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Fit statistics provide a direct measure of assessment accuracy by analyzing the fit of measurement models to an individual's (or group's) response pattern. Students that lose interest during the assessment, for example, will miss exercises that are within their abilities. Such students will respond correctly to some more difficult items and incorrectly to some less difficult items. Most assessment programs, including the National Assessment of Educational Progress (NAEP), currently either ignore such response anomalies or assume they do not exist. The use of a weighted-total-fit-mean-square as a measure of assessment accuracy was investigated using data from the 1990 and 1992 NAEP assessments. The distribution of fit across individuals was examined for fit and item-type differences, and the practical significance of this type of fit statistic was explored. It is concluded that this person-fit statistic has little to offer in the analysis of traditional NAEP data. Sixteen tables present analysis results. Appendix A contains 12 subscale tables, and Appendix B presents software routines. (Contains 62 references.) (SLD)
Since 1969, the National Assessment of Educational Progress (NAEP) has worked to provide reliable information on the academic performance of U.S. students in various subjects. This document is the most comprehensive listing of government-funded NAEP publications. By cataloguing this collection, the directory provides something of a history of the NAEP. Publications are organized by publication year and grouped into the following categories: (1) national reports; (2) state reports; (3) abbreviated documents; (4) technical reports; (5) focused reports and special studies; (6) conference proceedings and commissioned papers; (7) NAEP evaluation studies and grant publications; and (8) subject area objectives, frameworks, and achievement levels. Each publication category is preceded by descriptions of content, purposes, and intended audiences. (SLD)
The purpose of this book is to evaluate a new approach to the analysis and reporting of the large-scale surveys for the National Assessment of Educational Progress carried out for the National Center for Education Statistics. The need for a new approach was driven by the demands for secondary analysis of the survey data by researchers who needed analyses more detailed than those published by NCES, and the need to accelerate the processing and publication of results from the surveys. This new approach is based on a full multilevel statistical and psychometric model for students’ responses to the test items, taking into account the design of the survey, the backgrounds of the students, and the classes, schools and communities in which the students were located. The authors detail a fully integrated single model that incorporates both the survey design and the psychometric model by extending the traditional form of the psychometric model to accommodate the design structure while allowing for student, teacher, and school covariates.