Going after the data

1. Going after the data Data collection instruments FETP India

2. Competency to be gained from this lecture Design effective data collection instruments

3. Key elements • Instruments • Items • Finalization

4. The data collection instrument is a logical deduction of the analysis plan Research question:? Risk factors for leptospirosis Study objectives:Estimate association between water exposure and disease Design/ indicator:Case control Odds ratio Analysis plan:Dummy table Data elements Needed:? Water exposure ? Sick Data collection: Interview Individual items:? Swam in water ? Sick Consolidationof theinstrument

5. Information that may be collected with a data collection instrument • Facts • Judgements • Indicators of knowledge Instruments

6. Information that may be collected with a data collection instrument • Facts • Individual characteristics • Height, age, income • Environment • Housing, family size • Behaviours, practices • Alcohol or tobacco consumption • Judgements • Indicators of knowledge Instruments

7. Information that may be collected with a data collection instrument • Facts • Judgements • Opinions • Attitudes • Indicators of knowledge Instruments

8. Information that may be collected with a data collection instrument • Facts • Judgements • Indicators of knowledge • Risk factors • Elements of healthy lifestyle Instruments

9. Classical way to explore behaviours and their determinants in epidemiology • Knowledge • Attitude • Practices Instruments

10. Different ways to collect data with an instrument • Abstraction form • Clinical records • Surveillance records • Registers • Structured observation guide • Questionnaire Instruments

11. Triangulation to reconstitute the best possible reflection of the truth • Collection of information on the same topic through various mechanisms • Attempt to reconstitute a reliable reflection of the parameter Instruments

12. Examples of triangulation to estimate the proportion of blood units screened for HIV • Interview of the laboratory manager • Questionnaire • ? What is the number of units screened • Observation of the practices of the laboratory technician • Structured observation guide • ? Proportion of units tested • Review of registers • Abstraction form • ? Number of tests ordered, used Instruments

13. The four components of a data collection instrument • Introduction and conclusion • Identifiers • Instructions for the person who collects data • Body of the instrument Items

14. The four components of a data collection instrument • Introduction and conclusion • Introduction • Presentation, objectives • Elements needed for informed consent • Conclusion • Identifiers • Instructions for the person who collects data • Body of the instrument Items

15. The four components of a data collection instrument Cluster • Introduction and conclusion • Identifiers • Exact identifiers (e.g., name, address) • Collect and keep apart • Not entered in the computer • Coded ID number (composite) • Entered in the computer • Instructions for the person who collects data • Body of the instrument House Person Items

16. The four components of a data collection instrument • Introduction and conclusion • Identifiers • Instructions for the person who collects data • Guide for the person who collects data • Instructions (e.g., prompts) • Skip patterns • Use different fonts (e.g., italics) • Body of the instrument Items

17. The four components of a data collection instrument • Introduction and conclusion • Identifiers • Instructions for the person who collects data • Body of the instrument • Open items • Closed items • Semi-open items Items

18. Different types of items in the body of a questionnaire • Open questions • The interviewer leaves the answer free • Closed questions • The interviewer proposes options of answers • Semi-open questions • The interviewer proposes options of answers, but additional free answers are possible Items

19. Open questions • Answers are not suggested • Subjects must generate an answer • Advantages • Give freedom of response • Stimulate memory • Can be useful to generate closed responses later • Useful at a hypothesis raising stage • Inconvenient • Difficult to code and analyze • May be incomplete and / or unfocused Items

20. Examples of open questions • What disease can you acquire from tobacco? • What places did you eat at in the week preceding the disease? Items

21. Open questions with close ended answers • No option of answer is suggested • However, among the answers freely mentioned, the interviewer will tick those spontaneously specified • Expressed as an open question • Analyzed as a close-ended question Items

22. Example of open question with close ended answers • What are the practices that may increase your risk to get a heart attack? (DO NOT propose any option of answer) • Lack of exercise (Yes/No) • Smoking (Yes/No) • Poor dietary practices (Yes/No) • Eating too much salt (Yes/No) Items

23. Closed questions:1. Dichotomous options • Suggested answers include “Yes” and “no” • Advantages • Forces a clear position • May be useful for key, important, well framed issues • Inconvenient • May oversimplifies issues Items

24. Good and bad examples of closed dichotomous questions • Have you ever consumed tobacco products? • A dichotomous question here is likely to over-simplify, unless it is used as an introduction • Did you eat at restaurant X between 1 and 28 February? • Adapted to an outbreak investigation Items

25. Closed questions:2. Multiple options • Multiple options of answers are suggested • Advantage • Larger choice of answer options • Inconvenient • May be difficult to choose only one option Items

26. Examples of closed questions with multiple options • Where do you go to seek treatment when moderately sick? (e.g., for fever) • Hospital • Public clinic • Private clinic • Pharmacist • Do you wear a helmet when riding a bike? • Always • Sometimes • Never Items

27. Differentiating questions with multiple options from multiple dichotomous questions • If more than one option of response, be clear as to whether one or multiple answers are acceptable • Only one answer acceptable • One variable with multiple options • More than one answer acceptable • Equivalent to multiple dichotomous variables Items

28. Example of question with multiple options that lead to ambiguities • What are the elements that led you to stop smoking? • Fear of the danger of tobacco • Diagnosis of a tobacco related illness • Fear of dependence • Cost of tobacco products • Two possibilities: • Accept only one answer • Accept multiple answers Items

29. Possibility 1: More than one option acceptable • What are the elements that led you to stop smoking? • Fear of the danger of tobacco • Diagnosis of a tobacco related illness • Fear of dependence • Cost of tobacco products • Equivalent to multiple dichotomous questions, each option being a variable Items

30. Clarified possibility 1: More than one option acceptable • Among these elements, what are those that led you to stop smoking? • Fear of the danger of tobacco • Yes / No • Diagnosis of a tobacco related illness • Yes / No • Fear of dependence • Yes / No • Cost of tobacco product • Yes / No Items

31. Possibility 2: Only than one option acceptable • What are the elements that led you to stop smoking? • Fear of the danger of tobacco • Diagnosis of a tobacco related illness • Fear of dependence • Cost of tobacco products • Equivalent to one question with multiple options of answers, one variable Items

32. Clarified possibility 2: Only than one option acceptable • Among these elements, what is the one that was most important in your decision to stop smoking? • Fear of the danger of tobacco • Diagnosis of a tobacco related illness • Fear of dependence • Cost of tobacco products Items

33. Closed questions:3. Quantitative answers • The subject must provide a quantified answer • Advantage • Allows creation of continuous variables • Inconvenient • May requires validation: • Some “quantified” answers might be limited in the way they can be handled as continuous variables Items

34. Example of closed questions with quantitative answers • How many time did you visit the clinic in the last 12 months? • True continuous variable • Four visits is the double of two visits • How would you describe your pain on a 1-10 scale where 1 would be the minimum and 10 would be the maximum? • In fact a qualitative variable with 10 options • Requires validation • Six may not be the double of three on the scale Items

35. Semi-open questions • Suggested answers • Possibility to create another answer • Other, specify: __________ • Advantage • Leaves the door open to unplanned answers • Inconvenient • Difficult to analyze Items

36. Examples of semi-open questions • Did you child have complication following measles? • None • Pneumonia • Diarrhoea • Eye problems • Other, specify: ______________ Items

37. Formulating questions (1/2) • Write short and precise questions • Avoid ambiguities • Use simple words of every day language • Avoid negations and double negations • Do you sometimes care for patients without washing hands? • Do you systematically wash hands before caring for each patient? Production of the instrument

38. Formulating questions (2/2) • Ask only one question at the time • Did you refuse treatmentbecause you feared side effects? • Did you refuse treatment? • If yes, was this because you feared side effects? • Be specific • Are you aware of the modes of transmission of HIV? • Among these practices, can you tell me those that could lead to HIV? • Use neutral tone to avoid influence • Have you been promiscuous in the last six months? • How many partners have you had in the last six months? Production of the instrument

39. Sorting questions • From the general to the specific • From the simple to the complicated • From the casual to the intimate • Regroup identification questions at the beginning or at the end • Introduce simple questions as a break if the questionnaire is complex • Triangulate through multiple questions on the same topic if the subject is important Production of the instrument

40. Careful lay out the data collection instrument: Rationale • Easier to use • Guides the field worker • Reduces the risk of errors • Reduces the risk of forgotten questions • Simplifies coding • Simplifies data entry Production of the instrument

41. Careful laying out the data collection instrument: Principles • Split the sections • Space out questions • Use larger fonts • Align answers on the right hand side • Do not split questions across pages • Number questions • Standardize coding • Use auto-coding procedures Production of the instrument

42. Auto-coding • Q.25: Where did you go when your child had diarrhoea? • Hospital • Public clinic • Private clinic • Pharmacist 2 Production of the instrument

43. Checking the instrument against the analysis plan • Suppress unnecessary questions • Those that do not be used in the analysis • Add missing questions • Those that will provide variables needed in the analysis Production of the instrument

44. Colleagues who can help in reviewing the questionnaire • Colleagues • Experts • Statisticians (Coding) • Field workers • Data entry clerks Production of the instrument

45. Language • All questionnaires must be written in the language in which they will be administered • Not acceptable to have an English questionnaire translated in the field by the interviewers • No standardization • Translation is required, with quality assurance • Initial formulation (e.g., in English) • Translation (e.g., in Hindi) • Back-translation (e.g., back to English) Production of the instrument

46. Objectives of the pilot testing of the questionnaire • Check that the questionnaire is: • Clear • Understandable • Acceptable • Check flow and skip pattern • Check pertinence of coding • Estimate the time needed to ask all the questions Production of the instrument

47. Pilot testing the questionnaire in practice • Pilot test with yourself • Pilot test with a few volunteers • Pilot test in real size • Persons similar to the study population • Persons who are not to be included in the study Production of the instrument

48. Producing the last version of the questionnaire • Professional finish • Paper of good quality • Interviewer’s kit • Sleeves • Clip board • Pencil, eraser Production of the instrument

49. Summary of the systematic process leading to the data collection instrument Research question Study objectives Design/Indicators Analysis plan DANGER: By pass leads to poor studies Data elements needed Choice of data collection method Formulation of individualitems Consolidationof theinstrument

50. Take home messages • Think instruments, not only questionnaire • Prepare your items as future variables • Polish, polish and polish to ensure good data quality