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16-1
Chapter 16Chapter 16
Data Data PreparationPreparation
andand
DescriptionDescription
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
CriteriaCriteria
Consistent with the intent of Ques
Consistent with the intent of Ques
Uniformly entered
Uniformly entered
Arranged forsimplification
Arranged forsimplification
CompleteComplete
AccurateAccurate
16-6
Field Editing
• Field editing review• Abbreviations to be
clarified• Re interview some• 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
Central Editing
• Incorrect entries are corrected
• In appropriate or missing replies
• Armchair Interviewing can be detected
16-9
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
16-10
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
16-11
Coding Closed Questions
• Easier to code, record and analyse
• Precoding
16-12
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
16-13
16-14
Exhibit 16-2 Sample Codebook
16-15
Exhibit 16-3 Precoding
16-16
Exhibit 16-3 Coding Open-Ended Questions
16-17
Coding Rules
Categories should be
Categories should be
Appropriate to the research problem
Exhaustive
Mutually exclusiveDerived from one
classification principle
16-18
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
16-19
Coding Rules
• Mutual Exclusivity– Example of profession– Add more field of second profession
• Derived from one classification principle– Manager and Unemployed
16-20
Content Analysis
QSR’s XSight software for
content analysis.
16-21
Types of Content Analysis
Syntactical
Propositional
Referential
Thematic
16-22
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
16-23
Content Analysis
• Propositional units– Are assertions about an object, event, person
and so on
• Thematic – Past, present or the future in responses
16-24
Example
• How can company – customer relations be improved ?
16-25
Exhibit 16-4 & 16-5Open-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
16-26
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
16-27
Exhbit 16-7 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 = 650
100%n = 150
100%n = 200
16-28
Data Entry
Database Programs
Database Programs
Optical Recognition
Optical Recognition
Digital/Barcodes
Digital/Barcodes
Voicerecognition
Voicerecognition
KeyboardingKeyboarding
16-29
Missing Data
Listwise Deletion
Pairwise Deletion
Replacement
16-30
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
16-31
Appendix 16aAppendix 16a
Describing Data Describing Data StatisticallyStatistically
16-32
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
16-33
Distributions
16-34
Characteristics of Distributions
16-35
Measures of Central Tendency
Mean ModeMedian
16-36
Measures of Variability
Interquartile range
Interquartile range
Quartile deviationQuartile deviation
DispersionDispersion
Range
Standard deviationStandard deviation
VarianceVariance
16-37
Summarizing Distributions with Shape
16-38
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
16-39
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