This document discusses research methodology and processing of data. It covers editing, coding, classification, and tabulation as important steps in processing data collected during research. Editing involves correcting errors and omissions in the data. Coding assigns standardized codes to responses for efficient analysis. Classification groups the data based on common characteristics. Tabulation arranges the classified data in an organized table for analysis. The document also defines hypothesis and discusses types of hypotheses, characteristics of a good hypothesis, and the procedure for testing hypotheses using statistical techniques. Finally, it defines interpretation as drawing inferences from analyzed data and discusses techniques for proper interpretation.

1. Research Methodology
Part 2
Dr.M.Neelavathy,
M.Com(CA).,M.phil,Ph.d,CGT,DGT
Assistant Professor
Department of Commerce,
EMG Yadava Women’s College,
Thiruppalai,
Madurai.14

2. Processing of data
Processing refers to subjecting the data collected to a process
in which, the accuracy, completeness, uniformity of entries and
consistency of information gathered are examined. It is a very
important stage before the data is analyzed.
Processing of
Data
Editing Coding Classification Tabulation

3. 1. Editing – Editing means to rectify or to set to order or to correct of to
establish sequence. Once the data collection is completed, it is examined
carefully to eliminate any errors of mistakes. It is better if the data
collected is verified even before the data analysis is carried out. So, in
the editing stage mistakes and errors in the data are removed. Data
entered in the questionnaire / schedule are carefully scrutinized. Persons
with the editing responsibility should be trained and experienced in this
job.
Types of Editing
Editing is performed at two stages and depending on that it could be
of two types: Field editing and Centralized editing.
Types of Editing
Field
Editing
Centralized
Editing

4. Field editing
Field editing refers to the performance of the editing immediately in the field
where data is collected. That is, as soon as the investigator collects the data, the
data is collected. The advantage of this is to correct the data at the stage when it is
collected. The nature of editing will depend upon the method of data collection.
For example, suppose in an interview involving common public about their opinion
on the adequacy of buses in a particular route, the investigators does not raise this
questions. But he writes down the notes stating that the people are not satisfied
with the number of buses. This response is only based on the investigator’s own
opinion and not truly reflecting the public opinion. Such type of interview notes
may be misleading. Therefore, field editing is undertaken within one or two days
of recording the information or collecting the data.

5. Centralized Editing
In this type of editing, editing is done by a person or a team after all
the recorded questionnaires / schedules are collected. So clearly, it is not carried
out on the field itself or immediately after the data are collected. In such
editing, normally the instructions regarding editing are printed and circulated
to the person or the team doing the editing. This is only to ensure that there is
uniformity in editing.
For example, the unit of data collection may indicate that monthly income
should be collected and recorded, but in the field, the investigators would have
collected annual income. Or sometimes, the answer for a particular question
would remain unfilled. Or the code letters given for specifying the answer
would have been ignored. All these corrections are carried out at one stretch in
all the questionnaires or schedules. Sometimes, the respondents may have to be
contacted for clarifying certain points.

6. 2. Coding
Coding is a practice which simplifies recording of answer. When standard
answers for a question could be indicated, each answer is assigned a code. So,
instead on writing the answer in full, the investigator simply writes the code. This
not only saves time but also helps to avoid consuming answer.
For example, suppose a computer hardware engineer is attending a fault in a
computer. The system may be out of order due to several reasons. Say, booting
failure, software corruption, improper cable link, low voltage, memory failure,
etc., the engineer suppose each one of the above causes is coded like A) for booting
failure, B) software corruption C) improper cable link D) low voltage E)memory
failure etc. then in his service report if the engineer writes the code B against the
reason for the failure, it is clearly understood. Otherwise some service engineer
would simply write system not working, which may mean several things.

7. It is advisable to use alphabets or numbers for coding, as these would be
immediately recognized by the investigator. Care should be taken to avoid mixing
alphabets and number for the same answer. Each answer should have a distinct
code.
3. Classification
Classification of data means grouping the data on the basis of some
common characteristics. In other words, when some homogeneity could be
established in the data collected, then each group with similar characteristics
should be segregated. Such arrangement of data in groups or classes is called
classification.
Data analysis becomes easy with classified data : (a) common
characteristics or attributes like sex, literacy, colour, height, weight, age, etc., (b)
geographical regions like north, south, east, west etc., (c) time oriented classification
like yearly data, monthly data, weekly data, daily data etc., (d) value based
classification in which the collected data are grouped.

8. For example, data regarding marks scored by students, the data could be
classified as students between 0-10 marks, 10-20marks, 20-30marks and so on,
€ reply based classification like number of people who have answered ‘yes’ to a
question, ‘no’ to a question, no opinion to a question, etc.
Once the data is classified, then the frequency of each class is computed
and entered. In this process, tally bars or tally marks are used. Tally bars refers
to small vertical line each representing an occurrence. The tally bars erected for
each class is then counted and the numbers is recorded against each class.

9. 4.Tabulation
Tabulation is the arrangement of classified data in an orderly manner.
This involves creating tables for recoding the data. The process of presenting in an
orderly manner the classified in a table is called tabulation. In other words, it is a
method of presenting the summarized data. Tabulation is very important because
i) it helps to conserve space, ii) it avoids any need for explanation iii) computation
of the data is made easier, iv)comparison of data becomes very simple, v) adequacy
or inadequacy of the data is clearly visible.
A table contains columns and rows. These columns and rows create
small boxes which are called cells. Entries made in each cell of box is understood
with the title of the column and the row. Tables are classified as a) one way table b)
two way table and c) multi – way table.

10. Testing of Hypothesis
Hypothesis means a mere assumption or some supposition
or a possibility to be proved or disproved.
Define Hypothesis
1. “A hypothesis is a statement capable of being tested and thereby
verified or rejected”. –Rummel and Ballin.
2. “A proposition which can be put to test to determine validity”.
- Goode and Hatt

11. Types of Hypothesis
i) Descriptive hypothesis: Descriptive hypothesis are propositions that describe the
existence, size, form or distribution of some variables. For example, “The per
capital income of Indian is lower than that of Chinese”.
ii) Relational hypothesis: It describe the relationship between two variables. For
example, “Families with higher income spend more on recreation”
iii) Working hypothesis: The working hypothesis indicates the nature of data and
methods of analysis required for the study. Working hypothesis are subject to
modification as the investigation proceeds.
iv) Null Hypothesis: When a hypothesis is stated negatively, it is called a null
hypothesis. A null hypothesis should always be specific. The null hypothesis is the
one which one wishes to disprove. For example “Age of the respondents does not
influence their job satisfaction”
v) Alternative hypothesis: The set of alternatives to the null hypothesis is referred to as
the alternative hypothesis. Alternative hypothesis is usually the one which one
wishes to prove. For example, “Age of the respondents influences their job
satisfaction”.

12. vi) Statistical hypothesis: It is a quantitative statement about a population. When
the researcher derives hypothesis from a sample and hopes it to be true for the
entire population it is known as statistical hypothesis. For example, “Group X is
older than Group Y”.
vii) Simple hypothesis (or) common sense hypothesis: It states the existence of
certain empirical uniformities. Many empirical uniformities are common in
sociological research. For example, “ Fresh students conform to the conventions
set by the seniors”.
viii) Composite hypothesis: These hypothesis aim at testing the existence of logically
derived relationship between empirical uniformities obtain. For example,
“Members of minority groups suffer from oppression psychosis”.
ix) Explanatory hypothesis: It states the existence of one independent variable
causes or leads to an effect on dependent variable. For example, “Yield of
tomato is influenced by the application of fertilizer”.

13. Characteristics of Hypothesis
Clarity
Scope of
verification
Specific Testing Linked to
Theory

14. Procedure of testing a hypothesis
i) Making a formal statement: Construct a formal statement of the null hypothesis
(Ho) and also of the alternative hypothesis (Ha).
ii) Selecting a statistical techniques: There are many important parametric tests,
which are frequently used in hypothesis testing. They are Z-test, t-test, chi-
square test and F-test. The researcher has to select the appropriate test for his
research.
iii) Selecting a significance level: The hypothesis are tested on pre-determined level
of significance. In practice, either 5% level or 1% level of significance is
adopted for accepting or rejecting a hypothesis.
iv) Choosing the two tailed and one tailed tests: The hypothesis indicates whether we
should use a one- tailed test or a two tailed test. It the alternative hypothesis
(Ha) is of the type greater than or of the type lesser than. We use a one tailed
test. On the other hand if the alternative hypothesis is of the type “ not equal
to” then we use a two-tailed test.

15. v)Compute the appropriate statistics from the sample data: A random sample has to be selected as
per the sample design decided, and for the collected data, the appropriate statistic or measure
with reference to the research question, type of hypothesis to be tested and the level of
measurement of the data.
vi) Compute the significance test value: After the sample statistics is calculated, the formula for
the selected significance test is used to obtain the calculated test value.
vii) Obtain the critical test value: We must locate the critical value in the table concerned with the
selected probability distribution for the given level of significance for the appropriate number
of degree of freedom. The critical value so located in the Table is commonly known as Table
value.
viii) Deriving the inference: The calculated value is then compared with the predetermined
critical value. If the calculated value exceeds the critical value at 5% level, then the difference
is considered as significant. On the other hand, if the calculated valued is less than the critical
value at 5% level the difference is considered as insignificant.
eg. The critical value of Z at 5% level is 1.96. if the calculated Z value is 2.72 then the
inference would be that the difference at 5% level is significant and this difference is a real
one.

16. Interpretation
Interpretation refers to drawing inferences, generalization and results
from the collected data after the analysis. Analysis is not complete without
interpretation can not proceed without analysis. Interpretation can be
conceived of as a part of analysis. It is the task of interpreter to find out a link
or a position of the study in the whole analytical framework. It connects the
findings with the available material in a particular area of research.
Define Interpretation
“ Scientific interpretation seeks for relationship between the data of a study
and between the study findings and other scientific knowledge”. - Jahada and
Cook.

17. Techniques of interpretation
(i) Researcher must give reasonable explanations of the relations which he has found, and
he must interpret the lines of relationship in terms of the underlying processes and
must try to find out the thread of uniformity that lies under the surface layer of his
diversified research findings.
(ii) Extraneous information, if collected during the study, must be considered while
interpreting the final results of research study, for it may prove to be a key factor in
understanding the problem under consideration.
(iii) It is advisable, before embarking upon final interpretation, to consult someone having
insight into the study and who is frank and honest and will not hesitate to point out
omissions and errors in logical argumentation. Such a consultation will result in correct
interpretation and, thus, will enhance the utility of research results.
(iv) Researcher must accomplish the task of interpretation only after considering all
relevant factors affecting the problem to avoid false generalization. He must be in no
hurry while interpreting results, for quite often the conclusions, which appear to be all
right at the beginning may not at all be accurate.