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Issues in Building a Value-Added System

Issues in Building a Value-Added System . Data Requirements and Data QualityValue-Added Model and Indicator DesignEvaluating Instructional Practices, Programs and PoliciesAlignment with School, District, and State Policies and Practices, Including Performance IncentivesEmbed within a Framework of Data-Informed Decision-MakingProfessional Development to Support Understanding and Application of Value-Added and Data-Informed Decision-Making.

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Issues in Building a Value-Added System

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    1. Measuring Teacher Performance and the Efficacy of Instructional Practices: Value-Added Essentials Dr. Robert H. Meyer, Director Value-Added Research Center Wisconsin Center for Education Research University of Wisconsin-Madison

    3. Why Value-Added? Why not Average Attainment or the Proficiency Rate? Three broad criteria for evaluating models and indicators of school productivity: Statistical Validity and Reliability Behavioral Consequences Outcome Validity/Alignment to Standards

    4. Measurement Objective Statistically isolate the contribution of schools and programs to growth in student achievement at a given point in time from all other sources of student achievement growth, including prior student achievement and student and family characteristics. Use a statistical model to filter out non-teacher factors.

    5. Why not Average Attainment or the Proficiency Rate? � Statistical Biased as measures of school productivity, even if they are derived from highly valid assessments. Attainment indicators are biased because they: Reflect prior achievement and family and student factors associated with achievement growth Reflect out-of-date productivity effects from prior grades and years (back to pre-school and early grades) Are contaminated due to student mobility (and the bias differs across schools) Fail to localize school productivity to a specific grade level, but rather capture (at best) productivity effects from pre-school and onward.

    6. Why not Average Attainment or the Proficiency Rate? � Behavioral Provide institutions with the perverse incentive to "cream," that is, to raise measured performance by educating only those students that tend to have high test scores. Creaming mechanisms: Selective admissions Create an environment (not necessarily intentionally) that is unsupportive to potential dropouts, academically disadvantaged students, and special education students Aggressively retain students Migration of high-quality teachers and principals to schools with academically advantaged students

    7. Punch Line: An appropriately designed value-added model (more on this later) satisfies the statistical validity criterion and generally does not provide adverse incentives.

    8. The Simple Logic of Value-Added Analysis An example based on student longitudinal data for two consecutive years. Note: Value-added analysis is always based on longitudinal data (on the same students (not trend data for different students).

    10. NAEP Mathematics Examination Data

    11. What Does Value-Added Analysis Typically Demonstrate? It is possible for schools and teachers to provide high-productivity education to all types of students, including students with low prior achievement.

    12. Value-Added and Attainment Communicating Information on Two Different Dimensions of Student Achievement

    13. Two Options for Connecting Value-Added and Attainment Data

    14. Do Low Achieving Students Attend High Value-Added Schools?

    15. Value-Added vs. Attainment: Is There a Difference?

    16. Value-Added and Post Attainment: Low, Medium, and High Comparisons: Reading

    17. Value-Added and Post Attainment: Low, Medium, and High Comparisons: Math

    18. Issues in the Development of a Value-Added Indicator System How complex should a value-added model be? Possible rule: "Simpler is better, unless it is wrong." Implies need for �quality of indicator/ quality of model� diagnostics.

    19.

    20. A Value-Added Model of School Performance for a Given Subject, Grade, and Year � A T2 Model

    21. What�s Next? �����What Works? Evaluate the efficacy of instructional practices, programs, and policies

    22. Contact Information Robert H. Meyer University of Wisconsin-Madison Value-Added Analytics: VA2 http://varc.wceruw.org/ RHMeyer@wisc.edu

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