DATA PREPARATION AND DESCRIPTION

Chapter 15

McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

15-2

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.

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Learning Objectives

Understand . . .Problems with and solutions for “don’t

know” responses and handling missing data.

The options for data entry and manipulation.

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Pull Quote

“Pattern thinking, where you look at what’s working for someone else and apply it to your own situation, is one of the best ways to make big things happen for you and your team.”

David Novak, chairman and CEO,Yum! Brands, Inc.

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Data Preparation in the Research Process

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Monitoring Online Survey Data

Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .

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Editing

Criteria

Consistent

Uniformly entered

Arranged forsimplification

CompleteComplete

Accurate

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Field Editing

Field editing review Entry gaps identified Callbacks made Results validated

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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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Sample Codebook

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Precoding

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Coding Open-Ended Questions

6. What prompted you to purchase your most recent life insurance policy?

_______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________

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Coding Rules

Categories should be

Categories should be

Appropriate to the research problem

Exhaustive

Mutually exclusiveDerived from one

classification principle

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Content Analysis

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Types of Content Analysis

Syntactical

Propositional

Referential

Thematic

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Open-Question Coding Locus of

Responsibility Mentioned Not

Mentioned

A. Company ___________________________

_

B. Customer ___________________________

_

C. Joint Company-Customer _____________

_______________

F. Other ___________________________

_

Locus of ResponsibilityFrequency

(n = 100)

A. Management 1. Sales manager 2. Sales process 3. Other 4. No action area identifiedB. Management 1. Training C. Customer 1. Buying processes 2. Other 3. No action area identifiedD. Environmental conditionsE. TechnologyF. Other

102073

15

1285

20

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Proximity Plot

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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 = 650100%

n = 150100%

n = 200

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Data Entry

Database Programs

Optical Recognition

Digital/Barcodes

Voicerecognition

Keyboarding

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Missing Data Solutions

Listwise Deletion

Pairwise Deletion

Replacement

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Key Terms

• Bar code• Codebook• Coding• Content analysis• Data entry• Data field• Data file• Data preparation• Data record• Database

Don’t know response EditingMissing dataOptical character

recognitionOptical mark

recognitionPrecodingSpreadsheetVoice recognition

ADDITIONAL DISCUSSION OPPORTUNITIES

Chapter 15

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CloseUp: Dirty Data

Invalid: entry errors

Incomplete: missing, siloed, turf wars

Inconsistent: across databases

Incorrect: lost, falsified, outdated

Solutions: Data Steward, Data Protocols, Error Detection Software

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Snapshot: CBS labs

39 Million Visitors

Show Screenings

Dial Testing

Surveys

Focus Groups

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PicProfile: Content Analysis

QSR’s XSight software for

content analysis.

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Snapshot: Netnography Data

Posted on Internet & intranets

Product & company reviews

Employee experiences

Message board posts

Discussion forum posts

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Research Thought Leader

“The goal is to transform data intoinformation, and information into insight.

Carly Fiorina former president and chairwoman,

Hewlett-Packard Co

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PulsePoint: Research Revelation

55 The percent of white-collar workers who answer work-related calls or e-mail after work hours.

DATA PREPARATION AND DESCRIPTION

Chapter 15

15-30

Photo Attributions

Slide Source

6 Courtesy of CfMC Research Software

8 Courtesy of Western Watts

14 Courtesy of xSight

15 Eric Audras/Getty Images

19 Purestock/SuperStock

20 ©Pamela S. Schindler

24 ©fStop/SuperStock

25 Courtesy of QSR (xSight)

26 Scott Dunlap/Getty Images