This document provides an overview of preparing data for analysis, including editing, coding, classification, tabulation, validation, analysis, and interpretation. It discusses the key steps in editing data to ensure accuracy and consistency. Coding involves translating answers into numerical values to facilitate quantitative analysis. Classification involves organizing data into geographical, qualitative, quantitative, and chronological categories. Tabulation and validation are important for systematically presenting data and checking its accuracy. Different types of analysis, including uni-variate, bi-variate, and multi-variate, are used to describe variables. Proper interpretation communicates the meaning of analyzed data.

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14MBA23 – RM Notes
Research Methods
Module 5 – MBA – 2nd
Semester
V T University Syllabus

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14MBA23 – Research Methods
Syllabus of Module 5:
Preparing the Data for Analysis: Editing, Coding,
Classification, Tabulation, Validation Analysis and
Interpretation.
(This module emphasizes on activities, for conversion of RAW DATA
converted to a MEANINGFUL INFORMATION)

3. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Editing, : Editing is the process of checking and
adjusting the data for omissions, legibility, and
consistency. Editing may be differentiated from
coding, which is the assignment of numerical scales
or classifying symbols to previously edited data.
There are various variables, which need to be edited,
they include:
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4. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Editing, : Types of editing are:
1. Validation Edits: The researcher checks its validity.,
2. Logical Edits: The researcher satisfies, that two data
are not contradictory.,
3. Consistency Edits: The researcher satisfies, that
‘Consistent or correct arithmetic relationship is
found between data items.,
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5. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Editing, : (Contd…)
4. Range Edits: The researcher satisfies, that data
values fall in the acceptable range.,
5. Variance Edits: The researcher satisfies, that that
every data has a uniform variances., (there exists no
high variance between 2 variables).,
6. Micro-Editing and Macro-Editing: The researcher
distinguish in order to calculate the range of edits, in
his research. 5

6. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Editing, : Hence editing is one of the most important
activity to eradicate, minimize, detecting and
correcting errors in data. (Error Free Questionnaire
and so on).
Editing helps the researcher to collect only those
relevant data for his research and omit those which
are irrelevant for his research.
Editing makes to deduct errors, fill the missing data
and make the data complete for next step in research.
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7. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Coding, : Coding is translating answers into numerical
values or assigning numbers to the various categories
of a variable to be used in data analysis.
Coding is done by using a ‘Code book’, ‘Code Sheet’,
and a ‘Computer Card’.
Coding is done on the basis of the instructions given in
the codebook. The code book gives a numerical code
for each variable/ specification or description.
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8. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Coding, : Coding is usually done after examining the
each scale, and allocating the appropriate number
stating the proper range between the different scales.
In brief it can be said that ‘Coding’ is the conversion
of verbal (Qualitative) values to numerical
(Quantitative) values, therefore the study can be
more specific with relevant values.
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9. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Time of Coding, :
1.Before examining the data collected., (Applicable
in Deductive Hypothesis – Those research is based
on Theory is the Deductive Hypothesis)
2.After examining the data collected. (Applicable in
Inductive Hypothesis – Those study which are based
on ased on Observation)
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10. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Importance of Coding, :
1.To analyze the data,
2.To make the study meaningful,
3.To obtain to quantitative results,
4.To facilitate interpretation of the data collected,
5.To arrive and make the conclusion more specific.
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11. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
Distribution of data as a form of classification of scores
obtained for the various categories or a particular
variable.
There are four types of distributions, also four types
of classifications stated as under:
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12. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
In classification of the data, can be done in four
categories as follows:.
1.Geographical Classification,
2.Qualitative Classification,
3.Quantitative Classification,
4.Chronological Classification
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13. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
In classification of the data, can be done in four
categories as follows:.
1.Geographical Classification,: This classification
can be done in geographical manner, Ex: East Zone,
West Zone, South Zone, North Zone and Central
Zone (Best Example is the division of our Indian
Railways)
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14. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
In classification of the data, can be done in four
categories as follows:.
1.Qualitative Classification,: This classification is as
per the quality attributes like sense, sex, marital
status, literate level and so on.
Qualitative classification can be single or multi-mode
classification.
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15. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
In classification of the data, can be done in four
categories as follows:.
1.Quantitative Classification,: This classification is
based on quantitative attributes like weight, length,
Meters, Age or any attributes which can be measured
easily with numerical values.
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16. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Classification, : or The Data Classification:
In classification of the data, can be done in four
categories as follows:.
1.Chronological Classification, This classification is
based on the arrangement of the data, which can be
ascending or descending order.
In business operations, it is arranged or classified on
year based, month or week based.
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17. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Data Classification, : These data can also be
distributed as follows: The distribution classification
can be as follows:
1. Frequency distribution
2. Percentage distribution
3. Cumulative distribution
4. Statistical distribution
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18. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Data Classification, : These data can also be distributed as
follows: The distribution classification can be as follows:
Frequency distribution, : Frequency wise distributing the
group or ungroup.
Example for Grouped distribution can be All 1st
MBA
Students,
Example for Ungrouped distribution can be All MBA
students.
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19. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Data Classification, : These data can also be
distributed as follows: The distribution classification
can be as follows:
Percentage distribution, : This is classification of the
population based on percentage.
Example for percentage distribution, can be calculated x%
of students from Mysuru, and y% of students are non-
Mysuru Students.
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20. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Data Classification, : These data can also be
distributed as follows: The distribution classification
can be as follows:
Cumulative distribution, : This classification states the
cumulative distribution of the population.
Example for cumulative distribution, can the Age,
Income, Qualification cumulatively taken and divide
the population of Mysuru City.
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21. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Data Classification, : These data can also be distributed as
follows: The distribution classification can be as follows:
Statistical distribution, : This is the classification of the
population based on some kind of averages or Mean, Median
and Mode.
Example for statistical distribution in a business unit can be
divided into several business heads and collect the statistical
data of several years for each business head. (Ex of Business
Heads in each district of the state)
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22. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Tabulation, After editing, which ensures that the
information on the schedule is accurate and
categorized in a suitable form, the data are put
together in some kinds of tables and may also
undergo some other forms of statistical analysis.
Table can be prepared manually and/or by
computers. For a small study of 100 to 200 persons,
there may be little point in tabulating by computer
since this necessitates putting the data on punched
cards. 22

23. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Tabulation, Tabulation is the systematic presentation
of numerical data in rows and columns, classified
each with its characteristics, which displays a
accurate, clear and easy to understand the data
represented in the form of table.
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24. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Importance of Tabulation, Tabulation facilitates the
researcher to represent the data, in a concise form to
enable others to understand the same easily.
These include,
1.Tabulation simplifies the complex data,
2.Tabulation facilitates to identify each variables,
3.Tabulation facilitates to compare the variables.
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25. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Essentials of Tabulation, The essentials of Tabulation include;
1. Title of the table, (Main Head)
2. Caption, (Rows Caption and Columns Caption)
3. Box Head, (Column Caption is called Box Head)
4. Stub, (Row Caption is called the stub)
5. Body of the table, (Data represented in the table)
6. Prefatory or Head notes, (Appears between title and body, Ex: in units, in Rs)
7. Foot Notes, (Appears below the note, end of the page)
8. Source note, (Below the foot note to furnish the source of information)
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26. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Types of Tabulation, : The types include,
1.One way Tabulation or Simple Tabulation, This
table is classified with one characteristics only, Ex:
Religion.
2.Two Way Tabulation or Double Tabulation, This
table has two characteristics furnished. Ex: Religion
and Sex, and
3.Multi Tabulation, This table has multiple variances
in one table. Ex: Religion, Sex, Age, Literacy,
Income and so on. 26

27. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Validation, : Data Validation is the process of
checking the data base collected is accurate, clean
and specific to the objective of the study.
Validation requires that the data need to be checked to
confirm that the data collected can be verified and
confirmed on the genuine data collected.
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28. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Types of Data Validation Techniques, :
1.Form Level Validation,: This states that all the fields
required are filled by the user/ respondent, before
submission.
2.Field Level Validation,: This checks the validity of
the fields, Ex: A name can’t be in numbers, or a
email can’t be without @ symbol and so on.
3.Data Savings Validation,: This verifies that the
(column and rows) fields filled are been saved. 28

29. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Types of Data Validation Techniques, :
4. Search Criteria Validation, : This confirms that the
search engines depended and gathered information
are true, and
5. Range Validation, : This verifies and checks that the
values, characters or numbers fall on the specific
range only.
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30. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Analysis of Data ,: Analysis of data is the process of
inspection, cleaning and transforming and modeling
data with the goal of highlighting useful information,
suggestions, conclusions and supporting decision
making.
Data Analysis is a multi-facet approach, encompassing
diverse techniques under a variety of names, in
different business, science and social science
domain.
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31. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Types of Data Analysis ,:
1.Uni-variate Data Analysis, : (Description of single
Variable and its attributes)
2.Bi-variate Data Analysis,: (Description of two
Variables and its attributes – Independent and
Dependent Variables)
3.Multi-Variate Data Analysis,: (Description of multi
Variables and its attributes).
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32. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Process of Data Analysis ,:
Stage 1 - Data Cleaning,
Stage 2 – Initial Data Analysis,
Stage 3 – Checking the Quality of Data,
Stage 4 – Measurement of Quality,
Stage 5 – Initial Transformation,
Stage 6 – Characteristics of Data Sample,
Stage 7 – Final Stage of Initial Data Analysis. 32

33. 14MBA23 – RM – M5
Preparing the Data for Analysis:
Interpretation, : Data Interpretation is the furnishing
the data collected into a descriptive form. This
interpretation is done when the table or graph is
prepared and the same need to be expressed in a
paragraph.
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14MBA23 – Research Methods
End of Module 5
Sanjeev Kumar Singh.,
MBA DEPARTMENT, V T UNIVERSITY
Mob: +91 91640 76660
Email: harsubhmys@yahoo.co.in