Research Methodology-Data Processing

1. Data processing is concerned with editing,
coding, classifying, tabulating and charting
and diagramming research data Data
processing in research consists of five
important steps
 1. Editing of data
 2. Coding of data
 3. Classification of data
 4. Tabulation of data
 5. Data diagrams

2. Data processing occurs when data is collected
and translated into usable information. Data
processing starts with data in its raw form
and converts it into a more readable format
(graphs, documents, etc.), giving it the form
and context necessary to be interpreted by
computers and utilized by employees
throughout an organization.

3. 1. Data collection Collecting data is the first step in data
processing. Data is pulled from available sources,
including data lakes and data warehouses. It is
important that the data sources available are trustworthy
and well-built so the data collected is of the highest
possible quality.
2. 2. Data preparation Once the data is collected, it then
enters the data preparation stage. Data preparation,
often referred to as “pre-processing” is the stage at
which raw data is cleaned up and organized for the
following stage of data processing. During preparation,
raw data is thoroughly checked for any errors. The
purpose of this step is to eliminate incomplete, or
incorrect data and begin to create high-quality data for
the best business intelligence.

4.  3. Data input The clean data is then entered into
its destination and translated into a language
that it can understand. Data input is the first
stage in which raw data begins to take the form
of usable information.
 4. Processing During this stage, the data inputted
to the computer in the previous stage is actually
processed for interpretation. Processing is done
using machine learning algorithms, though the
process itself may vary slightly depending on the
source of data being processed (data lakes, social
networks, connected devices etc.) and its
intended use (examining advertising patterns,
medical diagnosis from connected devices,
determining customer needs, etc.).

5.  5. Data output/interpretation The output/interpretation
stage is the stage at which data is finally usable to non-
data scientists. It is translated, readable, and often in the
form of graphs, videos, images, plain text, etc.). Members
of the company or institution can now begin to self-serve
the data for their own data analytics projects.
 6. Data storage and Report Writing The final stage of data
processing is storage. After all of the data is processed, it
is then stored for future use. While some information may
be put to use immediately, much of it will serve a purpose
later on. Plus, properly stored data is a necessity for
compliance with data protection legislation like GDPR.
When data is properly stored, it can be quickly and easily
accessed by members of the organization when needed.

6. First step in analysis is to edit the raw data. Editing detects
errors and omissions, corrects them whatever possible. Editor’s
responsibility is to guarantee that data are – accurate; consistent
with the intent of the questionnaire; uniformly entered;
complete; and arranged to simplify coding and tabulation.
Editing of data may be accomplished in two ways –
(i) field editing
Field editing is preliminary editing of data by a field supervisor
on the same data as the interview. Its purpose is to identify
technical omissions, check legibility, and clarify responses that
are logically and conceptually inconsistent. When gaps are
present from interviews, a call-back should be made rather than
guessing what the respondent would probably said. Supervisor is
to re-interview a few respondents at least on some pre-selected
questions as a validity check.
(ii) in-house also called central editing.
In center or in-house editing all the questionnaires undergo
thorough editing. It is a rigorous job performed by central office
staff.

7. Coding refers to the process of assigning numerals or other
symbols to answers so that responses can be put into a limited
number of categories or classes. Such classes should be
appropriate to the research problem under consideration. They
must also possess the characteristic of exhaustiveness (i.e., there
must be a class for every data item)
Coding is necessary for efficient analysis and through it the
several replies may be reduced to a small number of classes
which contain the critical information required for analysis.
Coding decisions should usually be taken at the designing stage
of the questionnaire. This makes it possible to precode the
questionnaire choices and which in turn is helpful for computer
tabulation as one can straight forward key punch from the
original questionnaires. But in case of hand coding some
standard method may be used. One such standard method is to
code in the margin with a coloured pencil. The other method can
be to transcribe the data from the questionnaire to a coding
sheet. Whatever method is adopted, one should see that coding
errors are altogether eliminated or reduced to the minimum
level.

9. Most research studies result in a large volume
of raw data which must be reduced into
homogeneous groups if we are to get
meaningful relationships. This fact
necessitates classification of data which
happens to be the process of arranging data
in groups or classes on the basis of common
characteristics. Data having a common
characteristic are placed in one class and in
this way the entire data get divided into a
number of groups or classes.

11.  (a) Classification according to attributes: As stated above,
data are classified on the basis of common characteristics
which can either be descriptive (such as literacy, sex,
honesty, etc.) or numerical (such as weight, height,
income, etc.). Descriptive characteristics refer to
qualitative phenomenon which cannot be measured
quantitatively; only their presence or absence in an
individual item can be noticed
 (b) Classification according to class-intervals: Unlike
descriptive characteristics, the numerical characteristics
refer to quantitative phenomenon which can be measured
through some statistical units. Data relating to income,
production, age, weight, etc. come under this category.
Such data are known as statistics of variables and are
classified on the basis of class intervals.

12.  Tabulation is a systematic & logical
presentation of numeric data in rows and
columns to facilitate comparison and
statistical analysis. It facilitates comparison
by bringing related information close to each
other and helps in further statistical analysis
and interpretation. In other words, the
method of placing organised data into a
tabular form is called as tabulation. It may be
complex, double or simple depending upon
the nature of categorisation.

14.  Graphical Representation is a way of
analysing numerical data. It exhibits the
relation between data, ideas, information and
concepts in a diagram. It is easy to
understand and it is one of the most
important learning strategies. It always
depends on the type of information in a
particular domain.

15.  Line Graphs – Line graph or the linear graph is used to display the
continuous data and it is useful for predicting future events over time.
 Bar Graphs – Bar Graph is used to display the category of data and it
compares the data using solid bars to represent the quantities.
 Histograms – The graph that uses bars to represent the frequency of
numerical data that are organised into intervals. Since all the intervals are
equal and continuous, all the bars have the same width.
 Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is
placed above a number line each time when that data occurs again.
 Frequency Table – The table shows the number of pieces of data that falls
within the given interval.
 Circle Graph – Also known as the pie chart that shows the relationships of
the parts of the whole. The circle is considered with 100% and the categories
occupied is represented with that specific percentage like 15%, 56%, etc.
 Stem and Leaf Plot – In the stem and leaf plot, the data are organised from
least value to the greatest value. The digits of the least place values from the
leaves and the next place value digit forms the stems.
 Box and Whisker Plot – The plot diagram summarises the data by dividing
into four parts. Box and whisker show the range (spread) and the middle (
median) of the data.


17.  A hypothesis is a statement of the
researcher's expectation or prediction about
relationship among study variables. The
research process begins and ends with the
hypothesis. It is core to the entire procedure
and, therefore, is of the utmost importance.
Hypothesis is nothing but the heat of the
research.

18. Following are the characteristics of hypothesis:
 The hypothesis should be clear and precise to
consider it to be reliable.
 If the hypothesis is a relational hypothesis, then
it should be stating the relationship between
variables.
 The hypothesis must be specific and should have
scope for conducting more tests.
 The way of explanation of the hypothesis must
be very simple and it should also be understood
that the simplicity of the hypothesis is not related
to its significance.

19. Simple Hypothesis
Complex Hypothesis
Null Hypothesis
Alternative Hypothesis
Logical Hypothesis
Empirical Hypothesis
Statistical Hypothesis

20. Simple Hypothesis
A simple hypothesis predicts the relationship between two
variables: the independent variable and the dependent
variable. This relationship is demonstrated through these
examples.
 Drinking sugary drinks daily leads to being overweight.
 Smoking cigarettes daily leads to lung cancer.
Complex Hypothesis
A complex hypothesis describes a relationship between
variables. However, it’s a relationship between two or
more independent variables and two or more dependent
variables. You can follow these examples to get a better
understanding of a complex hypothesis.
 Adults who 1) drink sugary beverages on a daily basis and
2) have a family history of health issues are more likely to
a) become overweight and b) develop diabetes or other
health issues.

21. Null Hypothesis
A null hypothesis, denoted by H0, proposes that two factors or
groups are unrelated and that there is no difference between
certain characteristics of a population or process. You must test
the likelihood of the null hypothesis, in tandem with an
alternative hypothesis, in order to disprove or discredit it. Some
examples of a null hypothesis include:
 There is no significant change in a person’s health during the
times when they drink green tea only or coffee only.
Alternative Hypothesis
An alternative hypothesis, denoted by H1 or HA , is a claim that is
contradictory to the null hypothesis. Researchers will pair the
alternative hypothesis with the null hypothesis in order to prove
that there is no relation. If the null hypothesis is disproven, then
the alternative hypothesis will be accepted. If the null hypothesis
is not rejected, then the alternative hypothesis will not be
accepted. Some examples of alternative hypotheses are:
 A person’s health improves during the times when they drink
green tea only, as opposed to coffee only.

22. Logical Hypothesis
A logical hypothesis is a proposed explanation using limited
evidence. Generally, you want to turn a logical hypothesis into an
empirical hypothesis, putting your theories or postulations to the
test. In reference to these examples, there is currently no evidence
to support these hypotheses. However, you can form a hypothesis
based on the data available to draw a logical conclusion.
 Beings from mar would not be able to breathe the air in Earth's
atmosphere.
Empirical Hypothesis Examples
An empirical hypothesis, or working hypothesis, comes to life when
a theory is being put to the test using observation and experiment.
It's no longer just an idea or notion. Rather, it is going through trial
and error and perhaps changing around those independent
variables.
 Roses watered with liquid Vitamin B grow faster than roses watered
with liquid Vitamin E.
 Women taking vitamin E grow hair faster than those taking vitamin
K.

23. Statistical Hypothesis
A statistical hypothesis is an examination of a
portion of a population or statistical model. In this
type of analysis, you use statistical information
from an area. For example, if you wanted to
conduct a study on the life expectancy of people
from Savannah, you would want to examine every
single resident of Savannah. This is not practical.
Therefore, you would conduct your research using
a statistical hypothesis or a sample of Savannah's
population.
 50% madurai’s population lives beyond the age of
70.
 45% of the poor in the tamilnadu are illiterate.

24.  1. the act or the result of interpreting :
explanation. 2 : a particular adaptation or
version of a work, method, or style.
 Interpretation is the act of explaining,
reframing, or otherwise showing your own
understanding of something. ... Interpretation
requires you to first understand the piece of
music, text, language, or idea, and then give
your explanation of it.

25.  Inference is using observation and
background to reach a logical conclusion.
You probably practice inference every day.
For example,
if you see someone eating a new food and he
or she makes a face, then you infer he does
not like it. Or if someone slams a door, you
can infer that she is upset about something