16-1
Chapter 16
Data
Preparation
and
Description
McGraw-Hill/Irwin
© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
16-2
Learning Objectives
Understand . . .
• importance of editing the collected raw data
to detect errors and omissions
• how coding is used to assign number and
other symbols to answers and to categorize
responses
• use of content analysis to interpret and
summarize open questions
16-3
Learning Objectives
Understand . . .
• problems and solutions for “don’t know”
responses and handling missing data
• options for data entry and manipulation
16-4
Exhibit 16-1 Data Preparation
in the Research Process
16-5
Editing
Accurate
Arranged for
simplification
Consistent
Criteria
Complete
Uniformly
entered
16-6
Field Editing
•
•
•
•
Field editing review
Entry gaps identified
Callbacks made
Validate results
16-7
Central Editing
Be familiar with instructions
given to interviewers and coders
Do not destroy the original entry
Make all editing entries identifiable and in
standardized form
Initial all answers changed or supplied
Place initials and date of editing
on each instrument completed
16-8
Exhibit 16-2
Sample Codebook
16-9
Exhibit 16-3 Precoding
16-10
Exhibit 16-3 Coding OpenEnded Questions
16-11
Coding Rules
Appropriate to the
research problem
Exhaustive
Categories
should be
Mutually exclusive
Derived from one
classification principle
16-12
Content Analysis
QSR’s XSight
software for
content analysis.
16-13
Types of Content Analysis
Syntactical
Referential
Propositional
Thematic
16-14
Exhibit 16-4 & 16-5
Open-Question Coding
Locus of
Responsibility
Mentioned
Not Mentioned
A. Company
________________________
________________________
B. Customer
________________________
________________________
C. Joint Company-Customer
________________________
________________________
F. Other
________________________
________________________
Locus of Responsibility
A. Management
1. Sales manager
2. Sales process
3. Other
4. No action area identified
B. Management
1. Training
C. Customer
1. Buying processes
2. Other
3. No action area identified
D. Environmental conditions
E. Technology
F. Other
Frequency (n = 100)
10
20
7
3
15
12
8
5
20
16-15
Exhbit 16-7 Handling
“Don’t Know” Responses
Question: Do you have a productive relationship
with your present salesperson?
No
Don’t Know
10%
40%
38%
1 – 3 years
30
30
32
4 years or more
60
30
30
100%
n = 650
100%
n = 150
100%
n = 200
Years of Purchasing
Less than 1 year
Total
Yes
16-16
Data Entry
Keyboarding
Digital/
Barcodes
Database
Programs
Optical
Recognition
Voice
recognition
16-17
Missing Data
Listwise Deletion
Pairwise Deletion
Replacement
16-18
Key Terms
•
•
•
•
•
•
•
•
•
•
Bar code
Codebook
Coding
Content analysis
Data entry
Data field
Data file
Data preparation
Database
Don’t know response
• Editing
• Missing data
• Optical character
recognition
• Optical mark recognition
• Precoding
• Record
• Spreadsheet
• Voice recognition
16-19
Appendix 16a
Describing Data
Statistically
McGraw-Hill/Irwin
© 2006 The McGraw-Hill Companies, Inc., All Rights Reserved.
16-20
Frequencies
A
Unit Sales
Increase (%)
5
6
7
8
9
Total
Frequency
Percentage
1
2
3
2
1
9
11.1
22.2
33.3
22.2
11.1
100.0
Cumulative
Percentage
11.1
33.3
66.7
88.9
100
B
Unit Sales
Increase (%)
Frequency
Percentage
Cumulative
Percentage
Origin, foreign (1)
6
7
8
1
2
2
11.1
22.2
22.2
11.1
33.3
55.5
Origin, foreign (2)
5
6
7
9
Total
1
1
1
1
9
11.1
11.1
11.1
11.1
100.0
66.6
77.7
88.8
100.0
16-21
Distributions
16-22
Characteristics of
Distributions
16-23
Measures of
Central Tendency
Mean
Median
Mode
16-24
Measures of Variability
Variance
Quartile
deviation
Standard
deviation
Dispersion
Interquartile
range
Range
16-25
Summarizing
Distributions with Shape
16-26
Symbols
Population
Sample
Mean
µ
X
Proportion

p
Variance
2
s2
Standard deviation

s
Size
N
n
Standard error of the mean
x
_
Sx
Standard error of the proportion
p
Sp
_
Variable
_
16-27
Key Terms
•
•
•
•
•
Central tendency
Descriptive statistics
Deviation scores
Frequency distribution
Interquartile range
(IQR)
• Kurtosis
• Median
• Mode
•
•
•
•
•
Normal distribution
Quartile deviation (Q)
Skewness
Standard deviation
Standard normal
distribution
• Standard score (Z score)
• Variability
• Variance