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Going after the data Data collection instruments FETP India.

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Presentation on theme: "Going after the data Data collection instruments FETP India."— Presentation transcript:

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 Consolidation of the instrument

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 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 Cluster 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 treatment because 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? 1.Hospital 2.Public clinic 3.Private clinic 4.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 Data elements needed Choice of data collection method Formulation of individual items Consolidation of the instrument DANGER: By pass leads to poor studies

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

51 Additional resources on data collection instruments Case study on protocol writing (Scrub Typhus in Darjeeling, Volume 2) Example of questionnaire Guide to common errors in data collection instruments (with checklist)


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