Week 14 data preparation n description
Chapter 15 Chapter 15 Data Preparation Data Preparation and and
Description Description
McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
Learning Objectives 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.
Learning Objectives Learning Objectives Understand . .
- Problems with and solutions for “don’t know” responses and handling missing data.
- The options for data entry and manipulation.
Goal of Data Decription Goal of Data Decription
“ The goal is to transform data into information, and information into insight.
Carly Fiorina former president and chairwoman, Hewlett-Packard Co
PulsePoint: PulsePoint: Research Revelation Research Revelation
The percent of white-collar workers who answer work-related calls or e- mail after work hours.
55
Data Preparation Data Preparation in the Research Process in the Research Process
Monitoring Monitoring Online Survey Data Online Survey Data
Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .
Editing Editing Accurate Consistent Accurate Consistent Criteria Criteria Arranged for
Uniformly Arranged for
entered simplification entered Complete Complete
Field Editing Field Editing
- Field editing review
- Entry gaps identified
- Callbacks made
- Validate results
Speed without accuracy won’t help the manager choose the right direction.
Central Editing 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
Sample Codebook Sample Codebook
Precoding Precoding
Coding Coding Open-Ended Questions Open-Ended Questions
6. What prompted you to purchase your most recent life insurance policy? _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________
Coding Rules Coding Rules Categories should be Categories should be
Appropriate to the research problem Exhaustive
Mutually exclusive Derived from one classification principle
Content Analysis Content Analysis QSR’s XSight software for content analysis.
Content Analysis Content Analysis
Types of Content Analysis Types of Content Analysis Syntactical Propositional Referential Thematic
Open-Question Coding Open-Question Coding
Locus of Frequency (n = Locus of Not
Responsibility 100)
Responsibility Mentioned Mentioned
A. Management _____________ ______________
1. Sales manager
10 A. Company ___________ __________
2. Sales process
20
3. Other
7 _____________ ______________
B. Customer ___________ __________
4. No action area
3 identified C. Joint Company- _____________ ______________
B. Management
15 Customer ___________ __________
1. Training _____________ ______________
C. Customer
12 F. Other ___________ __________
1. Buying processes
8
2. Other
5
3. No action area identified
20 D. Environmental conditions E. Technology
F. Other
Handling “Don’t Know” Handling “Don’t Know” Responses 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 Total 100%
n = 650
100%
n = 150
100%
n = 200
Data Entry Data Entry
Missing Data Missing Data Listwise Deletion Pairwise Deletion Replacement
Key Terms Key Terms
- Bar code
- Don’t know response >Codebook • Editing • Coding
- Missing data
- Content analysis
- Optical character
recognition
- Data entry
- Optical mark
- Data field
recognition
- Data file
- Precoding • Data preparation
- Spreadsheet • Data record
- Voice recognition
- Database
Appendix 15a Appendix 15a
Describing Data Describing Data
Statistically Statistically
McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
Research Adjusts for Imperfect Research Adjusts for Imperfect Data 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
Frequencies Frequencies
Unit Sales
A
Increase
Cumulative (%) Frequency Percentage Percentage
5
1
11.1
11.1
6
2
22.2
33.3
7
3
33.3
66.7
8
2
22.2
88.9 B
9
1 11.1 100
Unit Sales Total 9 100.0
Increase
Cumulative (%) Frequency Percentage Percentage
Origin, foreign
6
1
11.1
11.1 (1)
7
2
22.2
33.3
8
2
22.2
55.5 Origin, foreign
5
1
11.1
66.6 (2)
6
1
11.1
77.7
7
1
11.1
88.8
9
1 11.1 100.0
Total 9 100.0
Distributions Distributions
Characteristics of Distributions Characteristics of Distributions
Measures of Central Tendency Measures of Central Tendency
Mean Mode Median
Measures of Variability Measures of Variability
Interquartile range Interquartile range
Quartile deviation Quartile deviation
Range Standard deviation Standard deviation Variance
Variance
Summarizing Distribution Shape Summarizing Distribution Shape
_ _
_ Symbols Symbols
Key Terms Key Terms
- Central tendency
- Descriptive statistics
- Deviation scores
- Frequency distribution
- Interquartile range
(IQR)
distribution
- Standard score (Z score)
- Variability
- Variance<
- Kurtosis • Median • Mode
- Normal distribution
- Quartile deviation (Q)
- Skewness
- Standard deviation
- Standard normal