Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis

1. Data Presentation & Analysis
Dr. D. Heena Cowsar
Assistant Professor of Commerce
Bon Secours College for Women
Thanjavur
heena.raffi@gmail.com

2. Introduction
Data analysis is how researchers go from a mass of data to
meaningful insights.
Research data analysis is a process used by researchers for
reducing data to a story and interpreting it to derive
insights. The data analysis process helps in reducing a large
chunk of data into smaller fragments, which makes sense.
Three essential things take place during the data analysis
process — Data Organisation, Data reduction and Data
Analysis.
Data organization. Summarization and categorization
together contribute to becoming the second known method
used for Data reduction. It helps in finding patterns and
themes in the data for easy identification and linking. Third
and the last way is Data analysis – researchers do it in both
top-down or bottom-up fashion.

3. What is Data Analysis?
Data analysis is defined as a process of cleaning,
transforming, and modeling data to discover useful
information for business decision-making. The purpose
of Data Analysis is to extract useful information from
data and taking the decision based upon the data
analysis.
There are many different data analysis methods,
depending on the type of research. The few methods used
to analyze the quantitative and qualitative data are:

4. Stages of analyzing data preparation
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:
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…
Fraud, to infer whether each respondent was actually
interviewed or not.
Screening, to make sure that respondents were chosen as per
the research criteria.
Procedure, to check whether the data collection procedure
was duly followed.
Completeness, to ensure that the interviewer asked the
respondent all the questions, rather than just a few required

5. Step 2: Data Editing
Typically, large data sets include errors. For example,
respondents may fill fields incorrectly or skip them
accidentally. To make sure that there are no such errors, the
researcher should conduct basic data checks, check for
outliers, and edit the raw research data to identify and clear
out any data points that may hamper the accuracy of the
results.
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.
There are 4 methods to deal with missing data.
List wise deletion method
Mean/median/mode imputation
Last observation carried forward
Resurveying

6. Step 3: Data Coding
This is one of the most important steps in data
preparation. 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. (For
example, respondents between 13-15 years old would have
their age coded as 0, 16-18 as 1, 18-20 as 2, etc.)
Then during analysis, the researcher can deal with
simplified age brackets, rather than a massive range of
individual ages.

7. 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.
1. 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. A few commonly used
descriptive statistics are:
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.
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.

8. Descriptive Vs Inferential measures
1. Descriptive Statistics: Descriptive statistics is a term given to the
analysis of data that helps to describe, show and summarize data in a
meaningful way. It is a simple way to describe our data. Descriptive
statistics is very important to present our raw data in an
effective/meaningful way using numerical calculations or graphs or
tables. This type of statistics is applied to already known data.
Types of Descriptive Statistics:
Measure of Central Tendency
Measure of Variability
2. Inferential Statistics: In inferential statistics, predictions are made
by taking any group of data in which you are interested. It can be
defined as a random sample of data taken from a population to describe
and make inferences about the population. Any group of data that
includes all the data you are interested in is known as population. It
basically allows you to make predictions by taking a small sample
instead of working on the whole population.

9. Descriptive Vs Inferential measures
S.No Descriptive Statistics Inferential Statistics
1.
It gives information about raw data
which describes the data in some
manner.
It makes inferences about the
population using data drawn from
the population.
2.
It helps in organizing, analyzing,
and to present data in a meaningful
manner.
It allows us to compare data, and
make hypotheses and predictions.
3. It is used to describe a situation.
It is used to explain the chance of
occurrence of an event.
4.
It explains already known data and
is limited to a sample or population
having a small size.
It attempts to reach the conclusion
about the population.
5.
It can be achieved with the help of
charts, graphs, tables, etc.
It can be achieved by probability.

10. 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. A
few commonly used descriptive statistics are:
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.
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.

11. 2. 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. 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.

12. Steps in Qualitative Data Preparation and
Basic Data Analysis
Analysis and preparation happen in parallel and include
the following steps:
Getting familiar with the data: Since most qualitative
data is just words, the researcher should start by reading
the data several times to get familiar with it and start
looking for basic observations or patterns. This also
includes transcribing the data.
Revisiting research objectives: Here, the researcher
revisits the research objective and identifies the questions
that can be answered through the collected data.

13. Steps in Qualitative Data Preparation &
Basic Data Analysis
Developing a framework: Also known as coding or
indexing, here the researcher identifies broad ideas,
concepts, behaviors, or phrases and assigns codes to them.
For example, coding age, gender, socio-economic status, and
even concepts such as the positive or negative response to a
question. Coding is helpful in structuring and labeling the
data.
Identifying patterns and connections: Once the data is
coded, the research can start identifying themes, looking for
the most common responses to questions, identifying data
or patterns that can answer research questions, and finding
areas that can be explored further.

14. Methods used for data analysis in
qualitative research
There are several techniques to analyze the data in qualitative
research, but here are some commonly used methods,
Content Analysis: It is widely accepted and the most
frequently employed technique for data analysis in research
methodology. It can be used to analyze the documented
information from text, images, and sometimes from the physical
items. It depends on the research questions to predict when and
where to use this method.
Narrative Analysis: This method is used to analyze content
gathered from various sources such as personal interviews, field
observation, and surveys. The majority of times, stories, or
opinions shared by people are focused on finding answers to the
research questions.

15. Methods used for data analysis in
qualitative research
Discourse Analysis: Similar to narrative analysis, discourse
analysis is used to analyze the interactions with people.
Nevertheless, this particular method considers the social context
under which or within which the communication between the
researcher and respondent takes place. In addition to that,
discourse analysis also focuses on the lifestyle and day-to-day
environment while deriving any conclusion.
Grounded Theory: When you want to explain why a particular
phenomenon happened, then using grounded theory for
analyzing quality data is the best resort. Grounded theory is
applied to study data about the host of similar cases occurring in
different settings. When researchers are using this method, they
might alter explanations or produce new ones until they arrive
at some conclusion.

16. References
https://www.guru99.com/what-is-data-analysis.html