1 / 71

New MEAP and MME Cut Scores, In Depth

New MEAP and MME Cut Scores, In Depth. Presentation at the Fall 2011 Meeting of the Michigan Educational Research Association. Study 1. Identifying MME Cut Scores. Data Sources: College Grades. College Courses Included. Grades Used in the Analyses.

Download Presentation

New MEAP and MME Cut Scores, In Depth

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. New MEAP and MME Cut Scores, In Depth Presentation at the Fall 2011 Meeting of the Michigan Educational Research Association

  2. Study 1 Identifying MME Cut Scores

  3. Data Sources: College Grades

  4. College Courses Included

  5. Grades Used in the Analyses • Grades were put on a numeric scale from 0-4 • 0 = F • 1 = D • 2 = C • 3 = B • 4 = A • Not used • AU, AWF, DR, R, RA, FR, T, TR, X • Coded as 3.0 • P, CR • Coded as 0.0 • IN, N, NC, NE, NS, W, WF, WP, WX, and U

  6. Descriptive Statistics on Grades Used By Subject

  7. Distribution of Student Grades by Course

  8. Distribution of Student Grades by Course

  9. Analyses Conducted to Identify MME Cut Scores • Students receiving an A • Students receiving a B or better • Students receiving a C or better • Students receiving a B or better in 4-year universities • Students receiving a B or better in 2-year institutions

  10. Analyses Conducted to Identify MME Cut Scores • Logistic Regression (LR) • Identify score that gives a 50% probability of achieving an A • Identify score that gives a 50% probability of achieving a B or better • Identify score that gives a 50% probability of achieving a C or better • Signal Detection Theory (SDT) • Identify scores that maximize the proportion receiving consistent classifications from MME to college grades • i.e., both proficient/advanced and receiving a A/B/C or better • i.e., both not proficient/partially proficient and receiving a A-/B-/C- or worse • Equivalent to LR under mild monotonicity assumptions • Selected SDT as the preferred method because of its purpose (maximizing consistent classification)

  11. Logistic Regression, Mathematically • Where • success is obtaining an A/B/C or better • e is the base of the natural logarithm • β0 is the intercept of the logistic regression • β1 is the slope of the logistic regression • x is the MME score

  12. How Logistic Regression Works in Identifying MME Cut Scores

  13. How Logistic Regression Works in Identifying MME Cut Scores

  14. How Logistic Regression Works in Identifying MME Cut Scores

  15. How Logistic Regression Works in Identifying MME Cut Scores

  16. How Signal Detection Theory Works in Identifying MME Cut Scores • Basic Idea • Set the MME cut score to… • Maximize the number of students in the Consistent cells • Minimize the number of students in the Inconsistent cells • Maximize consistent classification from MME to first-year college grades

  17. How Signal Detection Theory Works in Identifying MME Cut Scores

  18. How Signal Detection Theory Works in Identifying MME Cut Scores

  19. How Signal Detection Theory Works in Identifying MME Cut Scores Adjust the unknown cut score to maximize consistent classification

  20. How Signal Detection Theory Works in Identifying MME Cut Scores

  21. How Signal Detection Theory Works in Identifying MME Cut Scores

  22. Results of Study to Identify MME Cut Scores • Analyses treating grades of A as the success criterion produced unusable results (i.e., the highest possible MME scale scores • Analyses treating grades of C as the success criterion produced unusable results (i.e., MME scale scores below chance level) • Analyses treating 4-year and 2-year institutions did produce different cut scores, but they were within measurement error of each other • Used analyses based on all institutions and grades of B or better to produce MME cut scores • Used probability of success of 33% and 67% to set the “partially proficient” and “advanced” cut scores • SDT and LR produced very similar results • Used SDT because it was the preferred methodology

  23. Results of the Study to Identify MME Cut Scores

  24. Study 2 Identifying MEAP Cut Scores

  25. Data Sources: Cohorts with Data Available

  26. Analyses Conducted to Identify MEAP Cut Scores • Logistic Regression (LR) • Identify score that gives a 50% probability of achieving proficiency on a later-grade test (i.e., MME or MEAP) • Signal Detection Theory (SDT) • Identify scores that maximize the proportion receiving consistent classifications from one grade to a later grade • i.e., proficient/advanced on both tests • i.e., not proficient/partially proficient on both tests • Equivalent to LR under mild monotonicity assumptions • Equipercentile Cohort Matching (ECM) • Identify scores that give the same percentage of students proficient/advanced on both tests • Selected SDT as the preferred method because of its purpose (maximizing consistent classification) • However, SDT and LR are susceptible to regression away from the mean

  27. How Logistic Regression Works in Identifying MEAP Cut Scores • Same as for identifying MME cut scores • Criterion for success is proficiency on a later grade test rather than getting a B or better in a related college course

  28. How Signal Detection Theory Works in Identifying MEAP Cut Scores Each dot represents a plot of test scores in grade 8 and grade 11 for a single student

  29. How Signal Detection Theory Works in Identifying MEAP Cut Scores Grade 11: Not proficient Grade 11: Proficient

  30. How Signal Detection Theory Works in Identifying MEAP Cut Scores Grade 8: Proficient Grade 11: Not proficient Grade 8: Proficient Grade 11: Proficient Grade 8: Not proficient Grade 11: Not proficient Grade 8: Not Proficient Grade 11: Proficient

  31. How Signal Detection Theory Works in Identifying MEAP Cut Scores

  32. How Signal Detection Theory Works in Identifying MEAP Cut Scores

  33. Addressing Regression Effects • The more links in the chain, the greater the effects of regression • Original plan for Math and Reading • Link grade 11 MME to college grades • Link grade 8 MEAP to grade 11 MME • Link grade 7 MEAP to grade 8 MEAP • Link grade 6 MEAP to grade 7 MEAP • Link grade 5 MEAP to grade 6 MEAP • Link grade 4 MEAP to grade 5 MEAP • Link grade 3 MEAP to grade 4 MEAP • Original plan results in 7 links by the time the grade 3 cut is set • Original plan results in inflated cut scores in lower grades

  34. Addressing Regression Effects • Revised plan for Math and Reading • For Grade 3, 4, 5, 6 • Link grade 11 MME to college grades • Link grade 7 MEAP to grade 11 MME • Link grade 3, 4, 5, or 6 MEAP to grade 7 MME • For Grade 7, 8 • Link grade 11 MME to college grades • Link grade 7 or 8 MEAP to grade 11 MME • Results in a maximum of three links for any one grade

  35. Results • No evidence of regression away from the mean in identifying MEAP “proficient” cut scores • Looking for a consistently lower percentage of students proficient as one goes down in grades • Used SDT to identify MEAP “proficient” cut scores • Evidence of regression away from the mean in identifying MEAP “partially proficient” and “advanced” cut scores • Increasingly smaller percentages of students in the “Not proficient” and “Advanced” categories as one goes down in grade • Used ECM instead to identify MEAP “Not Proficient” and “Advanced” cut scores

  36. Results • No evidence of regression away from the mean in identifying MEAP “proficient” cut scores • Looking for a consistently lower percentage of students proficient as one goes down in grades • Used SDT to identify MEAP “proficient” cut scores • Evidence of regression away from the mean in identifying MEAP “partially proficient” and “advanced” cut scores • Increasingly smaller percentages of students in the “Not proficient” and “Advanced” categories as one goes down in grade • Used ECM instead to identify MEAP “Not Proficient” and “Advanced” cut scores

  37. Results: Classification Consistency Rates • Classification Consistency Rates for MEAP Cut Scores in Mathematics

  38. Results: Classification Consistency Rates • Classification Consistency Rates for MEAP Cut Scores in Reading

  39. Results: Classification Consistency Rates • Classification Consistency Rates for MEAP Cut Scores in Science

  40. Results: Classification Consistency Rates • Classification Consistency Rates for MEAP Cut Scores in Science

  41. Study 3 Creating Mini-Cuts for PLC Calculations in Reading and Mathematics

  42. Start with Conditional Standard Errors of Measurement

  43. Superimpose the New Cut Scores

  44. Identify Mini-Cut Scores Such That The Mini-Categories Are Larger than the CSEM across the Mini-Categories

  45. Results in 9 Mini-Categories

  46. Performance Level Change Transition Table

  47. Impact Data, Mathematics New Versus Old Cut Scores

  48. 48

  49. 49

  50. 50

More Related