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Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Contributors -- Introduction -- Purpose and Organization of This Volume -- What Is Classroom Assessment? -- What Is the Role of the Measurement Community in Classroom Practice? -- Conclusion -- References -- PART I: CLASSROOM ASSESSMENT INFORMATION -- Chapter 1: Perspectives on the Validity of Classroom Assessments -- Argument-Based Approach to Validation -- Perspectives on Assessment -- Classroom Assessment -- Concluding Remarks -- References -- Chapter 2: Cognitive Diagnosis Is Not Enough: The Challenge of Measuring Learning with Classroom Assessments -- Basic Goal of Classroom Assessments: Supporting Student Learning -- The Challenge of Integrating Students' Personality Traits with Classroom Assessment Results -- The Challenge of Measuring Students' States of Emotion and Relatedness -- A Model for Contextualizing Classroom Assessment -- Conclusion -- References -- Chapter 3: Language in Practice: A Mediator of Valid Interpretations of Information Generated by Classroom Assessments among Linguistically and Culturally Diverse Students -- Limitations of Large-Scale Testing of Situated Language Practices during Instruction -- Classroom Assessment: Capturing Situated EL Student Performance in Interaction -- Language Practices as Assessment Mediator -- Effects of Students' Language Variation on the Design of Valid Classroom Assessments -- Leveraging the Effects of Students' Language Variation for Improved Assessment -- Concluding Remarks -- Notes -- References -- Chapter 4: Feedback and Measurement -- Definitions and Key Concepts -- Types of Feedback, Types of Measurement, and Their Relationship -- Feedback and Validation -- Conclusion -- Note -- References -- Chapter 5: Discussion of Part I: Assessment Information in Context -- Introduction.

Performance data need a context to meaningfully interpret the data. One method of providing contextfor an individual unit's performance is to compare it with other similar units. This study compares three methods for selecting similar units: cluster groupings, index groups, and benchmark groups. Each of the three methods is evaluated on a number of criteria, primarily the minimization of within-group variance. Benchmark groups are the best at reducing the variation within the selected groups, and they resist attempts to "label" the groupings. Cluster groups are a close second to benchmarks in the minimization of variability within groups and are considerably easier to compute and administer. However, clustering allows labeling that could stigmatize the groups and threshold effects that might influence judgments about performance. Index groups, while simple, do not perform well on any of the other criteria.

Indicators of program performance are used as evaluative measures in a variety of fields. A particularly vexing problem for evaluation is the development of empirically based performance expectations. Should program sponsors be satisfied with average performance? How should evaluators account for client differences? This article presents a statistical technique for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit, in this case a school district, a unique comparison group is selected. The performance of the district is compared to the performance of its benchmark group. Then the credibility, predictability, and equity of the method are tested. The approach meets or exceeds these test criteria and appears to be a viable, albeit controversial, approach for developing comparative performance standards.