Transcript Data analysis - WHO archives

Promoting Rational Drug Use in the
Community
Data analysis
Objectives: Session on Analysis
 Describe in what ways quantitative and
qualitative data can be processed
 Describe how quantitative and qualitative
data can best be analysed
 Understand the differences between
analysis of quantitative and qualitative data
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Why plan for data-processing and
analysis?
 To make sure that all data needed to
answer research questions are indeed
collected
 To avoid collecting superfluous data
 To make sure you plan enough time and
resources for processing and analysis
 To make sure your research tools are
adequate and easily processed
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How to plan for data-processing and
analysis?
 Review research questions and data-collection
tools
 Decide how you want to present data:
- qualitative: as texts
- quantitative: as numbers
 Make a list of variables for quantitative analysis
 Decide on key drug use measures/indicators
 Make dummy tables
 Decide on data-master sheets for analysis of
quantitative data
 Make a list of key themes for qualitative analysis
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Processing of quantitative data
 Check if each questionnaire/interview form is
complete
 Sort data according to study populations (e.g.
women – men; intervention community – control
community)
 Review all responses to categorical variables and
refine the list of values for the categorical
variable (you may need to add values you had not
foreseen)
 Assign codes to responses in
questionnaires/interview forms
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Variables:
 Are defined as characteristics of persons or
objects which can take on different values
 Categorical variables are expressed in
words/categories
 Numerical variables are expressed in numbers
 When planning for analysis of quantitative data,
make a list of all variables and their values
 Assign codes to categorical variables
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Analysis of quantitative data
 Summarise data on data master sheet
 Determine missing values
 Check data master sheet for
consistency/mistakes
 Calculate drug use measures/indicators
 Make relevant frequency distributions
 Fill in tables
 Do statistical tests to test hypothesis on
associations between variables
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Examples of drug use measures
 Percentage of illness episodes not treated
 Percentage of illness episodes treated with
traditional medicines
 Percentage of illness episodes treated on
health worker advice
 Percentage of illness episodes treated in
self-care with medicines
 Percentage of fever episodes treated with
chloroquine
 Percentage of diarrhoea self-medicated
with antibiotics
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Examples of frequency distributions
as way of presenting data:
 Ten most commonly used medicines:
calculated as relative percentage of total
medications used
 Main sources of medicines, calculated as
the number of times medications are
obtained from specific sources divided by
total number of medications
 Five most commonly used medicines for
diarrhoea, expressed as percentage of
total number of medications used to treat
diarrhoea.
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Example of an illness master sheet
Number
respondent
Illness Treatment
describe
Y/N
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advice
Y/N
Drugs
used?
Y/N
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Trad Med
used?
Y/N
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Example of a medicine master sheet
Number
respondent
Drug
name
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Promoting rational drug use in the community
Generic
content
Illness
for which
it is used
Source
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Activity 1
 Review the two data master-sheets in
pairs
 Are any data missing: if yes, how will you
deal with it? Delete the record?
 How can you check if mistakes have been
made during data-entry?
 Have mistakes been made?
 Is the data master-sheet well-designed?
 How could the data master-sheets be
improved?
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Activity 2
 The data in the master-sheet allow for a
comparison between men and women of
types of drugs taken to the PRDUC course
 Design a dummy table to present the data
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Processing of qualitative data
 Expand notes/transcribe tapes everyday
 Add comments on non-verbal
communication
 Order data by type/group of informants
 Read notes/transcriptions, read again
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Qualitative analysis: an ongoing
process
 Read your notes, reflect, reflect more
 Review your research questions: have they been
answered: what do you still need to ask?
 What unexpected issues/problems emerged?
 Do you have sufficient data for each question; can
you triangulate? Are there inconsistencies in data:
do interviews confirm your observations or not?
 Write down preliminary conclusions and queries
 Go back to your informants: probe, ask them to
explain and respond to your preliminary
conclusions.
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Rapid qualitative analysis
 Review your list of themes for qualitative analysis,
read your notes and find out if new issues
emerged
 Make matrices to summarise the data by theme.
 Check if you have data on all your research
questions
 Beware of generalising: your data are not
representative.
 Describe your study population using key
demographic variables (age, marital status, etc.)
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Analysis of textual data
Make a list of codes
Apply codes to texts
Add codes as you go along
Make analytical notes on the relation
between factors; how things work
 Make methodological notes: observations
on how the methods influenced the results;
ideas on new questions to ask
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Coding of transcripts
 Typ-fev
 Type of fever
 Cause-fev
 Cause of fever
 Tx-fev
 Treatment of fever
 P.eff-Tx
 Perceived efficacy
treatment
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Summarizing qualitative data
 Matrix
 Flow charts
 Diagrams
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Example of an illness matrix
Type of
fever
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Signs and
symptoms
Treatment
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Example of a treatment matrix
Type of
treatment
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Perceived
effect
Perceived
side-effect
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Example of a medicine source matrix
Source of
medicines
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Promoting rational drug use in the community
Perceived
advantages
Perceived
disadvantages
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Example of a flow chart
Occurrence
of an illness
Perception of
cause
Choice of
therapy
Evaluation of
efficacy
Determination
if hiyang
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Drawing and verifying conclusions
Continuous process, based on:
 Summary of data
 Identifying trends
 Identifying associations - causations
 Consider confounding factors
 Validation in group and individual
discussions with informants
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Cite your informants to illustrate
 Select case-histories which are typical and
illustrate findings
 Use quotes to illustrate findings
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Strategies to confirm findings
Check for representativeness
Check for observer bias
Use multi-method
Compare and contrast data
Do additional research, include surveys to
test hypothesis
 Get feedback from communities and key
informants
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Activity 3
Community sub-groups:
 Review the illness-recall data in the SSI
forms.
 If you had collected 20 of such illnessrecalls: how can you summarize these data
in one or two data master-sheet(s)?
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Activity 3
Health institution sub-groups
 Review the simulated client visit guidelines.
 If you had done 20 such visits, how could
you have summarized the data in a datamaster-sheet?
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