Transcript BRM 8e
Editing and Coding:
Transforming Raw
Data into
Information
(Source: W.G Zikmund, B.J Babin, J.C Carr and M.
Griffin, Business Research Methods, 8th Edition,
U.S, South-Western Cengage Learning, 2008)
1
Objectives
1.
Know when a response is really an error and should be
edited
2.
Appreciate coding of pure qualitative research
3.
Understand the way data are represented in a data file
4.
Understand the coding of structured responses
including a dummy variable approach
5.
Appreciate the ways that technological advances have
simplified the coding process
2
Stages of Data Analysis
Raw Data
Nonrespondent Error
The unedited responses from a respondent
exactly as indicated by that respondent.
Error that the respondent is not responsible for
creating, such as when the interviewer marks a
response incorrectly.
Data Integrity
The notion that the data file actually contains the
information that the researcher is trying to obtain
to adequately address research questions.
3
Editing
Editing
Field Editing
The process of checking the completeness, consistency, and
legibility of data and making the data ready for coding and
transfer to storage.
Preliminary editing by a field supervisor on the same day as the
interview to catch technical omissions, check legibility of
handwriting, and clarify responses that are logically or
conceptually inconsistent.
In-House Editing
A rigorous editing job performed by a centralized office staff.
4
Editing
Checking for Consistency
Respondents match defined population
Check for consistency within the data collection
framework
Taking Action When Response is Obviously in Error
Change/correct responses only when there are
multiple pieces of evidence for doing so.
Editing Technology
Computer routines can check for consistency
automatically.
5
Editing for Completeness
Item Nonresponse
The technical term for an unanswered question on an otherwise
complete questionnaire resulting in missing data.
Plug Value
An answer that an editor “plugs in” to replace blanks or
missing values so as to permit data analysis.
Choice of value is based on a predetermined decision rule.
Impute
To fill in a missing data point through the use of a statistical
process providing an educated guess for the missing
response based on available information.
6
Editing for Completeness (cont’d)
What about missing data?
List-wise deletion
The entire record for a respondent that has left a
response missing is excluded from use in statistical
analysis.
Pair-wise deletion
Only the actual variables for a respondent that do not
contain information are eliminated from use in statistical
analysis.
7
Facilitating the Coding Process
Editing And Tabulating “Don’t Know” Answers
Legitimate don’t know (no opinion)
Reluctant don’t know (refusal to answer)
Confused don’t know (does not understand)
8
Coding Qualitative Responses
Coding
The process of assigning a numerical score or other character
symbol to previously edited data.
Codes
Rules for interpreting, classifying, and recording data in the
coding process.
The actual numerical or other character symbols assigned to raw
data.
Dummy Coding
Numeric “1” or “0” coding where each number represents an
alternate response such as “female” or “male.”
9
Coding Qualitative Data with Words
10
Data Storage Terminology in SPSS
11
A Data File Stored in SPSS
12
Computerized Survey Data Processing
Data Entry
The activity of transferring data from a research
project to computers.
Optical Scanning System
A data processing input device that reads material
directly from mark-sensed questionnaires.
13
Data View in SPSS Serves Much the Same Purpose of a Coding Sheet
14