data collection.ppt
- 1. Introduction • Data Collection Strategies • Characteristics of Good Measures • Quantitative and Qualitative Data • Tools for Collecting Data 1
- 2. Data Collection Strategies • No one best way: decision depends on: – What you need to know: numbers or stories – Where the data reside: environment, files, people – Resources and time available – Complexity of the data to be collected – Frequency of data collection – Intended forms of data analysis 2
- 3. Rules for Collecting Data • Use multiple data collection methods • Use available data, but need to know – how the measures were defined – how the data were collected and cleaned – the extent of missing data – how accuracy of the data was ensured 3
- 4. Rules for Collecting Data • If must collect original data: – be sensitive to burden on others – pre-test, pre-test, pre-test – establish procedures and follow them (protocol) – maintain accurate records of definitions and coding – verify accuracy of coding, data input 4
- 5. Structured Approach • All data collected in the same way • Especially important for multi-site and cluster evaluations so you can compare • Important when you need to make comparisons with alternate interventions 5
- 6. Use Structured Approach When: • need to address extent questions • have a large sample or population • know what needs to be measured • need to show results numerically • need to make comparisons across different sites or interventions 6
- 7. Semi-structured Approach • Systematic and follow general procedures but data are not collected in exactly the same way every time • More open and fluid • Does not follow a rigid script – may ask for more detail – people can tell what they want in their own way 7
- 8. Use Semi-structured Approach when: • conducting exploratory work • seeking understanding, themes, and/or issues • need narratives or stories • want in-depth, rich, “backstage” information • seek to understand results of data that are unexpected 8
- 9. Characteristics of Good Measures • Is the measure relevant? • Is the measure credible? • Is the measure valid? • Is the measure reliable? 9
- 10. Relevance Does the measure capture what matters? Do not measure what is easy instead of what is needed 10
- 11. Credibility Is the measure believable? Will it be viewed as a reasonable and appropriate way to capture the information sought? 11
- 12. Internal Validity How well does the measure capture what it is supposed to? Are waiting lists a valid measure of demand? 12
- 13. Reliability A measure’s precision and stability- extent to which the same result would be obtained with repeated trials How reliable are: – birth weights of newborn infants? – speeds measured by a stopwatch? 13
- 14. Quantitative Approach • Data in numerical form • Data that can be precisely measured – age, cost, length, height, area, volume, weight, speed, time, and temperature • Harder to develop • Easier to analyze 14
- 15. Qualitative Approach • Data that deal with description • Data that can be observed or self-reported, but not always precisely measured • Less structured, easier to develop • Can provide “rich data” — detailed and widely applicable • Is challenging to analyze • Is labor intensive to collect • Usually generates longer reports 15
- 16. Which Data? 16 - do not need to quantify the results - are not sure what you are able to measure Qualitative - want narrative or in-depth information - want to cover a large group - want to be precise - know what you want to measure Quantitative - want to conduct statistical analysis Then Use: If you:
- 17. Obtrusive vs. Unobtrusive Methods Obtrusive data collection methods that directly obtain information from those being evaluated e.g. interviews, surveys, focus groups Unobtrusive data collection methods that do not collect information directly from evaluees e.g., document analysis, GoogleEarth, observation at a distance, trash of the stars 17
- 18. How to Decide on Data Collection Approach • Choice depends on the situation • Each technique is more appropriate in some situations than others • Caution: All techniques are subject to bias 18
- 19. Triangulation to Increase Accuracy of Data • Triangulation of methods – collection of same information using different methods • Triangulation of sources – collection of same information from a variety of sources • Triangulation of evaluators – collection of same information from more than one evaluator 19
- 20. Data Collection Tools • Participatory Methods • Records and Secondary Data • Observation • Surveys and Interviews • Focus Groups • Diaries, Journals, Self-reported Checklists • Expert Judgment • Delphi Technique • Other Tools 20
- 21. Tool 1: Participatory Methods • Involve groups or communities heavily in data collection • Examples: – community meetings – mapping – transect walks 21
- 22. Community Meetings • One of the most common participatory methods • Must be well organized – agree on purpose – establish ground rules • who will speak • time allotted for speakers • format for questions and answers 22
- 23. Mapping • Drawing or using existing maps • Useful tool to involve stakeholders – increases understanding of the community – generates discussions, verifies secondary sources of information, perceived changes • Types of mapping: – natural resources, social, health, individual or civic assets, wealth, land use, demographics 23
- 24. Transect Walks • Evaluator walks around community observing people, surroundings, and resources • Need good observation skills • Walk a transect line through a map of a community — line should go through all zones of the community 24
- 25. Tool 2: Records and Secondary Data • Examples of sources: – files/records – computer data bases – industry or government reports – other reports or prior evaluations – census data and household survey data – electronic mailing lists and discussion groups – documents (budgets, organizational charts, policies and procedures, maps, monitoring reports) – newspapers and television reports 25
- 26. Using Existing Data Sets Key issues: validity, reliability, accuracy, response rates, data dictionaries, and missing data rates 26
- 27. Advantage/Challenge: Available Data Advantages Often less expensive and faster than collecting the original data again Challenges There may be coding errors or other problems. Data may not be exactly what is needed. You may have difficulty getting access. You have to verify validity and reliability of data 27
- 28. Tool 3: Observation • See what is happening – traffic patterns – land use patterns – layout of city and rural areas – quality of housing – condition of roads – conditions of buildings – who goes to a health clinic 28
- 29. Observation is Helpful when: • need direct information • trying to understand ongoing behavior • there is physical evidence, products, or outputs than can be observed • need to provide alternative when other data collection is infeasible or inappropriate 29
- 30. Degree of Structure of Observations • Structured: determine, before the observation, precisely what will be observed before the observation • Unstructured: select the method depending upon the situation with no pre-conceived ideas or a plan on what to observe • Semi-structured: a general idea of what to observe but no specific plan 30
- 31. Google Earth • Maps and satellite images for complex or pinpointed regional searches • Has an Advanced version and an Earth Outreach version • Web site for Google Earth – http://earth.google.com/ 31
- 32. Ways to Record Information from Observations • Observation guide – printed form with space to record • Recording sheet or checklist – Yes/no options; tallies, rating scales • Field notes – least structured, recorded in narrative, descriptive style 32
- 33. Guidelines for Planning Observations • Have more than one observer, if feasible • Train observers so they observe the same things • Pilot test the observation data collection instrument • For less structured approach, have a few key questions in mind 33
- 34. Advantages and Challenges: Observation Advantages Collects data on actual vs. self- reported behavior or perceptions. It is real-time vs. retrospective Challenges Observer bias, potentially unreliable; interpretation and coding challenges; sampling can be a problem; can be labor intensive; low response rates 34
- 35. Tool 4: Surveys and Interviews • Excellent for asking people about: – perceptions, opinions, ideas • Less accurate for measuring behavior • Sample should be representative of the whole • Big problem with response rates 35
- 36. Structures for Surveys • Structured: – Precisely worded with a range of pre-determined responses that the respondent can select – Everyone asked exactly the same questions in exactly the same way, given exactly the same choices • Semi-structured – Asks same general set of questions but answers to the questions are predominantly open-ended 36
- 37. Structured vs. Semi-structured Surveys Structured harder to develop easier to complete easier to analyze more efficient when working with large numbers Semi- structured easier to develop: open ended questions more difficult to complete: burdensome for people to complete as a self-administrated questionnaire harder to analyze but provide a richer source of data, interpretation of open-ended responses subject to bias 37
- 38. Modes of Survey Administration • Telephone surveys • Self-administered questionnaires distributed by mail, e-mail, or websites • Administered questionnaires, common in the development context • In development context, often issues of language and translation 38
- 39. Mail / Phone / Internet Surveys • Literacy issues • Consider accessibility – reliability of postal service – turn-around time • Consider bias – What population segment has telephone access? Internet access? 39
- 40. Advantages and Challenges of Surveys Advantages Best when you want to know what people think, believe, or perceive, only they can tell you that Challenges People may not accurately recall their behavior or may be reluctant to reveal their behavior if it is illegal or stigmatized. What people think they do or say they do is not always the same as what they actually do. 40
- 41. Interviews • In-depth/ detail/ specific topic/ little structure/ researcher wants to know more about the topic Unstructured • Focused interviews/ series of open ended questions/ freedom to follow a line of enquiry Semi structured • Respondents get the same questions/ tight structured/ questions posed/ phrased in order/ limited responses Structured
- 42. Challenges of Interviews • Can be expensive, labor intensive, and time consuming • Selective hearing on the part of the interviewer may miss information that does not conform to pre-existing beliefs • Cultural sensitivity: e.g., gender issues 42
- 43. Tool 5: Focus Groups • Type of qualitative research where small homogenous groups of people are brought together to informally discuss specific topics under the guidance of a moderator • Purpose: to identify issues and themes, not just interesting information, and not “counts” 43
- 44. Focus Groups Are Inappropriate when: • language barriers are insurmountable • evaluator has little control over the situation • trust cannot be established • free expression cannot be ensured • confidentiality cannot be assured 44
- 45. Focus Group Process Phase Action 1 Opening Ice-breaker; explain purpose; ground rules; introductions 2 Warm- up Relate experience; stimulate group interaction; start with least threatening and simplest questions 3 Main body Move to more threatening or sensitive and complex questions; elicit deep responses; connect emergent data to complex, broad participation 4 Closure End with closure-type questions; summarize and refine; present theories, etc; invite final comments or insights; thank participants 45
- 46. Advantages and Challenges of Focus Groups Advantages Can be conducted relatively quickly and easily; may take less staff time than in-depth, in-person interviews; allow flexibility to make changes in process and questions; can explore different perspectives; can be fun Challenges Analysis is time consuming; participants not be representative of population, possibly biasing the data; group may be influenced by moderator or dominant group members 46
- 47. Tool 6: Diaries and Self-Reported Checklists • Use when you want to capture information about events in people’s daily lives • Participants capture experiences in real-time not later in a questionnaire • Used to supplement other data collection 47
- 48. Guidelines for Diaries or Journals 48 Step Process 1 Recruit people face-to-face • encourage participation, appeal to altruism, assure confidentiality, provide incentive 2 Provide a booklet to each participant • cover page with clear instructions, definitions, example • short memory-joggers, explain terms, comments on last page , calendar 3 Consider the time-period for collecting data • if too long, may become burdensome or tedious • if too short may miss the behavior or event
- 49. Self-reported Checklists • Cross between a questionnaire and a diary • The evaluator specifies a list of behaviors or events and asks the respondents to complete the checklist • Done over a period of time to capture the event or behavior • More quantitative approach than diary 49
- 50. Advantages and Challenges of Diaries and Self-reported Checklists Advantages Can capture in-depth, detailed data that might be otherwise forgotten Can collect data on how people use their time Can collect sensitive information Supplements interviews provide richer data Challenges Requires some literacy May change behavior Require commitment and self-discipline Data may be incomplete or inaccurate Poor handwriting, difficult to understand phrases 50
- 51. Tool 7: Expert Judgment Use of experts, one-on- one or as a panel E.g., Government task forces, Advisory Groups Can be structured or unstructured Issues in selecting experts 51
- 52. Selecting Experts • Establish criteria for selecting experts not only on recognition as expert but also based on: – areas of expertise – diverse perspectives – diverse political views – diverse technical expertise 52
- 53. Advantages and Challenges of Expert Judgment Advantages Fast, relatively inexpensive Challenges Weak for impact evaluation May be based mostly on perceptions Value of data depends on how credible the experts are perceived to be 53
- 54. Tool 8: Delphi Technique • Enables experts to engage remotely in a dialogue and reach consensus, often about priorities • Experts asked specific questions; often rank choices • Responses go to a central source, are summarized and fed back to the experts without attribution • Experts can agree or argue with others’ comments • Process may be iterative 54
- 55. Advantages and Challenges of Delphi Technique Advantages Allows participants to remain anonymous Is inexpensive Is free of social pressure, personality influence, and individual dominance Is conducive to independent thinking Allows sharing of information Challenges May not be representative Has tendency to eliminate extreme positions Requires skill in written communication Requires time and participant commitment 55
- 56. Other Measurement Tools - scales (weight) - tape measure - stop watches - chemical tests : i.e. quality of water - health testing tools: i.e. blood pressure - aptitude and achievement tests -citizen report cards 56
- 57. Data Collection Summary Choose more than one data collection technique No “best” tool Do not let the tool drive your work but rather choose the right tool to address the evaluation question 57