Getting Value Out of Value-Added Report of a Workshop (2010) / Chapter Skim
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1 Introduction to Value-Added Modeling
1 Introduction to Value-Added Modeling
Pages 1-14
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From page 1... ...
In this report, we use the term "value-added models" to refer to a variety of sophisticated statistical techniques that use one or more years of prior student test scores, as well as other data, to adjust for preexisting differences among students when calculating contributions to student test performance. Value-added models have attracted considerable attention in recent years.
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The report summarizes views expressed by workshop participants, and the committee is responsible only for its overall quality and accuracy as a record of what transpired at a two-day event. The workshop was also not designed to generate consensus conclusions or recommendations but focused instead on the identification of ideas, themes, and considerations that contribute to understanding the current role of value-added models in educational settings.
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, interest in these meth ods grew precipitously following the publication of a technical report by Sanders and Rivers in 1996. They found that teacher effects, estimated using student test score trajectories, predict student outcomes at least two years into the future.
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" 3. Growth models measure student achievement by tracking the test scores of the same students from one year to the next to deter mine the extent of their progress.
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If, for example, students whose parents have college degrees tend to have higher test scores than students whose parents have lower educational attainment, then the average student achievement (status) scores of schools with a higher percentage of college-educated parents will be adjusted downward while the average scores of schools with a lower percentage of col lege-educated parents will be adjusted upward.
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THE PRObLEM THAT VALuE-ADDED METHODS AIM TO ADDRESS: NONRANDOM ASSIgNMENT OF STuDENTS Currently, the most common way of reporting school test results is simply in terms of the percentage of students who score at the proficient level or above. However, it is widely recognized among education researchers and practitioners that school rankings based on unadjusted test scores are highly correlated with students' socioeconomic status (SES)
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Under the most widely used evaluation models (status and cohort-to-cohort change) , teachers and school administrators often argue that they are being unfairly judged since students' current test scores are greatly influenced by factors beyond their control and, moreover, that these factors are unevenly distributed across schools and between classrooms within a school.
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However, in educational settings, random assignment is generally not feasible. As workshop presenter Dale Ballou noted, non random assignment is pervasive in education, resulting from decisions by parents and school administrators: residential location decisions (often influenced by the perceived quality of local schools)
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The law's "safe harbor" provision provides an alternative, allowing schools to make adequate yearly progress even if they do not meet proficiency targets, under the condition that they reduce the percentage of students below the proficient level by at least 10 percent. A number of problems with status models discussed at the workshop have already been mentioned.
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However, many participants argued that adjusting for background factors is a more appropriate approach to developing indicators of school effectiveness. Workshop participant Adam Gamoran suggested that using imperfect value-added models would be better than retaining NCLB in its current form.
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The final chapter summarizes a number of questions that policy makers should consider if they are thinking about using value-added indicators for decision making. 8Approaches to value-added models that employ linear models implicitly treat the score scale as having interval scale properties.
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Despite all the efforts that test developers devote to creating tests that accurately mea sure a student's knowledge and skills, all test scores are susceptible to measurement error at the individual and aggregate levels, and this mea surement error contributes to uncertainty in value-added estimates.
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Some value-added models require vertically linked test score scales; that is, the scores on tests from different grades are linked to a common scale so that students' scores from different grades can be compared directly. In other cases, raw test scores from different grades are placed on a common scale by the test vendor before they are reported to the state.
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GETTING VALUE OUT OF VALUE-ADDED BOX 1-1 Continued • Data quality. Missing or faulty data can have a negative impact on the precision and stability of value-added estimates and can also contribute to bias.
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