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The objectives of this project were to construct two sets of balanced repeated replication (BRR) weights for two National Education Longitudinal Study of 1988 (NELS:88) sets of weights: one based on the third follow-up survey done in 1994 (NELS88:94); and the second based on the fourth follow-up survey done in 2000 (NELS88:2000). These weights are described here. "F3QWT" is the first weight. It applies to all members of the third follow-up sample who completed an interview in 1994, regardless of their participation status in previous rounds. When used with the appropriate sample flags, it allows projections to the following populations: (1) Spring 1988 8th graders eligible to complete questionnaires in 1992 and 1994, regardless of 1988 eligibility; (2) Spring 1990 10th graders eligible to complete questionnaires in 1992 and 1994, regardless of 1988 eligibility and 1990 participation; and (3) Spring 1992 12th graders eligible to complete questionnaires in 1992 and 1994, regardless of 1988 and 1990 eligibility and 1992 participation. "F4PNLWT" is the second weight. This panel weight applies to sample members who completed questionnaires in all five rounds of NELS:88 and were in school in the spring of 1988. It can be used to make projections to the population of spring 1988 8th graders. Four appendices present: (1) Weighting Specifications of BRR Weights for Third Follow-Up; (2) Hadamard Matrix Used to Construct BRR Weights; (3) Variables Used for Calibration of Weights; and (4) Weighting Specifications of BRR Weights for Fourth Follow-Up Full Panel Weight.

Born January 11, 1921 in New York City, Monroe Sirken grew up in a suburb of Pasadena, California. He earned B.A. and M.A. degrees in sociology at UCLA in 1946 and 1947, and a Ph.D. in 1950 in sociology with a minor in mathematics at the University of Washington in 1950 where Professor Z. W. Birnbaum was his mentor and thesis advisor. As a Post-Doctoral Fellow of the Social Science Research Council, Monroe spent 1950--1951 at the Statistics Laboratory, University of California at Berkeley and the Office of the Assistant Director for Research, U.S. Bureau of the Census in Suitland, Maryland. Monroe visited the Census Bureau at a time of great change in the use of sampling and survey methods, and decided to remain. He began his government career there in 1951 as a mathematical statistician, and moved to the National Office of Vital Statistics (NOVS) in 1953 where he was an actuarial mathematician and a mathematical statistician. He has held a variety of research and administrative positions at the National Center for Health Statistics (NCHS) and he was the Associate Director, Research and Methodology and the Director, Office of Research and Methodology until 1996 when he became a senior research scientist, the title he currently holds. Aside from administrative responsibilities, Monroe's major professional interests have been conducting and fostering survey and statistical research responsive to the needs of federal statistics. His interest in the design of rare and sensitive population surveys led to the development of network sampling which improves precision by linking multiple selection units to the same observation units. His interest in fostering research on the cognitive aspects of survey methods led to the establishment of permanent questionnaire design research laboratories, first at NCHS and later at other federal statistical agencies here and abroad., Comment: Published in at http://dx.doi.org/10.1214/07-STS245 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Synthetic estimation is a statistical technique that estimates small-area statistics by combining national estimates of the relevant characteristics with estimates of other known characteristics of the small geographic area. The advantages of the synthetic estimation approach to local estimation are its intuitive appeal, its simplicity, and its low cost. A major disadvantage is its possibile lack of sensitivity to certain local characteristics. Another method used for the same purpose is the "nearly unbiased" estimator. The mathematics of both methods are presented, including formulas for estimating mean square error and average mean square error. An evaluation of synthetic estimates is demonstrated by comparing the results of a 50-cell grid with the results from 2, 4, 8, and 16-cell grids. (Author/CTM)