Data Analysis & Interpretation
Data analysis is defined as a process of cleaning, transforming, and
modeling data to discover useful information for business decisionmaking. The purpose of Data Analysis is to extract useful information
from data and taking the decision based upon the data analysis.
Data Analysis Process
The Data Analysis Process is nothing but gathering information by
using a proper application or tool which allows you to explore the
data and find a pattern in it. Based on that information and data, you
can make decisions, or you can get ultimate conclusions.
Data Analysis consists of the following phases:
Data Requirement Gathering
Data Collection
Data Cleaning
Data Analysis
Data Interpretation
Data Visualization
Data Interpretation
Data interpretation is the process of reviewing data
through some predefined processes which will help assign some
meaning to the data and arrive at a relevant conclusion. It involves
taking the result of data analysis. Data analysis is the process of
ordering, categorizing, manipulating, and summarizing data to obtain
answers to research questions. It is usually the first step taken towards
data interpretation.
It is evident that the interpretation of data is very
important, and as such needs to be done properly. Therefore,
researchers have identified some data interpretation methods to aid
this process.
Data Interpretation Methods
Data interpretation methods are how analysts help people make sense
of numerical data that has been collected, analyzed and presented.
Data, when collected in raw form, may be difficult for the layman to
understand, which is why analysts need to break down the
information gathered so that others can make sense of it.
For example, when founders are pitching to potential investors, they
must interpret data (e.g. market size, growth rate, etc.) for better
understanding. There are 2 main methods in which this can be done,
namely; quantitative methods and qualitative methods.
Qualitative Data Interpretation Method
The qualitative data interpretation method is used to analyze
qualitative data, which is also known as categorical data. This method
uses texts, rather than numbers or patterns to describe data. Qualitative data is usually gathered using a wide variety of person-toperson techniques, which may be difficult to analyze compared to the
quantitative research method.
Unlike the quantitative data which can be analyzed directly after it has
been collected and sorted, qualitative data needs to first be coded into
numbers before it can be analyzed. This is because texts are usually
cumbersome, and will take more time and result in a lot of errors if
analyzed in its original state. Coding done by the analyst should also
be documented so that it can be reused by others and also analyzed.
There are 2 main types of qualitative data, namely; nominal and
ordinal data. These 2 data types are both interpreted using the same
method, but ordinal data interpretation is quite easier than that of
nominal data.
In most cases, ordinal data is usually labelled with numbers during the
process of data collection, and coding may not be required. This is
different from nominal data that still needs to be coded for proper
interpretation.
Quantitative Data Interpretation Method
The quantitative data interpretation method is used to analyze
quantitative data, which is also known as numerical data. This data
type contains numbers and is therefore analyzed with the use of
numbers and not texts.
Quantitative data are of 2 main types, namely; discrete and continuous
data. Continuous data is further divided into interval data and ratio
data, with all the data types being numeric.
Due to its natural existence as a number, analysts do not need to
employ the coding technique on quantitative data before it is
analyzed. The process of analyzing quantitative data involves
statistical modelling techniques such as standard deviation, mean and
median.Some of the statistical methods used in analyzing quantitative data are
highlighted below:
Mean
The mean is a numerical average for a set of data and is calculated by
dividing the sum of the values by the number of values in a dataset. It
is used to get an estimate of a large population from the dataset
obtained from a sample of the population.
Standard deviation
This technique is used to measure how well the responses align with
or deviates from the mean. It describes the degree of consistency
within the responses; together with the mean, it provides insight into
data sets.
Frequency distribution
This technique is used to assess the demography of the respondents or
the number of times a particular response appears in research. It is
extremely keen on determining the degree of intersection between
data points.
Advantages of Data Interpretation
It helps to make informed decisions and not just through
guessing or predictions.
It is cost-efficient
The insights obtained can be used to set and identify trends in
data.
Conclusion
Data interpretation and analysis is an important aspect of working
with data sets in any field or research and statistics. They both go hand in hand, as the process of data interpretation involves the
analysis of data.
Data interpretation is very important, as it helps to acquire useful
information from a pool of irrelevant ones while making informed
decisions. It is found useful for individuals, businesses, and
researchers.
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