Bài giảng Business Research Methods - Chapter 15: Data Preparation and Description

Data Preparation and DescriptionChapter 15McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.Learning ObjectivesUnderstand . . .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 ObjectivesUnderstand . . .Problems with and solutions for “don’t know” responses and handling missing data.The options for data entry and manipulation.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.Data Preparation in the Research ProcessMonitoring Online Survey DataOnline surveys need special editing attention. CfMC provides software and support to research suppliers to prevent interruptions from damaging data .EditingCriteriaConsistentUniformly enteredArranged forsimplificationCompleteAccurateField EditingField editing review Entry gaps identified Callbacks made Results validatedCentral EditingBe familiar with instructions given to interviewers and codersDo not destroy the original entryMake all editing entries identifiable and in standardized formInitial all answers changed or suppliedPlace initials and date of editing on each instrument completedSample CodebookPrecodingCoding Open-Ended Questions6. What prompted you to purchase your most recent life insurance policy? _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________ _______________________________Coding RulesCategories should beAppropriate to the research problemExhaustiveMutually exclusiveDerived from one classification principleContent AnalysisTypes of Content AnalysisSyntacticalPropositionalReferentialThematicOpen-Question Coding Locus of Responsibility Mentioned Not MentionedA. 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. Other10207315128520Proximity PlotHandling “Don’t Know” ResponsesQuestion: Do you have a productive relationship with your present salesperson?Years of Purchasing YesNoDon’t KnowLess than 1 year10%40%38%1 – 3 years3030324 years or more603030Total100%n = 650100%n = 150100%n = 200Data EntryDatabase ProgramsOptical RecognitionDigital/BarcodesVoicerecognitionKeyboardingMissing Data SolutionsListwise DeletionPairwise DeletionReplacementKey TermsBar codeCodebookCodingContent analysisData entryData fieldData fileData preparationData recordDatabaseDon’t know response EditingMissing dataOptical character recognitionOptical mark recognitionPrecodingSpreadsheetVoice recognitionAdditional Discussion opportunitiesChapter 15CloseUp: Dirty DataInvalid: entry errorsIncomplete: missing, siloed, turf warsInconsistent: across databases Incorrect: lost, falsified, outdatedSolutions: Data Steward, Data Protocols, Error Detection SoftwareSnapshot: CBS labs39 Million VisitorsShow ScreeningsDial TestingSurveysFocus GroupsPicProfile: Content AnalysisQSR’s XSight software for content analysis.Snapshot: Netnography DataPosted on Internet & intranetsProduct & company reviewsEmployee experiencesMessage board postsDiscussion forum postsResearch Thought Leader“The goal is to transform data intoinformation, and information into insight.Carly Fiorina former president and chairwoman, Hewlett-Packard CoPulsePoint: Research Revelation55The percent of white-collar workers who answer work-related calls or e-mail after work hours.Data Preparation and DescriptionChapter 15Photo AttributionsSlideSource6Courtesy of CfMC Research Software8Courtesy of Western Watts14Courtesy of xSight15Eric Audras/Getty Images19Purestock/SuperStock20©Pamela S. Schindler24©fStop/SuperStock25Courtesy of QSR (xSight)26Scott Dunlap/Getty Images