Data Processing
Session 22: Data Processing
Total Session Time: 120 minutes + 240 minutes assignment
Prerequisites
None
Learning Tasks
By the end of this session students are expected to be able to:
Categorize the data
Code the data
Summarize the data in data master sheets
Compile the data manually without master sheets
Resources Needed
Flip charts, marker pens, and masking tape
Black/white board and chalk/whiteboard markers
Computer and LCD Projector
SESSION OVERVIEW
Activity/
Step Time Content
Method
1 05 minutes Presentation Introduction, Learning Tasks
Presentation
2 20 minutes Group Categorizing the Data
discussion
20 minutes Presentation
3 Coding the Data
Brainstorming
30 minutes
4 Presentation Summarizing the Data in Data Master Sheets
35 minutes
5 Presentation Compiling the Data Manually without Master Sheets
6 05 minutes Presentation Key Points
7 05 minutes Presentation Evaluation
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SESSION CONTENTS
STEP1: Presentation of Session Title and Learning Tasks (5 minutes)
READ or ASK students to read the learning tasks and clarify
ASK students if they have any questions before continuing
STEP 2: Categorizing the Data (20 minutes)
Activity: Small Group Discussion (20 minutes)
DIVIDE students into small manageable groups
ASK students to discuss on the following question
ALLOW students to discuss for 10 minutes
ALLOW few groups to present and the rest to add points not mentioned
CLARIFY and SUMMARIZE by using the contents below
o Sorting data
o Performing quality-control checks
o Data processing
o Data analysis
o An appropriate system for sorting the data is important for facilitating subsequent
processing and analysis.
o If you have different study populations (for example village health workers, village
health committees and the general population), number the questionnaires separately.
o In a comparative study sort the data right after collection into the two or three groups
that will be compared during data analysis.
o Checked in the field to ensure that all the information has been properly collected and
recorded.
o Before and during data processing, the information should be checked again for
completeness and internal consistency.
o If a questionnaire has not been filled in completely you will have missing data for
some of the variables.
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o If there are many missing data in a particular questionnaire, the decision may be to
exclude the whole questionnaire from further analysis.
o If an inconsistency is clearly due to a mistake made by the researcher/research
assistant
o For example, if a person in an earlier question is recorded as being a non-smoker,
whereas all other questions reveal that he is smoking, it may still be possible to check
with the person who conducted the interview and to correct the answer.
scale study) to return to the respondent and ask for clarification.
this particular part of the data from further processing and analysis as it will affect the
validity of the study.
excluded from further analysis. (Normally, however, you would discover such a problem
during the pre-test and change the wording of the question.)
o Have the data been sorted appropriately?
o Have questionnaires been numbered?
o Major categories of informants distinguished?
o Have quality checks been performed? For completeness and consistency of
information?
o Has all qualitative data been categorized as far as possible?
o Decide on the method for processing and analyzing data from questionnaires
Manually, using data master sheets or manual compilation of the questionnaires
By computer, for example, using micro-computer and existing software or self-
written programmes for data analysis
o Categorizing/classifying the data
o Coding
o Summarizing the data in data master sheets, manual compilation without master
o sheets, or data entry and verification by computer
o Decisions have to be made concerning how to categorize responses.
o Categorical variables that are investigated through closed questions or observation,
the categories are decided earlier.
o In interviews the answers to open-ended questions, the answers can be pre-
categorized to a certain extent, depending on the knowledge of possible answers that
may be given.
Answers that are difficult or impossible to categorize may be put in a separate
residual category called ‗others‘, but this category should not contain more than
5% of the answers obtained.
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income), decisions concerning how to categorize and code the data at the time you
develop your tools may be premature.
cannot reclassify the data anymore.
STEP 3: Coding the Data (20 minutes)
Activity: Brainstorming (05 minutes)
Ask students to brainstorm on the following question:
ALLOW few students to respond?
WRITE their responses on the flip chart/ board
CLARIFY and SUMMARISE by using the content below
o If the data will be entered in a computer for subsequent processing and analysis, it is
essential to develop a coding system.
o For computer analysis, each category of a variable can be coded with a letter, group of
letters or word, or be given a number. For example, the answer ‗yes‘ may be coded as
‗Y‘ or 1; ‗no‘ as ‗N‘ or 2 and ‗no response‘ or ‗unknown‘ as 'Ú' or 9.
o The codes should be entered on the questionnaires (or checklists) themselves.
o When finalising your questionnaire, for each question you should insert a box for the
code in the right margin of the page.
o These boxes should not be used by the interviewer. They are only filled in afterwards
during data processing.
o Take care that you have as many boxes as the number of digits in each code.
o If analysis is done by hand using data master sheets, it is useful to code your data as
well
o Coding conventions
o Common responses should have the same code in each question, as this minimizes
mistakes by coders.
o For example
Yes (or positive response) code – Y or 1
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No (or negative response) code – N or 2
Don't know code – D or 8
No response/unknown code – U or 9
sample of (say 20) questionnaires.
o You may group similar types of responses into single categories, so as to limit their
number to at most 6 or 7.
o If there are too many categories it is difficult to analyse the data.
early in the study
STEP 4: Summarizing the Data in Data Master Sheets (30 Minutes)
Data master sheets
On a data master sheet all the answers of individual respondents are entered by hand. You
would need several sheets to include all answers.
Enter the different codes for one question in one column instead of having different
columns of which you tick one
No Education (Q3) Occupation (Q4)
Y/N Highest level Still in Self Head HH
school
Years Type
1 Y 4 PS N 1 3
2 Y 9 SS N 4 NA
3 N NA NA NA 5 NA
4 U PS Y 0 2
In any small-scale study processed by hand in which groups will be compared, a different
master sheet should be made for each of those groups, e.g., good and poor compliers to
treatment.
As gender is an important cross-cutting theme, it is usually also advisable to subdivide
males and females within each of the groups that are being compared
STEP 5: Compiling the Data Manually without Master Sheets (35 Minutes)
more efficient to do the compilation manually
o If only one person is doing the compilation use manual sorting.
o If a team of 2 persons work together use either manual sorting or tally counting.
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entered in a separate questionnaire.
o Take one question at a time, for example, ‗use of health facility‘,
o Sort the questionnaires into different piles representing the various responses to the
o Count the number in each pile.
o One member of the compiling team reads out the information while the other records
o Tally count for no more than two variables at one time (e.g., sex plus type of facility
used)
o After tally counting, add the tallies and record the number of subjects in each group.
in each question to make sure that there has been no omission or double count
analysis when the relationship between two or three variables needs to be established, or
details analyzed
STEP 6: Key Points (5 minutes)
sheet, to facilitate data analysis
essential to develop a coding system
compilation manually
STEP 7: Evaluation (5 minutes)
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References
Hardon A, Boonmongkon P, and Streefland P. et al (2001). Applied Health research,
Anthropology of health and health care, (3rd Ed) Amsterdam, The Netherlands: Het
Spinhuis Publishers
Beaglehole R, Bonita R and Kjellstrom (1993) Basic epidemiology: Geneva, Switzerland:
World Health Organization,
Kothari C.R (1985). Research Methodology – Methods and techniques, (2nd ed); New Delhi,
India; Wiley Eastern Limited
Stewart A (2001). Basic Statistics and epidemiology, A practical guide,; London, United
Kingdom: Radcliffe Medical Press,
Varkevisser, C. M, Pathmanathan, I and Brownlee, A (1991). Designing and Conducting
Health Systems Research Projects, Vol. 2 Part I: Ottawa, Canada: IDRC
Polit, D. F and Beck, C. T (2004). Nursing Research – Principles and Methods, (7th Ed):
Philadelphi, USA: Lippincott Williams & Wilkins,
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