1. Data Preparation and Description
Chapter 15
Data Preparation
and
Description
McGraw-Hill/Irwin
Business Research Methods, 10e
Copyright © 2008 by The McGraw-Hill Companies, Inc. All Rights Reserved.

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.

3. Learning Objectives Understand . . .
Problems with and solutions for “don’t know” responses and handling missing data.
The options for data entry and manipulation.

4. PulsePoint: Research Revelation68
The percent of online consumers who put trust in online consumer recommendations.

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 director
Leo Burnett’s Starcom Media Unit

6. Data Preparation in the Research Process

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 .

8. Editing Accurate Consistent Criteria Arranged for simplification
Uniformly
entered
Complete

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.

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

11. Sample Codebook

12. Precoding

13. Coding Open-Ended Questions

14. Coding Rules Appropriate to the research problem Exhaustive Categories
should be
Mutually exclusive
Derived from one classification principle

15. QSR’s XSight software for content analysis.

16. Content Analysis

17. Types of Content Analysis
Syntactical
Referential
Propositional
Thematic

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

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 32
4 years or more 60
Total
100% n = 650
100% n = 150
100% n = 200

20. Data Entry Keyboarding Database Programs Digital/ Barcodes Optical
Recognition
Voice
recognition

21. Missing Data
Listwise Deletion
Pairwise Deletion
Replacement

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

23. Describing Data Statistically
Appendix 15a
Describing Data Statistically

24. Cumulative Percentage Cumulative Percentage
Frequencies A
Unit Sales Increase (%)
Frequency
Percentage
Cumulative Percentage 5 6 7 8 9
Total 1 2 3
11.1
22.2
33.3
100.0
66.7
88.9
100 B
Unit Sales Increase (%)
Frequency
Percentage
Cumulative Percentage
Origin, foreign (1) 6 7 8 1 2
11.1
22.2
33.3
55.5
Origin, foreign (2) 5 9
Total
100.0
66.6
77.7
88.8

25. Distributions

26. Characteristics of Distributions

27. Measures of Central Tendency
Mean
Median
Mode

28. Measures of Variability
Variance
Quartile deviation
Standard deviation
Interquartile range
Range

29. Summarizing Distribution Shape

30. Symbols _ _ _

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