Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the...
Transcript of Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the...
![Page 1: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/1.jpg)
![Page 2: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/2.jpg)
Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process
Focus of This Chapter
Relationship toPrevious Chapters
Relationship to MarketingResearch Process
• Preparing Data for Analysis
• Marketing Research Process (Chapter 1)
• Research Design Components (Chapter 3)
Problem Definition
Approach to Problem
Field Work
Data Preparation and Analysis
Report Preparationand Presentation
Research Design
![Page 3: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/3.jpg)
Application to Contemporary IssuesTechnology EthicsInternational
Be
a D
M!
B
e an
MR
!
Exp
erie
nti
al L
earn
ing
Opening Vignette
Wh
at Wo
uld
Yo
u D
o?
Figure 15.2 Data Preparation: An Overview
The Data Preparation Process
Questionnaire Checking and Editing
Coding
Transcribing
Data Cleaning
Selecting Data Analysis Strategy
Fig 15.3
Fig 15.4
Fig 15.5
Fig 15.6
Fig 15.7
![Page 4: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/4.jpg)
Preliminary Plan of Data Analysis
Questionnaire Checking
Editing
Coding
Transcribing
Data Cleaning
Selecting a Data Analysis Strategy
Figure 15.3 Data Preparation Process
![Page 5: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/5.jpg)
Questionnaire Checking
A questionnaire returned from the field may be unacceptable for several reasons.
– Parts of the questionnaire may be incomplete.
– The pattern of responses may indicate that the respondent did not understand or follow the instructions.
– The responses show little variance.
– One or more pages are missing.
– The questionnaire is received after the preestablished cutoff date.
– The questionnaire is answered by someone who does not qualify for participation.
![Page 6: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/6.jpg)
Figure 15.4 Treatment of Unsatisfactory Responses
Treatment ofUnsatisfactory
Responses
Return to theField
Discard UnsatisfactoryRespondents
Assign MissingValues
Substitute aNeutral Value
CasewiseDeletion
PairwiseDeletion
![Page 7: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/7.jpg)
Editing
Treatment of Unsatisfactory Results
– Returning to the Field – The questionnaires with unsatisfactory responses may be returned to the field, where the interviewers recontact the respondents.
– Assigning Missing Values – If returning the questionnaires to the field is not feasible, the editor may assign missing values to unsatisfactory responses.
– Discarding Unsatisfactory Respondents – In this approach, the respondents with unsatisfactory responses are simply discarded.
![Page 8: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/8.jpg)
CodingCoding means assigning a code, usually a number, to each possible response to each question. The code includes an indication of the column position (field) and data record it will occupy.
Coding Questions
Fixed field codes, which mean that the number of records for each respondent is the same and the same data appear in the same column(s) for all respondents, are highly desirable.
– If possible, standard codes should be used for missing data. Coding of structured questions is relatively simple, since the response options are predetermined.
– In questions that permit a large number of responses, each possible response option should be assigned a separate column.
![Page 9: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/9.jpg)
Coding
Guidelines for coding unstructured questions:• Category codes should be mutually exclusive and
collectively exhaustive. • Only a few (10% or less) of the responses should fall
into the “other” category. • Category codes should be assigned for critical issues
even if no one has mentioned them. • Data should be coded to retain as much detail as
possible.
![Page 10: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/10.jpg)
Codebook
A codebook contains coding instructions and the necessary information about variables in the data set. A codebook generally contains the following information:
• column number• record number• variable number• variable name• question number• instructions for coding
![Page 11: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/11.jpg)
Coding Questionnaires
• The respondent code and the record number appear on each record in the data.
• The first record contains the additional codes: project code, interviewer code, date and time codes, and validation code.
• It is a good practice to insert blanks between parts.
![Page 12: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/12.jpg)
TABLE 15.1 Illustrative Computer File: Department Store Patronage Project
FIELDS
COLUMN NUMBERS________________________________ RESPON- DENT 1-3 4 5-6 7-8........... 26..........35 77 1 001 1 31 01 6544234553 5 2 002 1 31 01 5564435433 4 3 003 1 31 01 4655243324 4 4 004 1 31 01 5463244645 6 Record #271 271 1 31 55 6652354435 5
![Page 13: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/13.jpg)
Figure 15.5 A Codebook Excerpt
Column Number1-3
45-67-89-1415-2021-2223-2425
26
27
2835
VariableNumber1
234567
8
9
10
1118
VariableNameRespondent ID
Record NumberProject CodeInterview Codedate CodeTime CodeValidation CodeBlankWho shops
Familiarity with store 1
Familiarity with store 2
Familiarity with store 3Familiarity with store 10
QuestionNumber
I
IIa
IIb
IIcIIj
CodingInstructions001 to 890 add leading zeros as necessary1 (same for all respondents)31 (same for all respondents)As coded on the questionnaireAs coded on the questionnaireAs coded on the questionnaireAs coded on the questionnaireLeave these columns blankMale head =1Female head =2 Other =3Punch the number circledMissing values =9For question II parts a through jPunch the number circledNot so familiar =1Very familiar =6Missing Values =9
![Page 14: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/14.jpg)
Figure 15.6 Data Transcription
Raw Data
Key Punching viaCRT Terminal
Mark SenseForms
ComputerizedSensoryAnalysis
Optical Scanning
CATI/CAPI
Verification: Correct Key Punching Errors
ComputerMemory
DisksMagnetic
Tapes
Transcribed Data
![Page 15: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/15.jpg)
Earlier Steps (1, 2, 3) of the Marketing Research Process
Known Characteristics of Data
Properties of Statistical Techniques
Background & Philosophy of the Researcher
Data Analysis Strategy
Figure 15.7 Selecting a Data Analysis Strategy
![Page 16: Chapter Fifteen. Figure 15.1 Relationship of Data Preparation to the Previous Chapters and the Marketing Research Process Focus of This Chapter Relationship.](https://reader035.fdocuments.us/reader035/viewer/2022071806/56649f4e5503460f94c700ec/html5/thumbnails/16.jpg)
SPSS Windows• Using the Base module, out-of-range values can be selected using
the SELECT IF command. These cases, with the identifying information (subject ID, record number, variable name, and variable value) can then be printed using the LIST or PRINT commands. The Print command will save active cases to an external file. If a formatted list is required, the SUMMARIZE command can be used.
• SPSS Data Entry can facilitate data preparation. You can verify that respondents have answered completely by setting rules. These rules can be used on existing datasets to validate and check the data, whether or not the questionnaire used to collect the data was constructed in Data Entry. Data Entry allows you to control and check the entry of data through three types of rules: validation, checking, and skip and fill rules.
• While the missing values can be treated within the context of the Base module, SPSS Missing Values Analysis can assist in diagnosing missing values and replacing missing values with estimates.
• TextSmart by SPSS can help in the coding and analysis of open ended responses.