chapter 19 editing and coding: transforming raw data into information © 2010 south-western/cengage...

Chapter 19Chapter 19Editing and Coding: Editing and Coding: Transforming Raw Transforming Raw

Data into Data into InformationInformation

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

EIGHTH EDITION

BUSINESS MARKET RESEARCH

ZIKMUND BABINCARR GRIFFIN

© 2010 South-Western/Cengage Learning. All rights reserved. May not be scanned, copied or duplicated, or posted to a publically accessible website, in whole or in part. 19–2

LEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMESLEARNING OUTCOMES

1.1. Know when a response is really an error and should Know when a response is really an error and should be editedbe edited

2.2. Appreciate coding of pure qualitative researchAppreciate coding of pure qualitative research

3.3. Understand the way data are represented in a data fileUnderstand the way data are represented in a data file

4.4. Understand the coding of structured responses Understand the coding of structured responses including a dummy variable approachincluding a dummy variable approach

5.5. Appreciate the ways that technological advances have Appreciate the ways that technological advances have simplified the coding processsimplified the coding process

After studying this chapter, you should be able to


Stages of Data AnalysisStages of Data Analysis

• Raw DataRaw Data The unedited responses from a respondent exactly as The unedited responses from a respondent exactly as

indicated by that respondent.indicated by that respondent.

• Nonrespondent ErrorNonrespondent Error Error that the respondent is not responsible for Error that the respondent is not responsible for

creating, such as when the interviewer marks a creating, such as when the interviewer marks a response incorrectly.response incorrectly.

• Data IntegrityData Integrity The notion that the data file actually contains the The notion that the data file actually contains the

information that the researcher is trying to obtain to information that the researcher is trying to obtain to adequately address research questions.adequately address research questions.


EXHIBIT 19.EXHIBIT 19.11 Overview of the Stages of Data AnalysisOverview of the Stages of Data Analysis


EditingEditing

• EditingEditing The process of checking the completeness, consistency, and The process of checking the completeness, consistency, and

legibility of data and making the data ready for coding and legibility of data and making the data ready for coding and transfer to storage.transfer to storage.

• Field EditingField Editing Preliminary editing by a field supervisor on the same day as the Preliminary editing by a field supervisor on the same day as the

interview to catch technical omissions, check legibility of interview to catch technical omissions, check legibility of handwriting, and clarify responses that are logically or handwriting, and clarify responses that are logically or conceptually inconsistent.conceptually inconsistent.

• In-House EditingIn-House Editing A rigorous editing job performed by a centralized office staff.A rigorous editing job performed by a centralized office staff.


EditingEditing

• Checking for ConsistencyChecking for Consistency Respondents match defined populationRespondents match defined population Check for consistency within the data collection Check for consistency within the data collection

frameworkframework

• Taking Action When Response is Obviously in Taking Action When Response is Obviously in ErrorError Change/correct responses only when there are Change/correct responses only when there are

multiple pieces of evidence for doing so.multiple pieces of evidence for doing so.

• Editing TechnologyEditing Technology Computer routines can check for consistency Computer routines can check for consistency

automatically.automatically.


Editing for CompletenessEditing for Completeness

• Item NonresponseItem Nonresponse The technical term for an unanswered question on an The technical term for an unanswered question on an

otherwise complete questionnaire resulting in missing otherwise complete questionnaire resulting in missing data.data.

Plug ValuePlug Value An answer that an editor “plugs in” to replace blanks or An answer that an editor “plugs in” to replace blanks or

missing values so as to permit data analysis.missing values so as to permit data analysis.

Choice of value is based on a predetermined decision rule.Choice of value is based on a predetermined decision rule.

ImputeImpute To fill in a missing data point through the use of a statistical To fill in a missing data point through the use of a statistical

process providing an educated guess for the missing process providing an educated guess for the missing response based on available information.response based on available information.


Editing for Completeness (cont’d)Editing for Completeness (cont’d)

• What about missing data?What about missing data? List-wise deletionList-wise deletion

The entire record for a respondent that has left a response The entire record for a respondent that has left a response missing is excluded from use in statistical analysis.missing is excluded from use in statistical analysis.

Pair-wise deletionPair-wise deletion Only the actual variables for a respondent that do not contain Only the actual variables for a respondent that do not contain

information are eliminated from use in statistical analysis.information are eliminated from use in statistical analysis.


Facilitating the Coding ProcessFacilitating the Coding Process

• Editing And Tabulating “Don’t Know” AnswersEditing And Tabulating “Don’t Know” Answers Legitimate don’t know (no opinion)Legitimate don’t know (no opinion) Reluctant don’t know (refusal to answer)Reluctant don’t know (refusal to answer) Confused don’t know (does not understand)Confused don’t know (does not understand)


Editing (cont’d)Editing (cont’d)

• Pitfalls of EditingPitfalls of Editing Allowing subjectivity to enter into the editing process. Allowing subjectivity to enter into the editing process.

Data editors should be intelligent, experienced, and objective.Data editors should be intelligent, experienced, and objective.

A A systematic proceduresystematic procedure for assessing the for assessing the questionnaire should be developed by the research questionnaire should be developed by the research analyst so that the editor has clearly defined decision analyst so that the editor has clearly defined decision rules. rules.

• Pretesting EditPretesting Edit Editing during the pretest stage can prove very Editing during the pretest stage can prove very

valuable for improving questionnaire format, valuable for improving questionnaire format, identifying poor instructions or inappropriate question identifying poor instructions or inappropriate question wording.wording.


Coding Qualitative ResponsesCoding Qualitative Responses

• CodingCoding The process of assigning a numerical score or other character The process of assigning a numerical score or other character

symbol to previously edited data.symbol to previously edited data.

• CodesCodes Rules for interpreting, classifying, and recording data in the Rules for interpreting, classifying, and recording data in the

coding process.coding process. The actual numerical or other character symbols assigned to The actual numerical or other character symbols assigned to

raw data.raw data.

• Dummy CodingDummy Coding Numeric “1” or “0” coding where each number represents an Numeric “1” or “0” coding where each number represents an

alternate response such as “female” or “male.”alternate response such as “female” or “male.” If If kk is the number of categories for a qualitative variable, is the number of categories for a qualitative variable, k-1k-1

dummy variables are needed.dummy variables are needed.


EXHIBIT 19.EXHIBIT 19.22 Coding Qualitative Data with WordsCoding Qualitative Data with Words


Data File TerminologyData File Terminology

• FieldField A collection of characters that represents a single A collection of characters that represents a single

type of data—usually a variable.type of data—usually a variable.

• String CharactersString Characters Computer terminology to represent formatting a Computer terminology to represent formatting a

variable using a series of alphabetic characters variable using a series of alphabetic characters (nonnumeric characters) that may form a word.(nonnumeric characters) that may form a word.

• RecordRecord A collection of related fields that represents the A collection of related fields that represents the

responses from one sampling unit.responses from one sampling unit.


Data File Terminology (cont’d)Data File Terminology (cont’d)

• Data FileData File The way a data set is stored electronically in The way a data set is stored electronically in

spreadsheet-like form in which the rows represent spreadsheet-like form in which the rows represent sampling units and the columns represent variables.sampling units and the columns represent variables.

• Value LabelsValue Labels Unique labels assigned to each possible numeric Unique labels assigned to each possible numeric

code for a response.code for a response.


EXHIBIT 19.EXHIBIT 19.33 Data Storage Terminology in SPSSData Storage Terminology in SPSS


EXHIBIT 19.EXHIBIT 19.44 AA Data File Stored in SPSS Data File Stored in SPSS


Code ConstructionCode Construction

• Two Basic Rules for Coding Categories:Two Basic Rules for Coding Categories:

1.1. They should be They should be exhaustiveexhaustive, meaning that a coding , meaning that a coding category should exist for all possible responses.category should exist for all possible responses.

2.2. They should be They should be mutually exclusive and independentmutually exclusive and independent, , meaning that there should be no overlap among the meaning that there should be no overlap among the categories to ensure that a subject or response can categories to ensure that a subject or response can be placed in only one category.be placed in only one category.

• Test TabulationTest Tabulation Tallying of a small sample of the total number of Tallying of a small sample of the total number of

replies to a particular question in order to construct replies to a particular question in order to construct coding categories.coding categories.


EXHIBIT 19.EXHIBIT 19.55 Precoding Fixed-Alternative ResponsesPrecoding Fixed-Alternative Responses


EXHIBIT 19.EXHIBIT 19.66 Precoded Format for Telephone InterviewPrecoded Format for Telephone Interview


EXHIBIT 19.EXHIBIT 19.77 Coding Open-Ended Questions about ChiliCoding Open-Ended Questions about Chili


Devising the Coding SchemeDevising the Coding Scheme

• A coding scheme should not be too elaborate.A coding scheme should not be too elaborate. The coder’s task is only to summarize the data.The coder’s task is only to summarize the data.

Categories should be sufficiently unambiguous that Categories should be sufficiently unambiguous that coders will not classify items in different ways.coders will not classify items in different ways.

• Code bookCode book Identifies each variable in a study and gives the Identifies each variable in a study and gives the

variable’s description, code name, and position in the variable’s description, code name, and position in the data matrix.data matrix.


EXHIBIT 19.EXHIBIT 19.88 Open-Ended Responses to a Survey about the Honolulu AirportOpen-Ended Responses to a Survey about the Honolulu Airport


Computerized Survey Data ProcessingComputerized Survey Data Processing

• Data EntryData Entry The activity of transferring data from a research The activity of transferring data from a research

project to computers.project to computers.

• Optical Scanning SystemOptical Scanning System A data processing input device that reads material A data processing input device that reads material

directly from mark-sensed questionnaires.directly from mark-sensed questionnaires.


Data View in SPSS Serves Much the Same Purpose of a Coding SheetData View in SPSS Serves Much the Same Purpose of a Coding Sheet

chapter 19 editing and coding: transforming raw data into information © 2010 south-western/cengage...

Documents

legibility of data

way data

data ready

data fileunderstand

data editors

missing data point

missing response

response missing