Transcript chapter16

16-1
Chapter 16
Data
Preparation
and
Description
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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
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Learning Objectives
Understand . . .
• problems and solutions for “don’t know”
responses and handling missing data
• options for data entry and manipulation
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Exhibit 16-1 Data Preparation
in the Research Process
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Editing
Accurate
Arranged for
simplification
Consistent with
the intent of Ques
Criteria
Complete
Uniformly
entered
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Field Editing
• Field editing review
• Abbreviations to be
clarified
• Re interview some
• Entry gaps identified
• Callbacks made
• Validate results
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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
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Central Editing
• Incorrect entries are corrected
• In appropriate or missing replies
• Armchair Interviewing can be detected
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Coding
• Coding involves assigning numbers or
other symbols to answers so that the
response can be grouped into a limited
number of categories
• Categorisation is the process of using
rules to partition a body of data
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Codebook Construction
• Code book or coding scheme contains
each variable in the study and specifies
the application of coding rules to the
variable
• Used to promote more accurate and more
efficient data entry
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Coding Closed Questions
• Easier to code, record and analyse
• Precoding
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Coding Free Response
• Open ended responses are used for
measuring sensitive or disapproved
behaviour, encourage natural modes of
expression
• Slows the analysis process and increases
the opportunity for error
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Exhibit 16-2
Sample Codebook
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Exhibit 16-3 Precoding
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Exhibit 16-3 Coding OpenEnded Questions
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Coding Rules
Appropriate to the
research problem
Exhaustive
Categories
should be
Mutually exclusive
Derived from one
classification principle
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Coding Rules
• Appropriateness
– Best partitioning of the data for testing
hypotheses and showing relationships
– Availability of comparison data
• Exhaustiveness
– Does the response cover all the possibilities
– Others may be classified further
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Coding Rules
• Mutual Exclusivity
– Example of profession
– Add more field of second profession
• Derived from one classification principle
– Manager and Unemployed
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Content Analysis
QSR’s XSight
software for
content analysis.
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Types of Content Analysis
Syntactical
Referential
Propositional
Thematic
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Content Analysis
• Syntactical
– Words, phrases, sentences, or paragraphs
– What is the meaning they reveal
• Referential
– Units described by words or phrases, they
may be objects, events, persons
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Content Analysis
• Propositional units
– Are assertions about an object, event, person
and so on
• Thematic
– Past, present or the future in responses
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Example
• How can company – customer relations be
improved ?
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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
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Don’t Know Responses
• Design better questions in the beginning
• Good rapport will ensure less DK
responses
– Who developed the Managerial grid concept
– Do you believe the current budget is good
– Do you like your present job
– How often each year you go to the movies
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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
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Data Entry
Keyboarding
Digital/
Barcodes
Database
Programs
Optical
Recognition
Voice
recognition
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Missing Data
Listwise Deletion
Pairwise Deletion
Replacement
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Key Terms
•
•
•
•
•
•
•
•
•
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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
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Appendix 16a
Describing Data
Statistically
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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
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Distributions
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Characteristics of
Distributions
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Measures of
Central Tendency
Mean
Median
Mode
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Measures of Variability
Variance
Quartile
deviation
Standard
deviation
Dispersion
Interquartile
range
Range
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Summarizing
Distributions with Shape
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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
_
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Key Terms
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•
•
•
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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