1. Promoting Rational Drug Use in the Community Data analysis

2. WHO Data analysis Promoting rational drug use in the community 2 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

3. WHO Data analysis Promoting rational drug use in the community 3 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

4. WHO Data analysis Promoting rational drug use in the community 4 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

5. WHO Data analysis Promoting rational drug use in the community 5 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

6. WHO Data analysis Promoting rational drug use in the community 6 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

7. WHO Data analysis Promoting rational drug use in the community 7 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

8. WHO Data analysis Promoting rational drug use in the community 8 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

9. WHO Data analysis Promoting rational drug use in the community 9 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.

10. WHO Data analysis Promoting rational drug use in the community 10 Example of an illness master sheet

11. WHO Data analysis Promoting rational drug use in the community 11 Example of a medicine master sheet

12. WHO Data analysis Promoting rational drug use in the community 12 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?

13. WHO Data analysis Promoting rational drug use in the community 13 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

14. WHO Data analysis Promoting rational drug use in the community 14 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

15. WHO Data analysis Promoting rational drug use in the community 15 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.

16. WHO Data analysis Promoting rational drug use in the community 16 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.)

17. WHO Data analysis Promoting rational drug use in the community 17 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

18. WHO Data analysis Promoting rational drug use in the community 18 Coding of transcripts  Typ-fev  Cause-fev  Tx-fev  P.eff-Tx  Type of fever  Cause of fever  Treatment of fever  Perceived efficacy treatment

19. WHO Data analysis Promoting rational drug use in the community 19 Summarizing qualitative data  Matrix  Flow charts  Diagrams

20. WHO Data analysis Promoting rational drug use in the community 20 Example of an illness matrix Type of fever Signs and symptoms Treatment

21. WHO Data analysis Promoting rational drug use in the community 21 Example of a treatment matrix Perceived side-effect Perceived effect Type of treatment

22. WHO Data analysis Promoting rational drug use in the community 22 Example of a medicine source matrix Perceived disadvantages Perceived advantages Source of medicines

23. WHO Data analysis Promoting rational drug use in the community 23 Example of a flow chart

24. WHO Data analysis Promoting rational drug use in the community 24 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

25. WHO Data analysis Promoting rational drug use in the community 25 Cite your informants to illustrate  Select case-histories which are typical and illustrate findings  Use quotes to illustrate findings

26. WHO Data analysis Promoting rational drug use in the community 26 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

27. WHO Data analysis Promoting rational drug use in the community 27 Activity 3 Community sub-groups:  Review the illness-recall data in the SSI forms.  If you had collected 20 of such illness- recalls: how can you summarize these data in one or two data master-sheet(s)?

28. WHO Data analysis Promoting rational drug use in the community 28 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 data- master-sheet?