1. Data editing involves checking completed questionnaires for errors, omissions, and inconsistencies to ensure data quality before coding. This includes field and in-house editing. 2. Coding is the process of assigning numerical or character codes to questionnaire responses to prepare the data for computer processing. Codes are developed to classify responses systematically. 3. After coding, data entry involves transferring the coded data from questionnaires to a computer file. Error checking verifies the accuracy of data entry.

1. Data Editing and Coding
Dr Raju Indukoori

2. Overview of the Stages of Data Analysis
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3. EDITING
• The process of checking and adjusting
responses in the completed questionnaires for
omissions, legibility, and consistency and
readying them for coding and storage.
• Detects errors and omissions, corrects them
when possible, and certifies that minimum data
quality standards are achieved
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4. Types of Editing
1. Field Editing
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.
2. In-house or Central Editing
Editing performed by a central office staff;
often dome more rigorously than field editing
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5. Purpose of Editing
To ensure
1. Accuracy of data collected
2. For consistency between responses
3. Uniformity
4. For completeness in responses– to reduce
effects of item non-response
5. To facilitate and Simplify coding and
Tabulation.
6. To better utilize questions answered out of order
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6. Editing for Completeness
1. Item Non-response
2. Plug Value
3. Impute
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7. Editing for Completeness
1. Item Non-response
The technical term for an unanswered question on
an otherwise complete questionnaire resulting in
missing data.
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8. Editing for Completeness
2. 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.
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9. Editing for Completeness
3. Impute
To fill in a missing data point through the use of a
statistical algorithm that provides a best guess for
the missing response based on available
information.
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10. Facilitating the Coding Process
• Data Clean-up
Checking written responses for any stray
marks
• 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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11. Pitfalls of Editing
• Allowing subjectivity to enter into the editing
process.
• Data editors should be intelligent, experienced,
and objective.
• Failing to have a systematic procedure for
assessing the questionnaires developed by the
research analyst
• An editor should have clearly defined decision
rules to follow.
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12. Pretesting Edit
Editing during the pretest stage can prove
very valuable for improving questionnaire
format, identifying poor instructions or
inappropriate question wording.
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13. CODING
• Code is a numerical score or symbol.
• Coding is the process of identifying and
classifying each answer with a numerical
score or other character symbol
• Identifying responses with codes is
necessary if data is to be processed by
computer
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14. What does coding do?
• It serves as a rule for interpreting,
classifying, and recording data
• It guides the establishment of category
sets
• Appropriate to the research problem and
purpose
• Exhaustive
• Mutually exclusive
• Derived from one classification principle
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15. Contd…
1. Set Rules For Code Construction are:
2. Pre-Coding Fixed-Alternative Questions (FAQs)
3. Coding Open-Ended Questions
4. Maintaining a Code Book
5. Production Coding
6. Combining Editing and Coding
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16. 1. Set Rules For Code Construction
a) Coding categories should be exhaustive
b) Coding categories should be mutually exclusive and
independent
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17. 2. Pre-Coding Fixed-Alternative Questions (FAQs)
1. Pre-Coding Fixed-Alternative Questions (FAQs) -
Writing codes for FAQs on the questionnaire before the
data collection
2. Coding Open-Ended Questions - A 3-stage process:
(a) Perform a test tabulation, (b) Devise a coding scheme,
(c) Code all responses
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18. 3. Maintaining a Code Book
Code book that identifies each variable in a
study, the variable’s description, code name,
and position in the data matrix
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19. Data Matrix
• It is a works sheet with coded data in a
rectangular form with data in rows and columns
• Rows representing cases and columns represent
variables.
• The data matrix is organized into fields, records,
and files
• Field: A collection of characters that represents a single
type of data
• Record: A collection of related fields, i.e., fields related
to the same case (or respondent)
• File: A collection of related records, i.e. records related to
the same sample
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20. 4. Production Coding
The physical activity of transferring the data
from the questionnaire or data collection form
[to the computer] after the data has been
collected. Sometimes done through a coding
sheet – ruled paper drawn to mimic the data
matrix
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21. 5. Combining Editing and Coding
• Finally the coding and editing is
combined for further analysis
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22. AFTER CODING
1. Data Entry
2. Error Checking
3. Data Transformation
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23. 1. Data Entry
The transfer of codes from questionnaires (or
coding sheets) to a computer. Often
accomplished in one of three ways:
a)On-line direct data entry – Student data
base entered online by students.
b)Optical scanning – for highly structured
questionnaires.
c) Keyboarding – data entry via a computer
keyboard; often requires verification.
d) Voice Recognition
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24. 1. Data Entry Formats
• Full screen editors
• Spread Sheet
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25. 2. Error Checking
Verifying the accuracy of data entry
and checking for some kinds of obvious
errors made during the data entry. Often
accomplished through frequency
analysis.
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26. 3. Data Transformation
Converting some of the data from the format in
which they were entered to a format most suitable
for particular statistical analysis.
Often accomplished through re-coding, to:
• reverse-score negative (or positive) statements into
positive (or negative) statements;
• collapse the number of categories of a variable
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