1. Universal Screening Measures Gary L. Cates, Ph.D. Illinois State University Copyright 2009

2. Who Am I? Insert Picture of funny cartoon here

3. Acknowledgements Cates, Blum, & Swerdlik (date). Authors of book Title and most of this power point slide show. Insert other people to be recognized here.

4. Today’s Agenda Insert Agenda Items here Insert amount of time for items here.

5. Today’s Objectives Provide a purpose, rationale, and description of what constitutes a universal screening measure for academic performance and social behavior. Discuss how to obtain cut-scores/benchmarks and what to consider. Describe how to make data based decisions with universal screening instruments to identify students at-risk for academic performance and social behavior concerns.

6. Activity 1 What have you heard about Universal Screening Measures? What are your biggest concerns?

7. Quick Review of RTI Comprehensive system of student support for academics and behavior. Has a prevention focus Matches instructional needs with scientifically based interventions/instruction for all students Emphasizes data-based decision making across a multiple tiered framework

8. Tier 3 Individualized Instruction Tier 2 Small Group Standard Protocol Instruction Tier 1 Core Universal Curriculum

9. Universal Core Curriculum Universal Screening Measures Identification of Students At-Risk Standard Educational Diagnostic Tool Tier II Standard Protocol Instruction Progress Monitoring Individualized Diagnostic Assessment Tier III Individualized Instruction Progress Monitoring Special Education Entitlement Progress Monitoring

10. 3 Purposes of Universal Screening: Predict which students are at-risk for not meeting AYP (or long term educational goals). Monitor Progress of all students over time Reduce the need to do more in-depth diagnostic assessment with all students Needed for reading, writing, math, and behavior

11. Rationale for Using Universal Screening Measures It is analogous to medical check ups. 3 times per year, not once Determine if all students meeting milestone (i.e. benchmarks) for predicted adequate growth. Provide intervention/support if they are not.

12. Characteristics of Universal Screening Measures Brief to administer. Allow for multiple administration. Simple to score and interpret Predict fairly well those students at-risk for not meeting AYP.

13. Activity 2 What Universal Screening Measures do you have in place currently for: – Reading – Writing – Math – Behavior How do these fit with the characteristics of USM outlined on the previous slide?

14. Examples of Universal Screening Measures for Academics (USM-A) Curriculum Based Measurement

15. Data-Based Decision Making with USM-A

16. Correlations Direction (Positive or negative) Magnitude/Strength (0 to 1) If you want to understand how much overlap (i.e. variance) between the two is explained then square your correlation r =.70then about 49% overlap (i.e. variance)

19. A word about correlations Do not tell you how much one variable causes the other! Use Multiple data sources whenever possible Another option is to triangulate the data (i.e. three data sources) by simply weighting them based on strength of correlation. Strong correlations do not always equate to accurate prediction of specific populations.

20. Student Identification: Percentile Rank Approach Dual Discrepancy to determine a change in intensity (i.e. tier) of service. Cut-Scores – Do not use percentiles! – District Derived cut scores based on screening instruments ability to predict state scores Rate of Improvement – Average gain made per day/per week?

21. sampling of students all students included

22. Student Identification: Dual Discrepancy Approach Rate of Improvement Average gain made per day/per week? Compared to Peers (or cut score) over time.

23. sampling of students all students included

24. Dual Discrepancy Discrepant from peers (or empirically supported cut score) at data collection point 1 (e.g. fall benchmark) Discrepancy continues or becomes larger at point 2 (e.g. winter benchmark) – This is referred to a student’s rate of improvement (ROI)

26. Resources as a Consideration Example of comparing percentile rank or some national cut score without considering resources You want to minimize: – False Positives – False negatives * This can be facilitated with educational diagnostic tool

28. Activity 3 How are you currently making data-based decisions using the Universal Screening Measures you have? Do you need to make some adjustments to your decision making process? If you answered yes to the question above, What might those adjustments be?

29. Data-Based Decision Making with USM-B

30. Some Preliminary Points Social Behavior screening is just as important as academic screening. We will focus on procedures Common Sense is needed: If a child displays severe behavior then bypass the system we will discuss today. We will focus on PBIS and SSBD – The programs are examples of basic principles – You do not need to purchase these exact programs

31. Office Discipline Referrals Good for a stand alone externalizing problem screener Also good for analyzing school wide data – Discussed later

32. Internalizing and Externalizing Screening: A Gating Approach SSBD serves a model 3 Gates – We will only discuss Gates 1 & 2 today as they relate to screening – Gate 3 relates to diagnostic assessment

33. Step 1 Screen All Studens Using Teacher Nominated Rankings of Internaliaer and Externalizers Step 2 Identify The top three Intenralizers and Externalizers in each classroom Step 3 For those six students complete the Critical Events Inventory, adaptive and maladaptive scale Step 4* If CE 0 stop student is not AR If CE for E is If CE for I is 1-3 1-4 go step 5 go to step 5 IF CE for E is 5 IF CE for I is 4 or or more to more go to step 9 step 9 Step 5* If Adaptive Score is : If 31 or more s 42 or more for E less stop for I stop not not AR AR If 30 or less If 41 or less go to step 6 got to step 6 Step 6* If Maladaptive Score is: less than 35 for E, less than 19 consider other evidence, such data before as ODR before determining determining not not AR. AR. If 35 or more If 19 or more Go to Step 7 go step 7 Step 7 The student is definatly at-risk. Consider Tier II intervetion immeditly

34. Gate 1: Teacher Nomination Teachers are generally good judges Nominate three students as Externalizers Nominate 3 students as internalizers Trust your instincts and make decision – There will be more sophisticated process to confirm your suspicions. See hand out of an Example Teacher Nomination Form

35. Gate 2: Confirming Teacher Nominations With Other Data Critical Events Inventory – 33 severe behaviors in checklist format (physical assault, stealing) – Room for other behaviors not listed Adaptive Scale – Assesses socially appropriate functional skills (e.g. following teacher directions) Maladaptive Scale – Assesses risk for developing anti-social behavior (e.g. testing teacher limits)

36. Critical Events Inventory (CEI) Administered to top 3 internalizers and top 3 externalizers of each class. Internalizers – Less than 3 check Maladaptive Scale – If 4 or more there is severe problem and you skip Tier II and move to Tier III (not discussed today) Externalizers – Less than 5 check Maladaptive Scale – If 5 or more then skip tier II and move to Tier III

37. Adaptive Scale Administered to those passing through CEI Internalizers – 42 or more, then stop. Not a problem. – Less than 42 then move to Maladaptive Scale Gate. Externalizers – More than 31 then Stop. Not a problem. – 30 or less then move to Maladaptive Scale Gate.

38. Maladaptive Scale Administered to those passing through Adaptive Scale Internalizers – Less than 19 consider other data but likely no problem – If 19 or more then Tier II intervention Externalizers – Less than 35 consider other data but likely no problem. – If score is 35 or more then Tier II Intervention

39. Data-Based Decision Making using Universal Screening Measures for Behavior Computer software available Web Based programs also available See Handout SWIS TM used as the example here.

40. Average Referrals Per Day Per Month

41. ODR Data by Behavior

42. ODR Data by Location

43. ODR Data by Time of Day

44. ODR Data by Student

45. Review of Important Points- Academics USMs used for screening and progress monitoring. It is important to adhere to the characteristics when choosing a USM USM-As typically are similar to curriculum based measurement procedures There are many ways of choosing an appropriate cut scores, but it is critical that available resources be considered.

46. Review of Important Points - Behavior Social behavior is an important area for screening. Office Discipline referrals is a strong measure for school wide data analysis and external behavior Both internalizing and externalizing behavior should be screened using a gating system Use computer technology to facilitate the data-based decision making process.

47. Questions?