1. Collecting High Quality Outcome Data: Part 1 Collecting High Quality Outcome Data Copyright © 2012 by JBS International, Inc. Developed by JBS International for the Corporation for National & Community Service

2. Collecting High Quality Outcome Data: Part 1 Learning Objectives By the end of this module, you will be able to: Recognize the benefits of collecting high-quality data Use theory of change to think about measurement Identify and evaluate merits of data sources and instruments Describe some uses of data collection methods, and evaluate their merits Describe steps to implement data collection Recognize data quality 2

3. Collecting High Quality Outcome Data: Part 1 What Do We Mean By Data? Data: Information collected to answer a measurement question, also known as evidence Data collection occurs as a planned process that involves recording information in a consistent way Instruments aid in collecting consistent data 3

4. Collecting High Quality Outcome Data: Part 1 Ensuring Data Quality: Reliability, Validity, Bias Reliability is the ability of a method or instrument to yield consistent results under the same conditions. Validity is the ability of a method or instrument to measure accurately. Bias involves systematic distortion of results stemming from how data are collected and how instruments are designed. 4

5. Collecting High Quality Outcome Data: Part 1 Benefits of Collecting High-quality Data Sound basis for decision making Improve service quality and service outcomes Increase accountability Tell story of program achievements 5

6. Collecting High Quality Outcome Data: Part 1 Measurement Question Implied by Theory of Change 6 Intended Outcome Students improve attitudes towards school. Intended Outcome Students improve attitudes towards school. Community Problem/Need Students with poor attitudes towards school at risk of failing academically. Community Problem/Need Students with poor attitudes towards school at risk of failing academically. Specific Intervention Individualized mentoring to promote positive attitudes towards school. Specific Intervention Individualized mentoring to promote positive attitudes towards school. "Did students in the mentoring program improve their attitudes towards school?"

7. Collecting High Quality Outcome Data: Part 1 Identifying a Data Source Data source: The person, group or organization that has information to answer the measurement question Identify possible data sources; list pros and cons of each Identify a preferred data source; consider its accessibility Alternative data sources: consider if they can give you same or comparable data 7

8. Collecting High Quality Outcome Data: Part 1 Data source and type of outcome Depends partly on the type of change you want to measure - attitude, knowledge, behavior, or conditions. Data on changes in attitudes or knowledge usually come directly from persons experiencing these changes. Data on changes in behavior or conditions can come from either persons experiencing these changes or from other observers. 8 AttitudeKnowledge BehaviorCondition

9. Collecting High Quality Outcome Data: Part 1 “How did mentored students’ feelings towards teachers change over time?” ProsCons Students In best position to describe how they feel about their teachers May not be open about their feelings towards teachers Teachers May know how students feel towards them May not know how students feel about other teachers May only spend one class period with students Mentors May know how students feel about a wide range of issues, including teachers Depends on students’ willingness to share feelings with mentors Students and mentors may not discuss this issue much Comparing Data Sources 9

10. Collecting High Quality Outcome Data: Part 1 Method: Process or Steps Taken to Systematically Collect Data SurveyWritten questionnaire completed by respondent Interview Interviewer poses questions and records responses; face-to-face or via telephone Observation Observer records behavior or conditions using via checklist or other form Standardized Test Used to assess knowledge of academic subjects (reading, math, etc.) Next, Consider Choice of Methods 10

11. Collecting High Quality Outcome Data: Part 1 Method: Process or Steps Taken to Systematically Collect Data Tracking Sheet Used to document service delivery; used primarily to track outputs Focus Group Facilitator leads small group through discussion in- depth discussion of topic or issue Diaries, Journals Respondent periodically (daily) records information about his/her activities or experiences Secondary Data Using data gathered by other agencies that can be used to assess program performance Consider Choice of Methods (continued) 11

12. Collecting High Quality Outcome Data: Part 1 Method and Outcomes Type— Attitude and Knowledge Attitude/BeliefKnowledge/Skill DefinitionThoughts, feelings Understanding, know-how Examples Attachment to school (academic engagement) Becoming a better reader Generally Preferred Data Source/Method Student: Survey or interview Learner: Standardized test* * Use of standardized tests is mandated for certain performance measures in the Education Focus Area. Other types of knowledge (e.g., financial literacy) can be measured using other types methods. 12

13. Collecting High Quality Outcome Data: Part 1 Method and outcome type— behavior and condition BehaviorCondition/Status DefinitionAction, conduct, habits Situation or circumstances Examples Exercising more frequently Improving stream banks Generally Preferred Data Source/Method Beneficiary: Exercise log Land manager: Observation checklist or rubric 13

14. Collecting High Quality Outcome Data: Part 1 Where to Find Instruments For CNCS priorities and performance measures, look for instruments by goal and focus area Go to http://www.nationalservice.gov/resources/npm/hom e http://www.nationalservice.gov/resources/npm/hom e Programs and projects can look anywhere they like to find instruments: Use Internet search engines Talk to others within you professional network to find out what they are using Look at evidence for intervention – how measured before? 14

15. Collecting High Quality Outcome Data: Part 1 Evaluating Instruments Pre-post measurement is preferable to post-only Can the instrument measure the outcome? Appropriate for your intervention? Appropriate for your beneficiaries? How many questions measure the outcome? Single question  low-quality data Series of questions: Too long or complex? Instrument should not exceed 2 pages Do questions cover all relevant aspects of your intervention? Can questions not specific to your intervention be removed? 15

16. Collecting High Quality Outcome Data: Part 1 Define Outcome Dimensions Outcome Dimensions: The main aspects, features, or characteristics that define an outcome and that should be taken into account for measurement to be valid Example: Increased attachment to school: Feelings about being in school Feelings about doing school work Feelings towards teachers Feelings towards students 16

17. Collecting High Quality Outcome Data: Part 1 Example: Dimensions of Attachment to School a.Feelings about being in school b.Feelings about doing school work c.Relations with other students d.Relations with teachers a a b b c c 17 d d a b c d

18. Collecting High Quality Outcome Data: Part 1 Summary: Identifying Outcome Dimensions National performance measures: look at performance measurement instructions Look at your theory of change Talk to stakeholders and program staff Build up a list of dimensions; look for repeated themes 18 Evidence Guides choice of intervention Supports cause-effect relationship Evidence Guides choice of intervention Supports cause-effect relationship Community Problem/Need Specific Intervention Intended Outcome

19. Collecting High Quality Outcome Data: Part 1 Instrument Design Issues Crowded layout Double-barreled questions Biased or “leading” questions Questions that are too abstract Questions that use unstructured responses inappropriately Response options that overlap or contain gaps Unbalanced scales 19

20. Collecting High Quality Outcome Data: Part 1 Crowded Layout 20 Problem: Crowded layout Most of the time, how do you feel about doing homework? ☐ I usually hate doing homework ☐ I usually don’t like doing homework ☐ I usually like doing homework ☐ I usually love doing homework Solution: Don’t use crowded layouts Most of the time, how do you feel about doing homework? ☐ I usually hate doing homework ☐ I usually don’t like doing homework ☐ I usually like doing homework ☐ I usually love doing homework

21. Collecting High Quality Outcome Data: Part 1 Double-barreled Question 21 They strongly like it ☐ They like it ☐ They are undecided ☐ They dislike it ☐ They strongly dislike it ☐ Problem: Asking two questions in one How do teachers and students at your school feel about the mentoring program? Solution: Break out questions separately How do teachers at your school feel about the mentoring program? How do students at your school feel about the mentoring program? They strongly like it ☐ They like it ☐ They are undecided ☐ They dislike it ☐ They strongly dislike it ☐ They strongly like it ☐ They like it ☐ They are undecided ☐ They dislike it ☐ They strongly dislike it ☐

22. Collecting High Quality Outcome Data: Part 1 Biased or “Leading” Question 22 Problem: Biased or “leading” questions Has the mentoring program improved how you feel about going to school? ☐ Yes ☐ No ☐ No opinion Solution: Use neutral questions How has the mentoring program affected how you feel about going to school? ☐ I feel better about going to school. ☐ I feel worse about going to school. ☐ I feel about the same about going to school. ☐ No opinion

23. Collecting High Quality Outcome Data: Part 1 Abstract or Broad Question 23 Problem: Questions are too abstract or broad. Did you enjoy the mentoring program?  Yes  No  Not Sure Solution: Make questions more concrete and specific. Would you recommend the mentoring program to other students?  Yes  No  Not Sure

24. Collecting High Quality Outcome Data: Part 1 Not Using Structured Responses 24 Problem: Using unstructured responses when structured responses are appropriate How much do your grades matter to you? Solution: Provide structured responses when appropriate How much do your grades matter to you? ☐ Not at all ☐ A little ☐ Somewhat ☐ A lot

25. Collecting High Quality Outcome Data: Part 1 Response Options with Overlaps or Gaps 25 Problem: Response options that overlap or contain gaps Approximately how many hours a day to you typically spend doing homework? ☐ Less than 1 hour ☐ 0 to 2 hours ☐ 4 to 5 hours ☐ More than 5 hours Solution: Scale with no overlaps or gaps Approximately how many hours a day to you typically spend doing homework? ☐ Less than 1 hour ☐ About 1 hour ☐ About 2 hours ☐ About 3 hours ☐ About 4 hours ☐ More than 4 hours

26. Collecting High Quality Outcome Data: Part 1 Unbalanced scales 26 Problem: Using unbalanced scales Solution: Use balanced scales Poor ☐ Average ☐ Good ☐ Very Good ☐ Excellent ☐ Very Poor ☐ Poor ☐ Average ☐ Good ☐ Very Good ☐

27. Collecting High Quality Outcome Data: Part 1 What else to look for in selecting an instrument Can the instrument work in your context? Does the instrument use simple and clear language? Is the instrument appropriate for the age, education, literacy, and language preferences of respondents? 27

28. Collecting High Quality Outcome Data: Part 1 What else to look for in selecting an instrument, continued Does the instrument rely mostly on multiple choice questions? Is the ready for use, or does it need to be modified? How will you extract information from the instrument to address performance measurement targets? 28

29. Collecting High Quality Outcome Data: Part 1 Implementing Data Collection After identifying a data source, method and instrument: 1. Identify data collection participants 2. Set a schedule for collecting data 3. Train data collectors 4. Pilot test the data collection process 5. Make changes 6. Implement data collection 29 FOR BEST RESULTS make key decisions about how to implement data collection BEFORE program startup!

30. Collecting High Quality Outcome Data: Part 1 Step 1: Identifying data collection participants 30 Brainstorm a list of all the relevant players in the data collection process. This includes: Clients/beneficiaries National service participants Staff members Host site staff Other stakeholders

31. Collecting High Quality Outcome Data: Part 1 Step 2: Creating A Data Collection Schedule 31 Identifies who will collect data, using which instrument, and when Share with team to keep everyone informed Include stakeholders in planning Include dates for collecting, analyzing, and reporting data Select a format

32. Collecting High Quality Outcome Data: Part 1 Step 3: Training Data Collectors Determine best person(s) to collect data Provide written instructions for collecting data Explain importance and value of data for program Walk data collectors through instrument Practice or role play data collection Review data collection schedule Explain how to return completed instruments 32

33. Collecting High Quality Outcome Data: Part 1 Step 4: Pilot Testing for Feasibility and Data Quality 1.Try out instruments with a small group similar to program participants 2.Discuss instrument with respondents 3.Analyze pilot test data to ensure the instrument yields the right information 33 Questions for Debrief How long did it take to complete? What did you think the questions were asking you about? Were any questions unclear, confusing, or difficult to answer? Were response options adequate? Did questions allow you to say everything you wanted to say?

34. Collecting High Quality Outcome Data: Part 1 Steps 5 & 6: Make Changes & Implement Your Plan 34 Make Changes Based on pilot test analysis: Improve instrument Strengthen process Implement Your Plan Perform periodic quality control checks

35. Collecting High Quality Outcome Data: Part 1 Ensuring Data Quality: Key Criteria Criteria for collecting high-quality, useful outcome data: Reliability Validity Minimizing Bias 35

36. Collecting High Quality Outcome Data: Part 1 Reliability Reliability: The ability of a method or instrument to yield consistent results under the same conditions. Requires that instruments be administer the same way every time: o Written instructions for respondents o Written instructions for data collectors o Train and monitor data collectors 36

37. Collecting High Quality Outcome Data: Part 1 Reliability Design instruments to improve reliability o Use clear and unambiguous language so question meaning is clear. 37 Unclear language “How has the availability companionship services altered your capacity with respect to attending visits with medical practitioners in a timely manner?” Clear language “How has use of companionship services affected your ability to get to medical appointments on time?”

38. Collecting High Quality Outcome Data: Part 1 Reliability Design instruments to improve reliability o Use attractive, uncluttered layouts that are easy to follow. 38 Cluttered layout “What grade are you in?” ☐ 6 th grade ☐ 7 th grade ☐ 8 th grade Uncluttered layout “What grade are you in?” ☐ 6 th grade ☐ 7 th grade ☐ 8 th grade

39. Collecting High Quality Outcome Data: Part 1 Validity Validity is the ability of a method or instrument to accurately measure what it is intended to measure. Instrument measures the same outcome identified in theory of change Instrument measures relevant dimensions of outcome (attitude, knowledge, knowledge, behavior, condition) Instrument results corroborated by other evidence 39

40. Collecting High Quality Outcome Data: Part 1 Minimizing Sources of Bias Bias involves systematic distortion of results stemming from how data are collected and how instruments are designed. Who: Non-responders = hidden bias How: Wording that encourages or discourages particular responses When and Where: Timing and location can influence responses Bias can lead to over- or under-estimation of program results 40

41. Collecting High Quality Outcome Data: Part 1 7 Ways of Minimizing Bias 1.Get data from as many respondents as possible 2.Follow up with non-responders 3.Take steps to reduce participant attrition 4.Work with program sites to maximize data collection 41

42. Collecting High Quality Outcome Data: Part 1 7 Ways of Minimizing Bias (continued) 5.Pilot test instruments and data collection procedures 6.Mind your language 7.Time data collection to avoid circumstances that may distort responses 42

43. Collecting High Quality Outcome Data: Part 1 Did students in the mentoring program increase their attachment to school? Output Number of disadvantaged youth/mentor matches that were sustained by the CNCS-supported program for at least the required time period (ED4A) Outcome Number of students in grades K-12 that participated in the mentoring or tutoring or other education program who demonstrated improved academic engagement (ED27) How MeasuredPre/post survey of students to gauge attachment to school Outcome Target 90 (of 100) students in grades 6-8 that participate in the after-school program for 9 months will improve academic engagement, defined as feelings of attachment to school. Academic Engagement 43

44. Collecting High Quality Outcome Data: Part 1 Did students in the mentoring program increase their attachment to school? Outcome Number of students in grades K-12 that participated in the mentoring or tutoring or other education program who demonstrated improved academic engagement (ED27) How MeasuredPre/post survey of students to gauge attachment to school Reliability Do survey responses reflect students’ stable and established beliefs about school or just fleeting and changeable feelings? Validity Does the survey get at the dimensions of school attachment that are relevant to the intervention? Are students telling us how the really feel or what they think we want to hear? Bias Does the survey ask the questions in a neutral way? Have we timed the survey to avoid unrelated factors like “exam stress” that could contaminate our results? Academic Engagement— Reliability, Validity, Bias 44

45. Collecting High Quality Outcome Data: Part 1 Summary of key points Steps to implement data collection include identifying the players involved in data collection, creating a data collection schedule, training data collectors, pilot testing instruments, and revising instruments as needed. A data collection schedule identifies who will collect data, using which instrument, and when. Training data collectors by walking them through the instrument and role playing the process. Pilot testing involves having a small group of people complete an instrument and asking them about the experience. 45

46. Collecting High Quality Outcome Data: Part 1 Summary of key points Reliability, validity, and bias are key criteria for data quality. Reliability is the ability of a method or instrument to yield consistent results under the same conditions. Validity is the ability of a method or instrument to measure accurately. Bias involves systematic distortion of results due to over- or under-representation of particular groups, question wording that encourages or discourages particular responses, and by poorly timed data collection. 46

47. Collecting High Quality Outcome Data: Part 1 Summary of key points The benefits of collecting high-quality data include providing a sound basis for decision making, improving service quality and outcomes, increasing accountability, and telling your story in a more compelling way. Your theory of change, and the key measurement question embedded in it, is a useful a guide to measurement. The type of outcome to be measured influences decisions about data sources, methods, and instruments. 47

48. Collecting High Quality Outcome Data: Part 1 Additional resources CNCS Performance Measurement o http://nationalservice.gov/resources/npm/home http://nationalservice.gov/resources/npm/home Instrument Formatting Checklist o https://www.nationalserviceresources.org/npm/practicum- collecting-data-part-2 https://www.nationalserviceresources.org/npm/practicum- collecting-data-part-2 Practicum Materials o http://www.nationalservice.gov/resources/npm/core- curriculum http://www.nationalservice.gov/resources/npm/core- curriculum 48