1. Chapter Fifteen
Chapter 15

2. OPENING QUESTIONS
What is the nature and scope of data preparation, and how can the data preparation process be described?
What is involved in questionnaire checking and editing?
How should questionnaires be coded to prepare the data for analysis?
What methods are available for cleaning the data and treating missing responses?
Which ethical issues are important in data preparation and analysis?

3. Relationship to Marketing
Figure Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process
Focus of This
Chapter
Relationship to
Previous Chapters
Relationship to Marketing
Research Process
Preparing Data for Analysis
Marketing Research Process (Chapter 1)
Research Design Components (Chapter 3)
Problem Definition
Approach to Problem
Figure Relationship to the Previous Chapters & The Marketing Research Process
Research Design
Field Work
Data Preparation
and Analysis
Report Preparation
and Presentation

4. Figure 15.2 Data Preparation: An Overview
Opening Vignette
The Data Preparation Process
Fig 15.3
Questionnaire Checking and Editing
Fig 15.4
Coding
What Would You Do?
Be a DM! Be an MR! Experiential Learning
Fig 15.5
Transcribing
Fig 15.6
Data Cleaning
Selecting Data Analysis Strategy
Fig 15.7
Application to Contemporary Issues
International
Technology
Ethics

5. Figure 15.3 Data Preparation Process
Preliminary Plan of Data Analysis
Figure Data Preparation Process
Questionnaire Checking
Editing
Coding
Transcribing
Data Cleaning
Selecting a Data Analysis Strategy

6. Questionnaire Checking
A questionnaire returned from the field may be unacceptable for several reasons.
Parts of the questionnaire may be incomplete.
The pattern of responses may indicate that the respondent did not understand or follow the instructions.
The responses show little variance.
One or more pages are missing.
The questionnaire is received after the preestablished cutoff date.
The questionnaire is answered by someone who does not qualify for participation.

7. Figure 15.4 Treatment of Unsatisfactory Responses
Return to the
Field
Assign Missing
Values
Discard
Unsatisfactory
Respondents
Substitute a
Neutral Value
Casewise
Deletion
Pairwise
Deletion

8. Coding Guidelines for coding unstructured questions:
Category codes should be mutually exclusive and collectively exhaustive.
Only a few (10% or less) of the responses should fall into the “other” category.
Category codes should be assigned for critical issues even if no one has mentioned them.
Data should be coded to retain as much detail as possible.

9. Codebook
A codebook contains coding instructions and the necessary information about variables in the data set. A codebook generally contains the following information:
column number
record number
variable number
variable name
question number
instructions for coding

10. Figure 15.5 A Codebook Excerpt
Column
Number
1-3 4
5-6
7-8
9-14
15-20
21-22
23-24 25 26 27 28 35
Variable
Number 1 2 3 4 5 6 7 8 9 10 11 18
Variable
Name
Respondent ID
Record Number
Project Code
Interview Code
date Code
Time Code
Validation Code
Blank
Who shops
Familiarity with store 1
Familiarity with store 2
Familiarity with store 3
Familiarity with store 10
Question
Number I
IIa
IIb
IIc
IIj
Coding
Instructions
001 to 890 add leading zeros as
necessary
1 (same for all respondents)
31 (same for all respondents)
As coded on the questionnaire
Leave these columns blank
Male head =1
Female head =2
Other =3
Punch the number circled
Missing values =9
For question II parts a through j
Not so familiar =1
Very familiar =6
Missing Values =9

11. Figure 15.6 Data Transcription
Raw Data
Figure Data Transcription
CATI/
CAPI
Key Punching via
CRT Terminal
Mark Sense
Forms
Optical
Scanning
Computerized
Sensory
Analysis
Verification: Correct
Key Punching Errors
Computer
Memory
Disks
Magnetic
Tapes
Transcribed Data

12. Figure 15.7 Selecting A Data Analysis Strategy
Earlier Steps (1, 2, 3) of the Marketing Research Process
Figure Selecting A Data Analysis Strategy
Known Characteristics of Data
Properties of Statistical Techniques
Background & Philosophy of the Researcher
Data Analysis Strategy