The document outlines the use of digital platforms for research, emphasizing data preparation, collection, and editing processes. It discusses the importance of relevant, high-quality, timely, and complete data for effective decision-making and highlights the stages involved in data analysis, including field and in-house editing. Furthermore, it details coding methods for qualitative responses and the significance of systematic procedures in error checking during the editing process.

1. Use of Digital
Base in
Research
Module 4

2. Syllabus
Use of Digital Platforms like
Survey Monkey, Google Forms,
Digital Sources for secondary
data
Data Preparation; Data Editing
& Coding
Practical Aspects: Data
Collection and Data Preparation

3. Data, Information and Intelligence
• Data are Facts or recorded measures of certain phenomena (things).
• Information is Data formatted (structured) to support decision making
or define the relationship between two facts. Business
• Intelligence is the subset of data and information that actually has
some explanatory power enabling effective decisions to be made

4. The Characteristics of Valuable Information
• Four characteristics help determine how useful data may be:
• Relevance: The characteristics of data reflecting how pertinent these
particular facts are to the situation at hand.
• Quality: The degree to which data represent the true situation.
• Timeliness: Means that the data are current enough to still be relevant.
• Completeness: Having the right amount of information

5. Use of Digital
Patforms

6. Google
Forms

7. Data Preparation
• A very common phrase that is used by researchers is “garbage in,
garbage out.” This refers to the idea that if data is collected
improperly, or coded incorrectly, your results are “garbage,” because
that is what was entered into the data set to begin with.

8. Editing
• Editing is the process of checking and adjusting data for omissions, ,
consistency, and legibility.
• When the editor discovers a problem, he or she adjusts the data to
make them more complete, consistent, or readable.

9. Stages of
Data Analysis

10. Field Editing
• Field editing is done on the same day as the interview.
• Field editing is used to
1. Identify technical omissions such as a blank page on an interview
form
2. Check legibility of handwriting for open-ended responses
3. Clarify responses that are logically or conceptually inconsistent.

11. In-House Editing
• In-house editing rigorously investigates the results of data collection.
The research supplier or research department normally has a
centralized office staff perform the editing and coding function.

12. ILLUSTRATING INCONSISTENCY FACT OR
FICTION?
• If the editor’s review of a questionnaire indicates that the respondent
was only 17 years old, the editor’s task is to correct this mistake by
deleting this response because this respondent should never have
been considered as a sampling unit.
• The sampling units (respondents) should all be consistent with the
defined population.

13. EDITING TECHNOLOGY
• For Electronic Questionnaires, rules can be entered which prevent
inconsistent responses from ever being stored in the file used for data
analysis.

14. Editing for Completeness
• In some cases the respondent may have answered only the second
portion of a two-part question.
• The following question creates a situation in which an in-house editor
may have to adjust answers for completeness:
• Does your organization have more than one computer network
server? Yes No If yes, how many? ____
• If the respondent checked neither yes nor no but indicated three
computer installations, the editor should change the first response to
a “Yes” as long as other information doesn’t indicate otherwise.

15. Item nonresponse
• Item nonresponse is the technical term for an unanswered question
on an otherwise complete questionnaire. Missing data results from
item nonresponse.
• Plug-ins

16. Coding
• Editing may be differentiated from coding, which is the assignment of
numerical scores or classifying symbols to previously edited data.
• Careful editing makes the coding job easier. Codes are meant to
represent the meaning in the data

17. Coding Qualitative Responses
In qualitative research, the codes are usually words or phrases that
represent themes.
STRUCTURED QUALITATIVE RESPONSES
• Qualitative responses to structured questions such as “yes” or “no”
can be stored in a data file with letters such as “Y” or “N.”
Alternatively, they can be represented with numbers, one each to
represent the respective category. So, the number 1 can be used to
represent “yes” and 2 can be used to represent “no.”

18. Code Construction
• There are two basic rules for code construction.
• First, the coding categories should be exhaustive, meaning that a
coding category should exist for all possible responses.
• Missing data should also be represented with a code. Most software will
understand that either a period or a blank response represents missing data.
• Second, the coding categories should be mutually exclusive and
independent.

19. More on Coding Open-Ended Questions
• For example, a consumer survey about frozen food also asked why a new
microwaveable product would not be purchased:
• We don’t buy frozen food very often. • I like to prepare fresh food. •
Frozen foods are not as tasty as fresh foods. • I don’t like that freezer taste.
• All of these answers could be categorized under “dislike frozen foods” and
assigned the code 1.
• Code construction in these situations reflects the judgment of the
researcher.
• A major objective in the code-building process is to accurately transfer the
meanings from written responses to numeric codes.

20. Error Checking

21. Pitfalls of Editing
• A systematic procedure for assessing the questionnaires should be
developed by the research analyst so that the editor has clearly
defined decision rules to follow. Any inferences such as imputing
missing values should be done in a manner that limits the chance for
the data editor’s subjectivity to influence the response.