This document discusses various aspects of data processing and analysis in marketing research. It covers topics like data editing, coding, classification, tabulation and exploratory data analysis. Various statistical techniques for univariate, bivariate and multivariate analysis of data are also summarized. These include techniques like frequency analysis, t-tests, chi-square, ANOVA etc. for metric and non-metric data.

1. BBA Vth SEM.
MARKETING RESEARCH
TOPIC: DATAPROCESSING
AND ANALYSIS
Pooja Luniya (Asst. Prof.)
GD Rungta College of Science & Technology

2. Data Processing
• Processing implies editing, coding, classification and tabulation of
collected data so that they are amenable to analysis.
DATA EDITING
DATA TABULATION
DATA CLASSIFICATION
DATA CODING
EXPLORATORY DATA ANALYSIS
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Pooja Luniya (Asst. Prof)

3. Data Editing:
• Editing of data is a process of examining the collected raw data
(specially in surveys) to detect errors and omissions and to correct
these when possible.
• Field Editing
• At the time of recording the respondent’s responses
• Central Editing
• Correction of obvious errors in the office
3Pooja Luniya (Asst. Prof)

4. Data Coding
• The process of identifying and denoting a numeral to the responses
given by the respondent is called coding
• Process of assigning numerals / symbols to answers to reduce the
responses into a limited number of categories or classes.
• In coding, each answer is identified and classified with a
numerical score or other symbolic characteristics for processing the
data in computers.
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Pooja Luniya (Asst. Prof)

5. Sample record: Excel sheet for two-
wheeler owners
Unit
Column 1
occupation
Column 2
Vehicle
Column 3 Km/day
Column 4 Marital status
Column 5
Family size
Column 6
1 4 1 20 1 3
2 3 2 25 2 1
3 5 1 25 1 4
4 2 1 15 2 2
5 4 2 20 2 4
6 5 2 35 2 6
7 1 1 40 1 3
8 5 2 20 2 4
5Pooja Luniya (Asst. Prof)

6. Pre-Coding closed-ended questions
Q.NO. Variable name Coding instructions Variable name
1. Balika Badhu Number from 1-10 X 10a
2. Sathiya Number from 1-10 X 10b
3. Sasural Genda Phool Number from 1-10 X 10c
4. Bidai Number from 1-10 X 10d
5. Pathshala Number from 1-10 X 10e
6. Bandini Number from 1-10 X 10f
7. Laptaganj Number from 1-10 X 10g
8. Sajan Ghar Jaaana Hai Number from 1-10 X 10h
9. Tere Liye Number from 1-10 X 10i
10. Uttaran Number from 1-10 X 10j
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Pooja Luniya (Asst. Prof)

7. Scaled questions
Col.no. Variable name Coding instructions Variable name
1. Individual shops more A number from 1 to 5
SA = 5, A = 4, N = 3, D =
2, SD = 1
X 1a
2. Well informed - do - X 1b
3. Knows what to buy - do - X 1c
4. More spending money - do - X 1d
5. More shopping options - do - X 1e
7Pooja Luniya (Asst. Prof)

8. Sample code book extract
Question
No.
Variable Name Coding Instruction
Symbol
used for
variable
name
1. Buy ready to eat food products
Yes = 1
No = 0
X1
2. Use ready to eat food products
Yes = 1
No = 0
X2
22. Age
Less than 20 yrs = 1,
21 to 26 years = 2,
27 to 35 years = 3,
36 to 45 years = 4,
More than 45 years = 5
X22
23. Gender
Male = 1
Female = 2
X23
24. Marital status
Single = 1
Married = 2
Divorced/widow = 3
X24
25. No. of children Exact no. to be written X25
26. Family size
One to two = 1,
Three to five = 2,
Six & more = 3
X26
27. Monthly household income
Rs.20000 to Rs.34999 = 1,
Rs.35000 to Rs.50000 = 2,
Rs.50001 to Rs.74999 = 3
Rs.75000 & above = 4
X27
28. Education
Less than graduation = 1
Graduation = 2
Post graduation & above = 3
X28
29. Occupation
Student = 1
Businessman = 2
Professional = 3
Service = 4
Housewife = 5
Others = 6
X29
8Pooja Luniya (Asst. Prof)

9. Data Classification
• Process of arranging data in groups or classes on the basis of
common characteristics
• Data with common characteristics are placed in one class
• Classification according to attributes
• Descriptive: Literacy, gender, Honesty, etc.
• Numerical: Weight, Height, Income, etc.
• Classification according to class intervals
• Intervals with frequency
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Pooja Luniya (Asst. Prof)

10. Data Tabulation
• Summarizing raw data and displaying in compact form
• Conserves space and reduces explanatory and descriptive
statement to a minimum
• Facilitates the process of comparison
• Facilitates summation of items and the detection of errors and
omission
• Provides a basis for various statistical computations
• Tabulation Methods
• Manual
• Electronic
• Simple Vs Complex Tabulation
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11. Data Analysis
• Exploratory data analysis
Sample characteristics: age group of the sample
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Pooja Luniya (Asst. Prof)

12. Exploratory data analysis
pie charts
46 & Above
41-45
36-40
31-35
26-30
20-25
Age Group
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13. Exploratory data analysis
bar charts
46 & Above41-4536-4031-3526-3020-25
Age Group
40
30
20
10
0
Frequency
Age Group
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Pooja Luniya (Asst. Prof)

14. Data Analysis
By and large statistical techniques for analysis can be placed in three
categories:
• Univariate Analysis – In univariate analysis, one variable is analysed
at a time.
• Bivariate Analysis – In bivariate analysis two variables are analysed
together and examined for any possible association between them.
• Multivariate Analysis – In multivariate analysis, the concern is to
analyse more than two variables at a time.
When the data are nominal or ordinal, non-parametric statistical
tests are used for data analyses, whereas when they are interval or
ratio parametric, statistical tests are used.
14Pooja Luniya (Asst. Prof)

15. UnivariateAnalysis: Classification
• Two-Group
• T-test
• z-test
• One-Way ANOVA
• Paired t-test
Univariate Techniques
Metric Data Nonmetric Data
One Sample
Two or
More Samples
One Sample
Two or
More Samples
• Frequency
• Chi-square
• K-S
• Runs
• Binomial
Independent Related Independent Related
• Chi-Square
• Mann-Whitney
• Median
• K-S
K-W ANOVA
• Sign
• Wilcoxon
• McNemar
• Chi-Square
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16. MultivariateAnalysis
Multivariate
Techniques
Dependence
Techniques
One Dependent
Variable
More than One
Dependent
Variable
Interdependence
Techniques
Variable
Interdependence
Interobject
Similarity
• Cross-tabulation
• ANOVA
• ANCOVA
• Multiple regression
• Two-group
Discriminant analysis
• Conjoint analysis
• MANOVA
• MANCOVA
• Canonical Correlation
• Multiple Discriminant
analysis
• Factor Analysis • Cluster Analysis
• Multidimensional
Scaling
16Pooja Luniya (Asst. Prof)