1. The document discusses various steps involved in data processing such as editing, coding, tabulation and cross-tabulation. 2. Editing involves detecting and correcting errors in raw data to ensure accuracy, completeness, consistency and uniformity. Coding assigns numerical codes to questionnaire responses to simplify analysis. 3. Tabulation organizes data into tables or lists to facilitate analysis and comparison. It can be univariate, tabulating one variable, or bivariate/multivariate, tabulating relationships between two or more variables. 4. Cross-tabulation summarizes the relationship between two or more categorical variables by classifying data into a contingency table. It provides a basic understanding of how variables are interrelated.

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Dr. Amitabh Mishra

2. Editing
of
Data
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3. Editing
• “Editing is a step where by researchers eliminate errors
or points of confusion in the raw data”.
• “Editing detects the errors, correct them when possible
and certifies that minimum data quality standards have
been achieved”.
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4. Objectives of Editing
• The purpose of editing is to guarantee that the
data are-
1. Accurate
2. Complete
3. Uniformly entered
4. Consistent with intent of questions
5. Arranged to simplify coding and tabulation.
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5. NEED FOR EDITING
Editing is needed because-
1. Parts of the questionnaire may be incomplete
2. The pattern of responses may indicate that the respondent did
not understood or follow the instructions
3. The responses show little variance
4. One or more pages are missing
5. The questionnaire is answered by someone who does not
qualify for participation
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6. Stages of Editing
• Editing can be done on two stages-
1. Field editing
2. Central editing
• Field editing is responsibility of field supervisor. During data
collection field worker/respondents often use abbreviations
and special symbols. soon after data have been gathered
interviewer must review the questionnaire.
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7. • After the field work is done trained and experienced
editors check and edit each questionnaire
thoroughly.
• Editors identify the inconsistencies between the
answers.
• Editor’s task is identify the fake interviews (Fake
interviews can be identified by checking responses of open ended
questions).
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8. Treatment of Unsatisfactory Results
1. Returning to the Field
(The questionnaires with unsatisfactory responses may be returned to the field,
where the interviewers re -contact the respondents)
2. Assigning Missing Values
(If returning the questionnaires to the field is not feasible, the editor may assign
missing values to unsatisfactory responses.)
3. Discarding Unsatisfactory Respondents
(In this approach, the respondents with unsatisfactory responses are simply
discarded)
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9. Coding
of
Data
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10. Coding
“Coding means assigning a code, usually a number, to each
possible response to each question.
“Coding involves assigning numbers or other symbols to
answer so the responses can be grouped in to a limited
number of classes or categories”- Cooper & Schindler
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11. Example
S.N. Category Code
1 Male 1
2 Female 2
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S.N. Category Code
1 Male M
2 Female F
S.N. Category Code
1 Male
2 Female

12. Rules of Coding
1. Appropriateness- categories should be
appropriate to research problem and objectives.
2. Exhaustiveness- there should be a class for
every data item. The researcher often uses
“other” option.
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13. 3. Mutually exclusivity- specific answers should be placed in
one and only one category.
Ex- In an occupation survey non mutually exclusive classification may
be-
a) Professional
b) Managerial
c) Sales
d) Clerical
e) Craft
f) Operative
g) Unemployed
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14. Coding close-ended questions
• Dichotomous or multiple choice questions have response
category.
• While coding such questions numerical codes are provided to
each response category.
Response
category
Codes Response
category
Codes
Yes 1 Male 1
Do not know 2 Female 2
No 3
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15. Coding open-ended questions
• Researcher should review each open question and
establish meaning full category .
Ex- How many cup of coffee/ tea you drink in a day?
If respondents
answered
Response category Code
More than 5 cups/day Heavy consumer 1
Between 2-5 cups/day Moderate consumer 2
Less than 2 cups/ day Light consumer 3
O cups/day Non consumer 4
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16. Tabulation
of
Data
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17. • “A table is a systematic arrangement of statistical data
in column and rows”.
• “Tabulation is a process where by raw data on
completed questionnaire are transformed in to the “list
of needed information”.
• The purpose of table is to simplify the presentation and
facilitate comparison.
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18. U.S. Auto Sales 2003 - 2007
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19. Significance of Tabulation
1. It simplifies the complex data
2. It facilitates comparison
3. It gives identity to the data
4. It reveals pattern
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20. Parts of Table
1. Table number
2. Title of table
3. Caption
4. Stub
5. Body of table
6. Head notes
7. Foot notes
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21. Stub heading
Caption
Column
heading
Column
heading
Column
heading
Column
heading
Stub entries
Stub entries
Stub entries
Body
Title of Table
Table number
Footnote
Head note
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22. Types of Tabulation
1. Uni-variate Tabulation
2. Bi-variate Tabulation or Multivariate tabulation
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23. Univariate Tabulation
• “Uni-variate tabulation counts one questions answer”
• Such a tabulation results in frequency distribution of
answers. As-
– No. of people who answered in first response category
– No. of people who answered in first response category. Etc.
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24. • What is your opinion regarding mandatory fitting of airbags,
GPRS system, & seat belts in all the vehicles in country.
– In favor of
– Indifferent towards
– Opposed to
Number Percent (%)
In favor of 55 33.4
Indifferent towards 31 19.4
Opposed to 74 46.2
Total 160 100(%)
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25. Approach B Approach C
Number
In favor of 55
Indifferent towards 31
Opposed to 74
Total 160
Percent
In favor of 34.4
Indifferent towards 19.4
Opposed to 46.2
Total 100 %
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26. Bi-variate Tabulation
or
Multivariate tabulation
• In Bi-variate Tabulation or Multivariate tabulation the
researcher simultaneously tabulate the responses of
two or more questions.
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27. EXAMPLE
1. What is your gender?
a) Male
b) Female
2. How often you use credit cards when purchasing PIZZA at Dominos.
a) Regularly
b) Occasionally
c) Never
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28. Uses of credit cards for purchase of Dominos Pizza
Usages rate Male Female
Number Percent (%) Number Percent (%)
Regularly 20 10 100 50
Occasionally 60 30 80 40
Never 120 60 20 10
Total 200 100% 200 100%
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29. General rules
for
tabulation
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30. • There are no hard and fast rules for preparing a statistical table.
• “In collection and tabulation, common sense is the chief requisite
and experience is the chief teacher.” - Prof. Bowley
• However, the following points should be borne in mind while
preparing a table-
1. Table must contain all the essential parts, such as, table number,
title, head note, caption, etc .
2. Table should be simple to understand.
3. It should also be compact, complete and self-explanatory.
4. Table should be of proper size
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31. 5. Indicate a zero quantity by a zero and do not use zero to indicate such
information which is not available.
6. In case of non-availability of information, one should write N.A. or
indicate it by dash (-).
7. Ditto marks (,,) should be avoided in a table. Similarly the expression
‘etc’ should not be used in a table.
8. Table should not be overloaded with details.
9. Abbreviations should be avoided, particularly in titles and sub-titles
10. In all tables the captions and stubs should be arranged in some
systematic manner. (The manner of presentation may be alphabetically,
or chronologically depending upon the requirement).
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32. 11. The unit of measurement should be mentioned in the head
note.
12. The figures should be rounded off to the nearest hundred, or
thousand or lakh.
13. There should be a proper title to each table. It should tell
what exactly the table presents
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33. Cross-
Tabulation
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34. • “Cross tabulation (or crosstabs) is a statistical process that
summarizes categorical data to create a contingency table”.
• “Cross tabulation is a technique for comparing data from two
or more categorical variables”.
• Cross tabulation provide a basic picture of the interrelation
between two variables and can help find interactions between
them.
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35. • While a frequency distribution describes one variable at a
time, a cross-tabulation describes two or more variables
simultaneously.
• It helps us to understand how one variable (such as brand) is
related to another variable (gender). Example- answer to
following questions can be determined by cross tabulation.
1. How many brand loyal users are male.
2. Is familiarity with new product related to age and education level.
3. Is product use (heavy user, medium users, light users, and non users) related
to interest in outdoor activities (high, medium and low).
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36. Significance of Cross tabulation
1. A cross-tabulation gives you a basic picture of how two
variables inter-relate.
2. It can be easily interpreted and understood by managers who
are statistically oriented.
3. A series of cross tabulation can provide greater insights into a
complex phenomenon. Etc.
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37. Example- Gender and Internet Usage
• Suppose we are interested in determining whether internet
usage is related to gender?
• For the purpose of cross-tabulation, respondents can be
classified as-
– Male and female.
– light users (whose reported use is less than 5 hrs.) & heavy users
(whose reported use is more than 5 hrs.)
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Gender & Internet usage
Row
Internet Usage Male Female Total
Light (1) 5 10 15
Heavy (2) 10 5 15
Column Total 15 1 5

39. Types of Cross tabulation
• Cross tabulation can be-
1. Cross tabulation with Two variable or Bi variate
cross tabulation.
2. Cross-Tabulation with Three Variables
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40. Two Variables Cross-Tabulation
• Since two variables have been cross-classified, percentages could be
computed either
– Column wise, based on column totals, or
– Row wise, based on row totals.
• The general rule is to compute the percentages in the direction of
the independent variable, across the dependent variable.
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41. Internet Usage by Gender: Percentage
calculation column wise
Gender
Internet Usage Male Female
Light 33.3% 66.7%
Heavy 66.7% 33.3%
Column total 100% 100%
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42. Gender by Internet Usage:
Percentage calculation row wise
Internet Usage
Gender Light Heavy Total
Male 33.3% 66.7% 100.0%
Female 66.7% 33.3% 100.0%
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