1 / 49

from analysis plan to data collection

from analysis plan to data collection. Helen Maguire acknowledgements Katharina Alpers, Yvan Hutin. its logical: data collection follows the analysis plan. Research question: ? Risk factors for leptospirosis. Study objectives: Estimate association between water exposure and disease.

stasia
Télécharger la présentation

from analysis plan to data collection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. from analysis plan to data collection Helen Maguire acknowledgements Katharina Alpers, Yvan Hutin

  2. its logical: data collection follows 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

  3. data quality • reliability • accuracy Data quality

  4. data quality • reliability • reproducibility/repeatability/precision • ability of a measurement to give the same result or similar result with repeated measurements of the same thing • refers to stability or consistency of information • accuracy • ability of a measurement to be correct on the average Data quality

  5. reliability and accuracy Data quality

  6. reliability and accuracy • Reliable • Accurate • Reliable • Not accurate • Not reliable • Accurate • Not reliable • Not accurate Data quality

  7. some essentials • question-by-question guide • train staff who collect data • standardize the data collection procedure • control instruments or specimen collection • validate Data collection

  8. 1 question by question guide(q-by-q) • short document • each question/data item – item by item • guidance as to how the data should be collected • derived variables explained Data collection

  9. 2 train field workers • select good, experienced field workers • walk through q-by-q guide • explain where data is • clarify how to record it • simulate interviews with team Data collection

  10. 3 standardize data collection • interviewers • work in teams • resolve issues in the whole group • instruments/specimens/samples • calibration, standardization, packaging, media, transport Data collection

  11. 4 control instruments • team checks instrument/samples before leaving • all take responsibility for the instrument: • names and signatures • investigator checks instruments/samples as they come Data collection

  12. 5 validatehow would you validate/verify?

  13. example - triangulation to estimate the proportion of blood units screened for HIV (internal validation) • interview laboratory manager • ? what is the number of units screened • observe practices of the laboratory technician • structured observation guide • ? proportion of units tested • review of registers - ? a sample • proforma • ? number of tests ordered, performed Instruments

  14. data might include • facts • individual characteristics • height, age, income • environment • housing, family size • behaviours, practices • alcohol or tobacco consumption • judgements – attitudes/opinions • indicators of socio-economic status … • blood test results • environmental samples Instruments

  15. list some ways to collect data Instruments

  16. proformas • clinical records • surveillance records • registers • questionnaires • sampling /laboratory results • other data • socio-economic status derived from postcode –via linkages • denominator data • reference data

  17. checking the instrument(s) against the analysis plan • make sure you can collect what you need for each variable /indicator • suppress unnecessary data collection or questions at interview • those that do not be used in the analysis Production of the instrument

  18. is a questionnaire good?why? • list 5 advantages and 5 disadvantages

  19. advantages of questionnaires • can reach a large number of people • relatively easy and economic • relate directly to study question • provide quantifiable answers • relatively easy to analyse

  20. possible disadvantage of questionnaire • bias …? • how might it be introduced at this stage? • how would you avoid it? • pilot - check for leading questions

  21. how to reduce bias • structured questionnaire • ensure high response rate • random choice of interview partners (next birthday) • train interviewers

  22. disadvantages of questionnaires • provide only limited insight into a problem • the range of possible responses is limited • the question maybe misleading Unclear question can lead to • misunderstanding • misinterpretation • do not allow for mistakes • must be right from the beginning • missing data hard to chase how to avoid ?

  23. pilot testing of the questionnaire • check that the questionnaire is: • clear • understandable • acceptable • check flow and skip pattern • check coding • estimate time needed Production of the instrument

  24. how would you administer a questionnaire?

  25. questionnaires • internet/email/post self completion • interviewer-administered • face to face • telephone

  26. what makes a well designed questionnaire?

  27. what makes a well designed questionnaire? • good appearance (easy for the eye) • short and simple • numbering / flow /sign-posting /instructions/where to return and how • relevant and logical

  28. introduction • covering letter/ interview introduction • Who are you / you work for • Why are you investigating • Where did you obtain the respondent’s name • How and where can you be contacted • Guarantee of confidentiality • Length of interview (be honest) ⇒ Usefulness of study should be clear to all respondents

  29. Good morning , My name is Katharina Alpers .... , I work for …….. You may have been already informed that a survey on risk factors for being stung by a jellyfish will be done this week in Mahon. This study has been approved by the Spanish national ethical committee. Only anonymous data will be analysed. You have been randomly selected to participate in this study. Your participation is voluntary. The interview is about 10 minutes long. Are you able to help us? thanks so much .....

  30. questions do you like to go swimming and do you mind being stung by jellyfish?  Yes  No

  31. what is the jellyfish situation?  Good  Bad versus how often did you see jellyfish during the last week?  Once  Twice  Three times or more  Never • Don´t know

  32. did you see more than an average of 33 jellyfish/m2 salt water surface on more than 3 occasions that you went swimming in the morning last week?  Yes  No versus have you seen jellyfish on more than 3 mornings last week?  Yes  No  Don´t know

  33. main question formats • closed format  forced choice Yes  Always  No  Sometimes  Don’t know  Never  • open format  free text What did you do to avoid being stung by jellyfish? Please describe :__________________________________________________________________________________

  34. when would open questions be good ? • what problems might there be with open questions?

  35. advantages of open questions • exploration possible – to generate hypotheses • useful for exploring knowledge and attitudes • qualitative research • focus groups • trawling questionnaires

  36. disadvantages of open questions • interviewer bias • time-consuming • coding problems • difficult to analyse • difficult to compare groups

  37. advantages of closed questions • simple • less discrimination against less verbally expressive people • easy to code, record, analyse • easy to compare

  38. disadvantages of closed questions • restricted number of possible answers • possible loss of additional information Compromise if yes specify : __________”

  39. checklist which of the following beaches have you visited during your stay in Menorca? Lazareto beach  Yes  No  Don´t know Cala’n Porter  Yes  No  Don´t know Rafalet  Yes  No  Don´t know Macarella  Yes  No  Don´t know Sa Mesquida  Yes  No  Don´t know

  40. rating scale how often did you see jellyfish during the past week? Always Sometimes Seldom Never Mornings     Lunchtime     Evenings    

  41. rating scale numerical how severe was your pain after you were stung? (please circle) 1 2 3 4 5 6 7 Not painful at all Very painful analogue how severe is your pain (put the tick on the line) 0 10

  42. Likert Scale Rensis Likert, 1903-1981 Five (or more) ordered response levels Jellyfish also have the right to swim in the Mediterranean sea  I strongly disagree I disagree  I neither agree or disagree  I agree  I strongly agree

  43. problems and pitfalls • avoid questions that ask two things at once - you won’t know which part people are answering: have you seen or been stung by jellyfish? • ambiguity..... do you swim a lot?

  44. problems and pitfalls • avoid jargon/abbreviations/slang should jellyfish sting victims receive PEP? (post exposure prophylaxis) • avoid not mutually exclusive options What is your age ? 16-20  20-25  25-30  35-40

  45. summary a well designed questionnaire: • helps you answer your research question • minimises potential sources of bias -> increases the validity of the replies • is more likely be completed

  46. questionnaire validation • use or adapt existing questionnaires • validated • new questionnaires • need to be tested (pilot)

  47. conclusion • don’t forget to thank the interviewed persons • tell them when the results will be available and where

  48. take home messages • think instruments, data sources, not only questionnaire • list your indicators • prepare your variables ->indicators • prepare dummy tables • polish, polish and polish to ensure good data quality

  49. thanks for your attention

More Related