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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Data Preparation for Analysis Chapter 11, Student Edition MR/Brown & Suter 1
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter2 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter3 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 1 MR/Brown & Suter4 Editing – the inspection and correction of the data received from each element of the sample (or census) During the editing process, it must be decided what to do about cases with incomplete answers, obviously wrong answers, and answers that reflect a lack of interest
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter5 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 2 MR/Brown & Suter6 Coding – the process of transforming raw data into symbols (usually numbers) that can be utilized for analysis Coding Closed-ended Items Examples 1 = Female, 2 = Male 1 = Unfavorable to 7 = Favorable Coding Open-ended Items Factual open-ended items are highly structured and easy to code Exploratory open-ended items are less structured and allow for multiple responses making it more difficult to code
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter7 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 3 MR/Brown & Suter8 SPORTING GOODS SURVEY Please answer the following questions about buying sporting goods over the internet: 1.During the past year, what percentage of the sporting goods you purchased were ordered through the internet? ________ percent 2.How willing are you to purchase merchandise offered through the Avery Sporting Goods web site? Not at all willing Somewhat willing Very willing 3.Please provide some reasons why someone might not want to purchase sporting goods over the internet:
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 3 MR/Brown & Suter9 Variable NameDescriptionResponse Options IDQuestionnaire Identification No. PERCENT% Products purchase through Internet (record response) WILLINGWillingness to purchase through Web site 1=not at all willing 2=somewhat willing 3=very willing REASON1 REASON2 REASON3 Reasons for not purchasing (all open-ended) 1=security issues 2=no Internet access 3=can’t examine goods 4=difficult to return 5=don’t want to wait 6=prior bad experience 7=other Missing=Blank
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter10 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 4 MR/Brown & Suter11 Blunder An error that arises during editing, coding or data entry Blunders are usually due to researcher carelessness Blunders Can Be Located By Examining frequency distributions on all variables Checking a sample of questionnaires against the data file Double-entry of data in which data are entered into two separate data files and then compared for discrepancies (preferred) Optical scanning can be used to “read” responses Nonresponse/Missing Data Should Also Be Investigated
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objectives MR/Brown & Suter12 1. Explain the purpose of the editing process 2. Define what coding is 3. Describe the kinds of information contained in a codebook 4. Describe common methods for cleaning the data file 5. Discuss options for dealing with missing data in analyses
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© 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part. Learning Objective 5 MR/Brown & Suter13 Eliminate the Case with the Missing Items from All Further Analyses Eliminate the Case with the Missing Items from Analyses Using the Variable Substitute Values for the Missing Items Contact the Respondent Again
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