Chapter 15 DATA PREPARATION AND DESCRIPTION McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. 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. 15-2 Learning Objectives Understand . . . Problems with and solutions for “don’t know” responses and handling missing data. The options for data entry and manipulation. 15-3 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. 15-4 Data Preparation in the Research Process 15-5 Monitoring Online Survey Data Online surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data . 15-6 Editing Accurate Arranged for simplification Consistent Criteria Uniformly entered Complete 15-7 Field Editing Field editing review Entry gaps identified Callbacks made Results validated 15-8 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 15-9 Sample Codebook 15-10 Precoding 15-11 Coding Open-Ended Questions 6. What prompted you to purchase your most recent life insurance policy? _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ 15-12 Coding Rules Appropriate to the research problem Exhaustive Categories should be Mutually exclusive Derived from one classification principle 15-13 Content Analysis 15-14 Types of Content Analysis Syntactical Referential Propositional Thematic 15-15 Open-Question Coding Locus of Responsibility Mentioned A. Company _____________ B. Customer _____________ C. Joint CompanyCustomer _____________ F. Other _____________ Not Mentioned _______________ Locus of Responsibility 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 Frequency (n = 100) 10 20 7 3 15 12 8 5 20 15-16 Proximity Plot 15-17 Handling “Don’t Know” Responses Question: Years of Purchasing Do you have a productive relationship with your present salesperson? No Don’t Know 10% 40% 38% 1 – 3 years 30 30 32 4 years or more 60 30 30 100% n = 650 100% n = 150 100% n = 200 Less than 1 year Total Yes 15-18 Data Entry Keyboarding Digital/ Barcodes Database Programs Optical Recognition Voice recognition 15-19 Missing Data Solutions Listwise Deletion Pairwise Deletion Replacement 15-20 Key Terms • Bar code Don’t know response • Codebook Editing • Coding Missing data • Content analysis Optical character • Data entry • Data field • Data file • Data preparation • Data record recognition Optical mark recognition Precoding Spreadsheet Voice recognition • Database 15-21 Chapter 15 ADDITIONAL DISCUSSION OPPORTUNITIES 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 15-23 Snapshot: CBS labs 39 Million Visitors Show Screenings Dial Testing Surveys Focus Groups 15-24 PicProfile: Content Analysis QSR’s XSight software for content analysis. 15-25 Snapshot: Netnography Data Posted on Internet & intranets Product & company reviews Employee experiences Message board posts Discussion forum posts 15-26 Research Thought Leader “The goal is to transform data into information, and information into insight. Carly Fiorina former president and chairwoman, Hewlett-Packard Co 15-27 PulsePoint: Research Revelation 55 The percent of white-collar workers who answer work-related calls or email after work hours. 15-28 Chapter 15 DATA PREPARATION AND DESCRIPTION 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 15-30