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)
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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