mcgraw-hill/irwin business research methods, 10ecopyright © 2008 by the mcgraw-hill companies, inc....
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McGraw-Hill/IrwinBusiness Research Methods, 10e
Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.
Chapter 15Chapter 15
Data Data PreparationPreparation
andand
DescriptionDescription
15-2
Learning Objectives
Understand . . .
• The 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.
• The use of content analysis to interpret and summarize open questions.
15-3
Learning Objectives
Understand . . .
• Problems with and solutions for “don’t know” responses and handling missing data.
• The options for data entry and manipulation.
15-4
PulsePoint: Research Revelation
68 The percent of online consumers who put trust in online consumer recommendations.
15-5
Research Adjusts for Imperfect Data
“In the future, we’ll stop moaning about the lack of perfect data and start using the good data with much more advanced analytics and data-matching techniques.”
Kate Lynch, research directorLeo Burnett’s Starcom Media Unit
15-6
Data Preparation in the Research Process
15-7
Monitoring Online Survey Data
Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .
15-8
Editing
CriteriaCriteria
ConsistentConsistent
Uniformly entered
Uniformly entered
Arranged forsimplification
Arranged forsimplification
CompleteComplete
AccurateAccurate
15-9
Field Editing
• Field editing review• Entry gaps identified• Callbacks made• Validate results
Ad message: Speed without accuracy won’t help the manager choose the right direction.
15-10
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
15-11
Sample Codebook
15-12
Precoding
15-13
Coding Open-Ended Questions
15-14
Coding Rules
Categories should be
Categories should be
Appropriate to the research problem
Exhaustive
Mutually exclusiveDerived from one
classification principle
15-15
Content Analysis
QSR’s XSight software for
content analysis.
15-16
Content Analysis
15-17
Types of Content Analysis
Syntactical
Propositional
Referential
Thematic
15-18
Open-Question Coding
Locus of Responsibility Mentioned Not Mentioned
A. Company ________________________ ________________________
B. Customer ________________________ ________________________
C. Joint Company-Customer ________________________ ________________________
F. Other ________________________ ________________________
Locus of Responsibility Frequency (n = 100)
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
10
20
7
3
15
12
8
5
20
15-19
Handling “Don’t Know” Responses
Question: Do you have a productive relationship with your present salesperson?
Years of Purchasing Yes No Don’t Know
Less than 1 year 10% 40% 38%
1 – 3 years 30 30 32
4 years or more 60 30 30
Total100%
n = 650100%
n = 150100%
n = 200
15-20
Data Entry
Database Programs
Database Programs
Optical Recognition
Optical Recognition
Digital/Barcodes
Digital/Barcodes
Voicerecognition
Voicerecognition
KeyboardingKeyboarding
15-21
Missing Data
Listwise Deletion
Pairwise Deletion
Replacement
15-22
Key Terms
• Bar code• Codebook• Coding• Content analysis• Data entry• Data field• Data file• Data preparation• Data record• Database
• Don’t know response • Editing• Missing data• Optical character
recognition• Optical mark recognition• Precoding• Spreadsheet• Voice recognition
McGraw-Hill/IrwinBusiness Research Methods, 10e
Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.
Appendix 15aAppendix 15a
Describing Data Describing Data StatisticallyStatistically
15-24
Frequencies
Unit Sales Increase (%) Frequency Percentage
Cumulative Percentage
5
6
7
8
9
Total
1
2
3
2
1
9
11.1
22.2
33.3
22.2
11.1
100.0
11.1
33.3
66.7
88.9
100
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
A
B
15-25
Distributions
15-26
Characteristics of Distributions
15-27
Measures of Central Tendency
Mean ModeMedian
15-28
Measures of Variability
Interquartile range
Interquartile range
Quartile deviationQuartile deviation
Range
Standard deviationStandard deviation
VarianceVariance
15-29
Summarizing Distribution Shape
15-30
Variable 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
__
_
Symbols
15-31
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