Universal Screening Measures (Chapter 2)

1. Universal Screening Measures(Chapter 2) Gary L. Cates, Ph.D. Illinois State University

2. 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

4. 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

5. Rationale for Using Universal Screening Measures • It is analogous to medical check-ups (but three times a year, not once) • Determine whether all students are meeting milestone (i.e., benchmarks) for predicted adequate growth • Provide intervention/support if they are not

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

7. Examples of Universal Screening Measures for Academic Performance (USM-A) Curriculum-Based Measurement

8. Data-Based Decision Making with USM-A

9. 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 are based on screening instruments’ ability to predict state scores • Rate of Improvement • Average gain made per day/per week?

10. sampling of students all students included

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

12. sampling of students all students included

13. 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)

15. 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 an educational diagnostic tool

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 = .70 then about 49% overlap (i.e., variance)

18. A Word About Correlations • They 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., use three data sources) by simply weighting them based on strength of correlation • Strong correlations do not always equate to accurate prediction of specific populations

19. Presentation 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?

20. Data-Based Decision Making with USM-B

21. 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

23. Office Discipline Referrals • Good as a stand-alone screening tool for externalizing behavior problems • Also good for analyzing schoolwide data • Discussed later • See example teacher nomination form – Chapter 2 of book and on CD

24. Teacher Nomination • Teachers are generally good judges • Nominate three students as externalizers • Nominate three students as internalizers • Trust your instincts and make decision • There will be more sophisticated process to confirm your choices • See example teacher nomination form – Chapter 2 of book and on CD

25. Confirming Teacher Nominations with Other Data • Teacher, Parent, and Student Rating Scales • BASC • CBCL (Achenbach)

26. Example: Systematic Screening for Behavior Disorders (SSBD) • Critical Events Inventory: • 33 severe behaviors (e.g., physical assault, stealing) in checklist format • Room for other behaviors not listed • Adaptive Scale: Assesses socially appropriate functional skills (e.g., following teacher directions) • Maladaptive Scale: Assesses risk for developing antisocial behavior (e.g., testing teacher limits)

27. Data-Based Decision Making Using Universal Screening Measures for Behavior • Computer software available • Web-based programs also available • See handout (Microsoft Excel Template)

28. Average Referrals Per Day Per Month

29. ODR Data by Behavior

30. ODR Data by Location

31. ODR Data by Time of Day

32. ODR Data by Student

33. Review of Important Points: Academic Peformance • USMs used for screening and progress monitoring • It is important to adhere to the characteristics when choosing a USM • USM-A’s typically are similar to curriculum-based measurement procedures • There are many ways to choose appropriate cut scores, but it is critical that available resources be considered

34. Review of Important Points: Behavior • Social behavior is an important area for screening • Number of office discipline referrals is a strong measure for schoolwide data analysis and external behavior • Both internalizing and externalizing behaviors should be screened using teacher nominations • Follow-up with rating scales • Use computer technology to facilitate the data-based decision-making process

35. Questions