Figure 15.1 Relationship of Data Preparation to the
Previous Chapters and the Marketing Research Process
Focus of This
Chapter
• Preparing Data
for Analysis
Relationship to
Previous Chapters
• Marketing
Research
Process
(Chapter 1)
• Research
Design
Components
(Chapter 3)
Relationship to Marketing
Research Process
Problem Definition
Approach to Problem
Research Design
Field Work
Data Preparation
and Analysis
Report Preparation
and Presentation
Data Preparation: An Overview
Opening Vignette
The Data Preparation Process
What Would You Do?
Experiential Learning
Figure 15.2
Fig 15.3
Questionnaire Checking and Editing
Fig 15.4
Coding
Be a DM!
Be an MR!
Fig 15.5
Transcribing
Fig 15.6
Data Cleaning
Selecting Data Analysis Strategy
Fig
15.7
Application to Contemporary Issues
International
Technology
Ethics
Figure 15.3
Data Preparation Process
Preliminary Plan of Data Analysis
Questionnaire Checking
Editing
Coding
Transcribing
Data Cleaning
Selecting a Data Analysis Strategy
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.
Figure 15.4 Treatment of Unsatisfactory Responses
Treatment of
Unsatisfactory
Responses
Return to the
Field
Assign Missing
Values
Substitute a
Neutral Value
Casewise
Deletion
Discard
Unsatisfactory
Respondents
Pairwise
Deletion
Editing
Treatment of Unsatisfactory Results
– Returning to the Field – The questionnaires with
unsatisfactory responses may be returned to the field,
where the interviewers recontact the respondents.
– Assigning Missing Values – If returning the
questionnaires to the field is not feasible, the editor
may assign missing values to unsatisfactory
responses.
– Discarding Unsatisfactory Respondents – In this
approach, the respondents with unsatisfactory
responses are simply discarded.
Coding
Coding means assigning a code, usually a number, to each
possible response to each question. The code includes an
indication of the column position (field) and data record it will
occupy.
Coding Questions
Fixed field codes, which mean that the number of records for
each respondent is the same and the same data appear in the
same column(s) for all respondents, are highly desirable.
– If possible, standard codes should be used for missing data.
Coding of structured questions is relatively simple, since the
response options are predetermined.
– In questions that permit a large number of responses, each
possible response option should be assigned a separate
column.
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.
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
Coding Questionnaires
• The respondent code and the record number appear
on each record in the data.
• The first record contains the additional codes: project
code, interviewer code, date and time codes, and
validation code.
• It is a good practice to insert blanks between parts.
TABLE 15.1 Illustrative Computer File: Department Store Patronage Project
FIELDS
COLUMN NUMBERS________________________________
RESPONDENT
1-3
4
5-6
7-8...........
26..........35
77
1
001
1
31
01
6544234553
5
2
002
1
31
01
5564435433
4
3
003
1
31
01
4655243324
4
4
004
1
31
01
5463244645
6
31
55
6652354435
5
Record #271
271
1
Figure 15.5 A Codebook Excerpt
Column
Number
1-3
Variable
Number
1
4
5-6
7-8
9-14
15-20
21-22
23-24
25
2
3
4
5
6
7
26
9
27
10
28
35
11
18
8
Variable
Name
Respondent ID
Question Coding
Number Instructions
001 to 890 add leading zeros as
necessary
Record Number
1 (same for all respondents)
Project Code
31 (same for all respondents)
Interview Code
As coded on the questionnaire
date Code
As coded on the questionnaire
Time Code
As coded on the questionnaire
Validation Code
As coded on the questionnaire
Blank
Leave these columns blank
Who shops
I
Male head
=1
Female head
=2
Other
=3
Punch the number circled
Missing values
=9
Familiarity with store 1 IIa
For question II parts a through j
Punch the number circled
Familiarity with store 2 IIb
Not so familiar
=1
Very familiar
=6
Missing Values
=9
Familiarity with store 3 IIc
Familiarity with store 10 IIj
Figure 15.6 Data Transcription
Raw Data
CATI/
CAPI
Key Punching via
CRT Terminal
Mark Sense
Forms
Optical
Scanning
Computerized
Sensory
Analysis
Verification: Correct
Key Punching Errors
Computer
Memory
Disks
Transcribed Data
Magnetic
Tapes
Figure 15.7 Selecting a Data Analysis Strategy
Earlier Steps (1, 2, 3) of the Marketing Research Process
Known Characteristics of Data
Properties of Statistical Techniques
Background & Philosophy of the Researcher
Data Analysis Strategy
SPSS Windows
• Using the Base module, out-of-range values can be selected using
the SELECT IF command. These cases, with the identifying
information (subject ID, record number, variable name, and variable
value) can then be printed using the LIST or PRINT commands. The
Print command will save active cases to an external file. If a
formatted list is required, the SUMMARIZE command can be used.
• SPSS Data Entry can facilitate data preparation. You can verify that
respondents have answered completely by setting rules. These
rules can be used on existing datasets to validate and check the
data, whether or not the questionnaire used to collect the data was
constructed in Data Entry. Data Entry allows you to control and
check the entry of data through three types of rules: validation,
checking, and skip and fill rules.
• While the missing values can be treated within the context of the
Base module, SPSS Missing Values Analysis can assist in
diagnosing missing values and replacing missing values with
estimates.
• TextSmart by SPSS can help in the coding and analysis of open
ended responses.