on specific curriculum objectives (Smith and O'Day, 1990; Clune, 1991). As a result of this criticism, many school districts, states, and professional test developers are experimenting with new types of assessments—for example, tests with open-ended questions, performance-based assessments, graded portfolios, and curriculum-based multiple-choice tests—more closely related to educational objectives.1 As new tests are developed, test developers and curriculum designers need to determine whether the new tests and assessments are valid, in the sense of measuring the skills that are highly valued by society.

Noncorruptability

Second, a performance indicator must accurately measure performance with respect to the outcome that it purports to measure. Test scores can be "corrupted" in various ways. For example, a test could be administered in such a way that it is easy for students and staff to cheat. Alternatively, a test form that is administered year after year could stimulate instructors to teach narrowly to the test, rather than to the broader domain of knowledge that underlies the test.2

Valid Measurement of School Performance

Finally, a performance indicator must accurately and reliably measure school performance, where school performance with respect to a particular test or other student outcome is defined as the contribution of the school to that outcome. In a recent paper Meyer (1994) demonstrated that the common indicators of school performance—average and median test scores—are highly flawed even though derived from valid assessments. The simulation results reported by Meyer indicate that changes over time in average test scores could be negatively correlated with actual changes in school performance.

The purpose of this chapter is to consider the class of educational indicators referred to as value-added indicators that satisfy the third criterion discussed above. For simplicity the focus here is entirely on value-added indicators derived from student test scores. The first section explains the theory and logic of value-added indicators, emphasizing the interpretation and reporting of value-added models and indicators rather than methods of estimating these models and other technical questions. The second section compares value-added and nonvalue-added indicators such as the average test score. The third part discusses policy considerations that are relevant to the use and nonuse of value-added and nonvalue-added indicators. Finally, conclusions are drawn and recommendations offered.

1  

See, for example, Wiggins (1989), Darling-Hammond (1991), Shepard (1991), and Koretz et al. (1994).

2  

See, for example, Haladyna et al. (1991), Nolen et al. (1992), Smith and Rottenberg (1991), and Shepard (1991).



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