. This data
type is non-numerical in essence.
Qualitative
Data Collection Methods
One-to-One
Interviews: It is one of the commonly utilized data
collection devices for qualitative exploration, primarily because of its
subjective strategy. The interviewer or the researcher compiles data
immediately from the interviewee on a one-to-one basis. The interview may be
informal and unstructured – conversational. Mostly the open-ended questions are
asked spontaneously, with the interviewer allowing the flow of the interview to
dictate the questions be inquired.
2. Focus groups:
This is done in a group discussion
environment. The group is limited to 6-10 people, and a mediator is appointed to
mediate the continuous discussion.
To understand in a detailed manner about this arena
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How
Qualitative and Quantitative Data Helps in Analysis?
Now that we have enlisted the basics of qualitative
and quantitative data, it’s time to review which category is beneficial for
data analysis and why.
Qualitative data is commonly contemplated as
unstructured data or semi-structured. This type of data is loosely formatted
with very limited structure. Due to this, qualitative data cannot be assembled
and assessed using traditional procedures. Understanding of the qualitative
data can be time-consuming and expensive, although there are some procedures of
“structuring” this data.
The growth of No SQL databases has made the
exhibition and storing of qualitative data much more adaptable, but it's still
an upward spurt before being eligible to stifle this data effectively.
Quantitative data is usually perceived as structured
data. This kind of data is formatted in a means so it can be instantly composed
and searchable within relational databases. Since qualitative data and
structured data go hand-in-hand, this kind of data is usually desired for data
analysis.
Bottom Line:
Data analysis is presumably the most significant
element of exploration. The unstable examination generates faulty outcomes that
not only impede the genuineness of the study but also make the outcomes
unusable. It’s imperative to select your data analysis techniques carefully to
guarantee that your discoveries are insightful and actionable.
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Created on Sep 28th 2020 07:32. Viewed 388 times.