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New MEAP and MME Cut Scores, In Depth - PowerPoint PPT Presentation

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.

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New MEAP and MME Cut Scores, In Depth

Presentation at the Fall 2011 Meeting of the

Michigan Educational Research Association

Identifying MME Cut Scores

• 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

• 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

• 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)

• 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

How Logistic Regression Works in Identifying MME Cut Scores

How Logistic Regression Works in Identifying MME Cut Scores

How Logistic Regression Works in Identifying MME Cut Scores

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

How Signal Detection Theory Works in Identifying MME Cut Scores

Adjust the unknown cut score to maximize consistent classification

• 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

Study 2 Scores

Identifying MEAP Cut Scores

Analyses Conducted to Identify ScoresMEAP 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

• 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

How ScoresSignal 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

Grade 11: Not proficient

Grade 11: Not proficient

Grade 8: Not proficient

Grade 11: Not proficient

Grade 8: Not Proficient

• The more links in the chain, the greater the effects of regression

• Original plan for Math and Reading

• 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

• Revised plan for Math and Reading

• For Grade 3, 4, 5, 6

• Link grade 3, 4, 5, or 6 MEAP to grade 7 MME

• For Grade 7, 8

• Link grade 7 or 8 MEAP to grade 11 MME

• Results in a maximum of three links for any one grade

Results Scores

• 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

Results Scores

• 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

• Classification Consistency Rates for MEAP Cut Scores in Mathematics

• Classification Consistency Rates for MEAP Cut Scores in Reading

• Classification Consistency Rates for MEAP Cut Scores in Science

• Classification Consistency Rates for MEAP Cut Scores in Science

Study 3 Scores

Creating Mini-Cuts for PLC Calculations in Reading and Mathematics

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

Results in 9 Mini-Categories Larger than the CSEM across the Mini-Categories

Performance Level Change Transition Table Larger than the CSEM across the Mini-Categories

Impact Data, Mathematics Larger than the CSEM across the Mini-Categories

New Versus Old Cut Scores

48 Larger than the CSEM across the Mini-Categories

49 Larger than the CSEM across the Mini-Categories

50 Larger than the CSEM across the Mini-Categories

51 Larger than the CSEM across the Mini-Categories

52 Larger than the CSEM across the Mini-Categories

53 Larger than the CSEM across the Mini-Categories

54 Larger than the CSEM across the Mini-Categories

Impact Data, Reading Larger than the CSEM across the Mini-Categories

New Versus Old Cut Scores

56 Larger than the CSEM across the Mini-Categories

57 Larger than the CSEM across the Mini-Categories

58 Larger than the CSEM across the Mini-Categories

59 Larger than the CSEM across the Mini-Categories

60 Larger than the CSEM across the Mini-Categories

61 Larger than the CSEM across the Mini-Categories

62 Larger than the CSEM across the Mini-Categories

Impact Data, Science Larger than the CSEM across the Mini-Categories

New Versus Old Cut Scores

64 Larger than the CSEM across the Mini-Categories

65 Larger than the CSEM across the Mini-Categories

66 Larger than the CSEM across the Mini-Categories

Impact Data, Social Studies Larger than the CSEM across the Mini-Categories

New Versus Old Cut Scores

68 Larger than the CSEM across the Mini-Categories

69 Larger than the CSEM across the Mini-Categories

70 Larger than the CSEM across the Mini-Categories

Contact Information Larger than the CSEM across the Mini-Categories

• Joseph A. Martineau

• Executive Director

• Bureau of Assessment & Accountability

• Michigan Department of Education

• martineauj@michigan.gov

• 517-241-4710