2. Topics to be covered
Data Analysis : Editing, Coding,
Classification, Tabulation, Analysis
and Interpretation
3. Difference between Data and
Information
Any raw facts or figures is known as
data.
When the data is processed by doing
statistical analysis and some
conclusion can be drawn from it, it is
known as information.
4. Steps in Processing of Data
Questionnaire
checking
Editing
Coding
Tabulation
Data Cleaning
Statistically adjusting
the data
Selecting a Data
Analysis Strategy
5. Questionnaire checking – The initial step in
questionnaire checking involves a check of all
questionnaires for completeness and
interviewing quality. A questionnaire returned
from the field may be unacceptable for
several reasons:
1. Part of the questionnaire may be
incomplete.
2. The pattern of responses may indicate that
the respondent did not understand or follow
the instructions.
3. The responses show little variance.
4. The questionnaire is answered by someone
who does not qualify for participation.
5. The returned questionnaire is physically
incomplete, one or more pages are missing.
6. Editing – Review of the questionnaires with the objective
of increasing accuracy and precision. It consists of
screening questionnaires to identify illegible, incomplete,
inconsistent or ambiguous responses. This can be done
in two stages:
a) Field Editing – Objective of field editing is to make sure
that proper procedure is followed in selecting the
respondent, interview them and record their responses.
The main problems faced in field editing are:
1. Inappropriate Respondents – Instead of house owners,
tenant is interviewed.
2. Incomplete interviews, 3. Improper understanding, 4.
Lack of consistency, 5. Legibility, 6, Fictitious interview –
Questionnaires are filled by interviewer himself without
conducting the interview.
b) Office Editing – It is more thorough than field editing.
Problems of consistency, rapport with respondents are
some of the issues which get highlighted during office
editing.
7. Example of Inconsistency:
A respondent indicated that he doesn’t drink coffee, but
when questioned about his favorite brand, he replied
‘BRU’.
Treatment of Unsatisfactory Responses
Returning to the field – Questionnaires with
unsatisfactory responses may be returned to the
field, where the interviewers recontact the
respondents.
Assigning missing value – Editor may assign missing
values to unsatisfactory responses. This approach
may be desirable if 1) the number of respondents
with unsatisfactory responses is small, 2) the
proportion of unsatisfactory responses for each of
these respondents is small, or 3) the variables with
unsatisfactory responses are not the key variables.
Discarding unsatisfactory respondents – This is
possible only when proportion of unsatisfactory
respondents is small or the sample size is large.
8. Coding – Coding refers to those activities which helps in
transforming edited questionnaires into a form that is
ready for analysis. Coding speeds up the tabulation while
editing eliminates errors. Coding involves assigning
numbers or other symbols to answers so that the
responses can be grouped into limited number of classes
or categories. The code includes an indication of the
column and data record it will occupy. For eg. Sex of
respondents may be coded as 1for males and 2 for
females.
Questions Answers Codes
1. Do you own
a vehicle?
Yes 1
No 2
2. What is your
occupation?
Salaried S
Business B
Retired R
9. Tabulation – Refers to counting the number of
cases that fall into various categories. The
results are summarized in the form of statistical
tables. The raw data is divided into groups and
sub-groups. The counting and placing of data in
a particular group and sub-group are done. The
tabulation involves:
1. Sorting and counting.
2. Summarising of data.
Tabulation may be of two types:
1. Simple tabulation – In simple tabulation, a
single variable is counted.
2. Cross tabulation – Includes two or more
variables, which are treated simultaneously.
Tabulation can be done entirely by hand, or by
machine, or by both hand and machine.
10. Sorting and counting of data: Sorting can be
done as follows:
Format of a Blank table
Table No.
TITLE – Number of children per family
Head Note – Unit of measurement
Income (Rs) Tally Marks Frequencies
1000 IIII 4
1500 II 2
2000 III 3
Sub-
Headin
g
Caption
Body
Foot note
Total
Sub heading
indicates the row
title or the row
headings.
Caption
indicates what
each column is
meant for.
Body of the table
gives full
information of
11. Kinds of Tabulation
1. Simple or one-way tabulation – The multiple
choice questions which allow only one answer
may use on-way tabulation or univariate. The
questions are predetermined and consist of
counting the number of responses falling into a
particular category and calculate the percentage.
Example
Table 14.1: Study of number of children in a family
No. of children Family Percentage
0 10 5
1 30 15
2 70 35
12. 2. Cross Tabulation or Two-way Tabulation –
This is known as Bivariate Tabulation.The
data may include two or more variables.
Eg. Popularity of a health drink among
families having different incomes.
Table 14.3: Use of Health Drink
Income per
month
No. of
children
per family
(0)
1 2 No. of
families
1000 10 5 8 23
1001-2000 5 0 8 13
2001-3000 20 10 12 42
13. Data cleaning – Includes consistency
checks and treatment of missing responses.
Although preliminary consistency checks
have been made during editing, the checks
at this stage are more thorough and
extensive, because they are made by
computer.
Consistency checks – Identify data that are
out of range, logically inconsistent or have
extreme values. For eg. A respondent may
indicate that she charges long distance calls
to a calling card, although she does not have
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14. Treatment of missing responses – Missing responses
represent values of a variable that are unknown, either
because respondents provided ambiguous answers or
their answers were not properly recorded.
1. Substitute a Neutral Value – A neutral value, typically
the mean to the variable, is substituted for the missing
responses.
2. Substitute an Imputed Response – The respondent’s
pattern of responses to other questions are used to
impute or calculate a suitable response to the missing
questions.
3. Casewise Deletion – Cases or respondents with any
missing responses are discarded from the analysis.
4. Pairwise deletion – Instead of discarding all cases with
any missing values, the researcher uses only the cases
or respondents with complete responses for each
calculation. As a result, different calculations in an
analysis may be based on different sample sizes.
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15. Statistically Adjusting the Data – If any
correction needs to be done for the
statistical analysis, the data is adjusted
accordingly.
Selecting a Data Analysis Strategy – The
selection of a data analysis strategy should
be based on the earlier steps of the
marketing research process, known
characteristics of the data, properties of
statistical techniques and the background
and philosophy of the researcher.
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