The Research

1. Data Analysis for Educational Leaders“Data Rich, Information Rich!”“Comprehensive data analysis is tied to systematic and systemic continuous improvement” -Victoria L. Bernhardt, Ph.D.-

2. The Research • The ability to accurately and appropriately use the data … is critical to principals' effectiveness at ultimately improving student achievement (Price & Burton, 2004; Yeagley, 2001) • Holcomb (2004) found that proper training is key to effectively using data.

3. Article ReviewGroup A: “Developing an Inquiry-Minded District”Group B: “Looking Deeper into Data”Group C: “Expanding data analysis skills in educational leaders: implications for preparation programs.”Group D: “First Things First: Demystifying Data Analysis”

4. Inquiry: • Question • Triangulate • Examine Angles

5. In this workshop, you will: • Become familiar with analyzing and interpreting data. • Learn how to translate data into SMART Goals. • Develop a school-wide method of progress monitoring.

6. Statistics Two types: Descriptive statistics are used to describe or summarize ourobservations without making inferences. Inferential statistics are to make predictions or estimates using a sample population. What type of Statistics will we be using?

7. Summative Strand Level Disaggregation Why is triangulation essential? TriangulationUsing Multiple Measures! What does the data tell us about our students? What are the curricular, programmatic, and instructional implications? Evidence of instructional practice Benchmark

8. Take a few minutes to answer a question: What was your school or district goal for reading last year? For reading, how many students at each grade level were: Below Basic? Basic? Proficient? Goal? Advanced?

9. How well did the students perform overall?

10. Is this information enough to make a school-wide goal? If so, what could one be? If not, what other information do you need?

11. CMT 2010 Data Grade 3 Why is it important to see the proficiency levels?

12. Administrators must look at “Big Picture Data”!

13. This school’s goal was to increase the % of students scoring at proficiency in math by 10%. Was this a good goal? Why or why not? Performance Level Scores School A

14. Performance Level Scores School A Total # of students % of students in this level How does this data impact your action plan? What are the instructional, professional development, curricular, and programmatic implications? Resources vs. Need?

15. Let’s try!

16. Group Work • Work with your group to determine the overall performance of the following school. Then answer the following questions: • What could a goal be? • How does this data impact your action plan? • What are the instructional, professional development, curricular, and programmatic implications? Resources vs. Need?

19. AYP (Reading 2010-2011=89%) Goal= To increase the % of Students scoring At/Above Proficiency from 90% (14+41+35) to 95% (91%needed for AYP plus, in this case, because the scores are so close to AYP, add an educational significant difference of 4%pts.) as measured by the 2011 CMT Math Score and monitored by the Common Assessments.

20. Where do we need to be? AYP vs. Safe Harbor

21. Below Basic Basic Proficient Goal Advanced

22. Performance Level Scores School A

23. Safe Harbor: An alternate method for measuring progress toward AYP. For any school and/or subgroup that does not meet the proficiency target: • Reduce the % not proficient by 10 % • Meet the additional academic indicators • Meet the 95% participation rate requirement. • Example:

24. Group Work: • Work with your partner to figure out safe harbor for this school. • Use this information to develop a school-wide goal • Is there anything else this data tells you?

25. This same process can be used for all sorts of data (subgroup, instructional, attendance….)! Lets Try! Group A: Attendance Data Group B: Instructional Data Group C: Subgroup Data

26. Number of Days Absent vs. Grade 2009-2010 What could be a goal for grade 9?

27. Your assessment results should render information about curriculum implementation and instruction. How would this data impact your action plan (PD, support, data collection…)?

30. Vertical Scale Scores

32. Disaggregation

34. Notice: there is no overlap

35. For the Economically Disadvantaged student data below, to what subgroup should we compare this data?

36. The SIOP model was implemented to reach these learners and additional instructional hours Is it working? % % ELL Subgroup

37. % % % ELL Subgroup Non-ELL % % %

38. ELL Group “N”=25 Non-ELL Group “N”=50 New Arrival ”N”=1 %

39. ELL Group “N”=25 Non-ELL Group “N”=50 New Arrival ”N”=1 %

40. What questions do you have? What are some cautions here?

41. What is Educationally Significant?

42. N (Male)=142 N (Female)=139 Is this significant?

43. Look at the following graphs: What are the “take away” messages?Is there any actions you would do as a result of this information?

46. N=200

47. Know the assessment • Summative, Formative, Diagnostic • Points needed for mastery (Benchmark) • # and types of questions. • Strand make-up

50. Third Generation CAPT Mathematics Assessment Blueprint