Business 19
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Transcript of Business 19
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Business
Research MethodsWilliam G. Zikmund
Chapter 19:
Editing and Coding
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DATA ANALYSIS
DATA ENTRY
ERROR
CHECKING
AND
VERIFICATION
CODING
EDITINGStages of Data Analysis
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Editing The process of checking and adjusting
the data
for omissions for legibility
for consistency
And readying them for coding andstorage
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Coding The process of identifying and
assigning a numerical score or other
character symbol to previously editeddata
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Editing
IN-HOUSE
EDITING
FIELD
EDITING
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CONSISTENCYCONSISTENCY COMPLETENESSCOMPLETENESSQUESTIONSQUESTIONS
ANSWEREDANSWERED
OUT OF ORDEROUT OF ORDER
Reasons for Editing
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Codes The rules for interpreting, classifying,
and recording data in the coding
process The actual numerical or other
character symbols
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I believe that people judge your
success by the kind of car you drive.Strongly disagree 1
Mildly disagree 2
Neither disagreenor agree
3
Mildly agree 4
Strongly agree5
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Rules for Coding Categories should be exhaustive
Categories should be mutually
exclusive and independent
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Coding Open-Ended Responses Purpose: To reduce the large number of
responses to a few general, meaningfulcategories that can be assigned numbers.
Steps: Take 20% of surveys to develop coding
scheme Separate into individual responses Look for themes in the answers Assign like themes a numerical code
Continue process until responses have beengrouped into a meaningful, categoricalscheme
Have at least 2 new coders code remainingsurvey responses Check for intercoder reliability
Key codes into data file
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Data Entry The process of transforming raw data
from the research project to computers.
Controllable source of error.
ALWAYS verify data entry by either:1. Having two people key the data into two
separate files and comparing fileselectronically.
2. Having someone other than the original key
stroker to manually check the file either onscreen or paper.
For your projects, be sure to report the methodused in your paper.
Optical scanning systems Marked-sensed questionnaires
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Recode Recoding is the process of using a
computer to convert original codesused for raw data to codes that aremore suitable for analysis.
Examples: Attitude items in scaleswith both positive and negativestatements. Reverse the coding for negative
statements to reflect the same directionand order or magnitude as the positivestatements
greater magnitude = higher number
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Error Checking and Verification Data cleaning
If properly verified, data should be 100%
clean already. Use frequency tables for good triple-
check.