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Batya Elbaum, NCSEAM Pam Roush, West Virginia Part C OSEP National Early Childhood Meeting

How to Explain the Numbers: Helping Staff, Parents, and Other Stakeholders Understand the Results of the NCSEAM Surveys for Part C and 619. Batya Elbaum, NCSEAM Pam Roush, West Virginia Part C OSEP National Early Childhood Meeting Arlington, VA, December 2007. Purpose of the session.

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Batya Elbaum, NCSEAM Pam Roush, West Virginia Part C OSEP National Early Childhood Meeting

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  1. How to Explain the Numbers:Helping Staff, Parents, and Other Stakeholders Understand the Results of the NCSEAM Surveys for Part C and 619 Batya Elbaum, NCSEAM Pam Roush, West Virginia Part C OSEP National Early Childhood Meeting Arlington, VA, December 2007

  2. Purpose of the session • To provide participants with strategies for explaining the measures and percentages that come from the NCSEAM rating scales addressing Indicators C4 and B8.

  3. Part C Indicator #4 “Percent of families participating in Part C who report that early intervention services have helped the family a) know their rights, b) effectively communicate their children’s needs, and c) help their children develop and learn.”

  4. Part B Indicator #8 “Percent of parents with a child receiving special education services who report that schools facilitated parent involvement as a means of improving services and results for children with disabilities.”

  5. WV Part C Indicator 4 • 4A – Know rights • 2006 76.5% • 2007 73.8% • 4B – Communicate • 2006 72.8% • 2007 71.0% • 4C – Help child • 2006 86.3% • 2007 78.7%

  6. Change in mean measure by region

  7. Percent on 4A by Region in 2006 and 2007

  8. Why use a measurement analysis?

  9. Why use a measurement analysis? • Use of a measurement framework ensures that measures will mean the same thing, regardless of how many items, or which specific items, are administered.

  10. Why use a measurement analysis? • We can’t assume that all survey items are equally agreeable. • A measurement analysis gives us a measure of each item’s overall agreeability.

  11. Order of Impact on Family items from least to most agreeable

  12. Why use a measurement analysis? • Use of a measurement framework allows us to test whether all the items are measuring the same thing.

  13. Measuring the 3 subindicators • NCSEAM’s measurement analysis of pilot data from thousands of families showed that items that are related to the three OSEP outcomes all fit into a single scale of family outcomes.

  14. Measuring the 3 subindicators • Families appear to achieve these outcomes in a very consistent order. • Families who report that EI helped them know their rights also report that EI also helped them help their child develop and learn. • Families who report that EI helped them effectively communicate their children’s needs also report that EI also helped them know their rights and help their child develop and learn.

  15. Location of key items related to Indicator C4 Indicator 4b: Effectively communicate their children’s needs. IFS Item: “Communicate more effectively with the people who work with my child and family.” [556] 556 Indicator 4a: Know their rights. IFS Item: “Know about my child's and family's rights concerning Early Intervention services.” [539] 539 Indicator 4c: Help their children develop and learn. IFS items: “Understand my child's special needs.” [516] “Do things with and for my child that are good for my child's development.” [498] 516

  16. Why use a measurement analysis? • We can’t assume that the response choices (very strongly disagree, strongly disagree, disagree, agree, strongly agree, very strongly agree) indicated by equidistant circles or numbers on a page are really equidistant.

  17. Spacing of response choices

  18. Spacing of response choices

  19. The logic of distributions

  20. Example: Distribution of height

  21. Distribution of Measures of Reading Achievement - 1

  22. Distribution of Measures of Reading Achievement - 2

  23. West Virginia results 2006

  24. West Virginia results 2007

  25. WV without extreme cases 2006

  26. WV without extreme cases 2007

  27. Instrumentation and approach to data analysis make a difference

  28. Choice of instruments: Part B • NCSEAM K-12 and 619 9 • NCSEAM K-12 21 • Customized NCSEAM survey 11 • State-developed or adapted 18 • ECO Family Outcomes Survey 1

  29. States’ reported baseline data onIndicator 8B

  30. Choice of instruments: Part C • NCSEAM Survey 25 • ECO Family Outcomes Survey 18 • State-developed or adapted 10 • Combination 1

  31. Analysis of states’ baseline data:Indicator C4

  32. Analysis of states’ baseline data:Indicator C4

  33. APR Family Outcome Results – Part C States Using NCSEAM Survey and RASCH analysisAPRs submitted February 2007

  34. Reference to a standard

  35. Percent of measures above the adopted standards standards

  36. What is the relationship between % at or above the NCSEAM standard and % agreement on the “threshold item?”

  37. Sample descriptive interpretation related to Indicator 4A • Approximately 90-95% of families agreed, with approximately two-thirds of families expressing strong or very strong agreement, that early intervention helped them: • Understand their child’s special needs. • Do things with and for their child that are good for their child’s development. • Feel that they can handle the challenges of parenting a child with special needs.

  38. Part C Example: WV data

  39. Q44 - know about my child's and family's rights

  40. Q42 - communicate more effectively

  41. Q46 - understand my child's special needs

  42. Part B Example

  43. The NCSEAM standard is a stringent standard • Cut score for B at 600 • Cut scores for C at 539, 516, 556 • The standard is set so as to ensure that approximately 95% or more of the responses will be in one of the agree categories on the item designated as the “threshold item” • Simple agreement with the “threshold item” is not enough

  44. How precise are the percentages that states are reporting?

  45. Error in estimation • Whenever data are based on a sample (whether owing to sampling or to a less than 100% return rate), there is some amount of error in generalizing to the population. • “Error,” or imprecision, can be represented by a confidence interval. • As sample size decreases, error of estimate - and its representation as a confidence interval - increases. • The decrease is not linear!

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