Evaluating Pretest to Posttest Score Differences in CAP Science and Social Studies Assessments:

1. Evaluating Pretest to Posttest Score Differences in CAP Science and Social Studies Assessments: How Much Growth is Enough? February 2014 Dale Whittington, Ph.D. – Shaker Heights Russ Brown, Ph.D – CMSD Denis Jarvinen, Ph.D. – Strategic Measurement and Evaluation, Inc.

2. Setting Standards for Performance Licensing Tests One Test and One Standard for Performance (e.g., Pharmacists) Pass or Fail State Accountability Testing One Test, Multiple Standards (e.g., Ohio OAA) Below Basic, Basic, Proficient, Advanced CAP Foundation Science and Social Studies Assessments Two Tests, One Standard Evaluating Growth (How Much?)

3. Looking at Performance Standards Goal of standard setting is to determine a level of knowledge and skill judged to be appropriate for test purpose Content-Based Standards Goal of standard setting is to use common statistical feature(s) of the data to set a criteria for acceptable performance Growth-Based Standards

4. Three Statistic-Based Approaches for Evaluating Growth of Student Scores • Using Effect Size • Using The Score Distribution • Using the Standard Error of Measurement

5. Describing and Comparing Approaches • Data Points Needed • Calculations Required • Outcomes Using a Common Set of Student Data • Advantages and Disadvantages

6. The Common Data Set

7. The Common Data Set

8. The Common Data Set

9. The Common Data Set

10. The Common Data Set

11. Shaker Heights Schools Effect Size for SLO’s and Growth Prepared by Dale WhittingtonShaker Heights City School DistrictOhio Middle Level Annual ConferenceColumbus, OhioFebruary 21, 2014

12. What is effect size? • In an educational setting, effect size is one way to measure the effectiveness of a particular intervention. • Effect size enables us to measure both the improvement (gain) in learner achievement for a group of learners AND at the same time, take into account the variation of student performance. Adapted from Understanding, using and calculating effect size, Govt of South Australia, Department of Education & Child Development, http://www.decd.sa.gov.au/quality/files/links/WhatIsEffectSize.pdf

13. Practical Advantages • Easy to calculate • Easy to understand; makes intuitive sense • Adaptable to different kinds of assessments • Adaptable to different kinds of ways of considering growth and goals for SLO’s: • Shared attribution across the district • Shared attribution within a school • Attribution for a specific teacher or group of students

14. So how do you calculate effect sizes for SLO’s or growth? Start with a set of pretest scores and posttest scores for the same students Calculate the difference between the pretest & posttest for each student

15. Calculations Continued • Pretest • Mean: 35.0 • SD: 9.1 • Posttest • Mean: 38.8 • SD: 8.5 Calculate the means and standard deviations for both tests Average the Standard Deviations The average of 9.1 and 8.5 is 8.8

16. How to adapt • If your pretest and posttest are different lengths, convert to a similar scale, like percentages. • Think about who you are basing your analysis on and use that to decide what standard deviation (SD) to use • Common attribution for district: District SD • Common attribution for school: School SD • Class: Class SD • Specific group, such as economically disadvantaged: the group’s SD

17. Use the average standard deviation and the gains to calculate the effect size: Effect Size=Gain/SD

18. Interpret your results: Common criteria Cohen (1969) • ‘Small’ (.2) • real, but difficult to detect • difference between the heights of 15 year old and 16 year old girls in the US • ‘Medium’ (.5) • ‘large enough to be visible to the naked eye’ • difference between the heights of 14 & 18 year old girls • ‘Large’ (.8) • ‘grossly perceptible and therefore large’ • difference between the heights of 13 & 18 year old girls Hattie: “For students moving from one year to the next, the average effect size across all students is 0.40.”

19. How results differ, depending on attribution and how you tier students

20. Another Example based on OAA

22. Resources • Understanding, using and calculating effect size. Government of South Australia, Department of Education & Child Development, http://www.decd.sa.gov.au/quality/files/links/WhatIsEffectSize.pdf • Review Methods/Interpreting Effect Sizes. JHU: Best Evidence Encyclopedia. http://www.bestevidence.org/methods/effectsize.htm • Calculating an effect size: a practical guide. Visible Learning Plus. http://visiblelearningplus.com/faqs/calculating-effect-size-practical-guide

23. Establishing Growth Targets with Limited Data Prepared by Russ Brown, Ph.D – CMSD

24. Overview • Design Principles for Student Growth Model work • The PROBLEM! • An Idea for a Solution • Strengths/Weaknesses

25. Guiding Principles Equity - like measures for like teachers, like expectations for like students. Simplicity - Parsimony and transparency are critical. Continuous improvement will be critical – It simply will not be perfect on the first try!

26. The PROBLEM How much growth is enough? How do you estimate this when you don’t know the relationship between the two tests? ?

27. What do we know? Basic information about the distribution of scores. The relative position of each student on the distribution. Can we leverage this to set targets?

28. The Idea Devoid of any way to estimate what growth “should be”… Students of like ability (ie., same pretest scores) would typically be expected to make comparable growth over time. Use Normal Curve Equivalents as a means to establish targets and relative growth.

29. How Translate Pre-Test scores to NCEs Z= (Pretest Score - Mean Pretest Score) Standard Deviation of the Pretest NCE = (Z x 21.063)+ 50 (1-99 Interval)

30. Outcomes – What Threshold? • Calculating whether the goal is obtained: • Must make a judgment about the stringency of the goal/calculation

31. Outcomes – What Performance Level?

32. Outcomes – What Performance Level? • Not surprisingly – outcomes vary by the stringency of the expectation…

33. Outcomes – Quick Comparison

34. Outcomes – What about Real Data? Applied to 3rd Grade OAA (Fall to Spring):

35. Outcomes – What about Real Data? Applied to 4th Grade Benchmark to OAA (Fall to Spring):

36. Pros and Cons • + Students with like scores have like expectations for growth • + Relatively simple and relatively transparent • Must make a value judgment about the amount of error for which one wishes to compensate (not so transparent) • More adjustment = more bias at the bottom

37. Standard Error of Measurement All scores have a “true”score and “error” • Error bands on score reports Standard Error quantifies degree of “error” in a test score Formula is: Standard Error of Measurement = Values needed: Mean, Standard Deviation, Reliability of the Test Assumptions that underlie this approach

38. Steps 1) For a set of data, calculate the mean and standard deviation 2) Calculate the reliability of the test 3) Use the formula to determine the Standard Error of Measurement (class level, school level) 4) Set a level for the growth standard (1 se, 2 se, etc.) 5) Add chosen level of standard error to raw score 6) Convert (raw score + standard error) to percent correct on pretest 7) Find corresponding percent correct/raw score on posttest (Note: Assumptions here not required once IRT equating is completed) 8) Compare actual student posttest score with target score 9) At or above target score = “Acceptable Progress”

39. Calculations for one student

40. Results

41. Observations High pretest scores can lead to out-of-range posttest score targets. Any modification to the sample that increases the Standard Deviation will increase the value of the Standard Error and therefore require more score growth to reach the target.