Chapter 15
Chapter 15
Data Preparation
Data Preparation
and
and
Description
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
Learning Objectives
Learning Objectives
Understand . . .
•
Problems with and solutions for “don’t know”
responses and handling missing data.
Goal of Data Decription
Goal of Data Decription
“
The goal is to transform data into
information, and information into insight.
Carly Fiorina
PulsePoint:
PulsePoint:
Research Revelation
Research Revelation
Data Preparation
Data Preparation
in the Research Process
Monitoring
Monitoring
Online Survey Data
Online Survey Data
Editing
Uniformly
entered
Uniformly
entered
Arranged for
simplification
Arranged for
simplification
Complete
Complete
Accurate
Field Editing
Field Editing
Speed without accuracy won’t
help the manager choose the
right direction.
•
Field editing review
•
Entry gaps identified
•
Callbacks made
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
Sample Codebook
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
Content Analysis
Content Analysis
Content Analysis
Types of Content Analysis
Types of Content Analysis
Syntactical
Propositional
Referential
Open-Question Coding
Open-Question Coding
Locus of
Responsibility Mentioned
Mentioned
Not
A. Company ________________________ ________________________ B. Customer ________________________ ________________________ C. Joint
Company-Customer
_____________ ___________
______________ __________ F. Other
_____________ ___________
______________ __________
Locus of
Responsibility
Frequency (
100)
n
=
A. Management1. 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
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
Data Entry
Missing Data
Missing Data
Listwise Deletion
Pairwise Deletion
Key Terms
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
Appendix 15a
Appendix 15a
Describing Data
Describing Data
Statistically
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
Frequencies
Frequencies
Unit Sales
Increase
(%)
Frequency
Percentage
Cumulative
Percentage
5Unit Sales
Increase
(%)
Frequency
Percentage
Cumulative
Percentage
Origin, foreign(1) Origin, foreign
