National Assessment Approach to Sampling Error Estimation

By R Folsom

Beginning in 1969 and continuing through 1982, RTI conducted the first 12 survey years of the National Assessment of Educational Progress (NAEP). RTI’s role included developing analytic weights and providing technical support for calculating estimates and their standard errors. Ralph Folsom, PhD, led RTI’s effort to evaluate sampling error estimation methodologies appropriate to the complex NAEP design, including a comparison of the Taylor series linearization method and jackknife variance estimation methodology. Later, his work on developing Taylor series standard errors for NAEP balanced effects became a basis for the REGRESS procedure in RTI’s SUDAAN® software. This monograph provides a historical perspective to the continuing evolution of complex survey analysis methods.

Bibliography

Folsom, R. (2014). National Assessment Approach to Sampling Error Estimation. (RTI Press Publication No. BK-0013-1412). Research Triangle Park, NC: RTI Press. DOI: 10.3768/rtipress.2014.bk.0013.1412

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Authors

R Folsom — Ralph Folsom, PhD, is an expert in the design and analysis of complex probability samples. Working on the nation's largest household survey (the National Survey on Drug Use and Health or NSDUH), Dr. Folsom initiated innovative weight adjustment methods based on his logistic response propensity and exponential poststratification models. This pioneering work led to the sophisticated GEM weight adjustment methods currently employed for NSDUH. Dr. Folsom also introduced model-based imputations for missing frequency of use and income data items, and he has been an influential collaborator in the development of NSDUH's current Predictive Mean Neighborhoods (PMN) imputation methodology. Dr. Folsom has recently led RTI's innovative work in small area estimation research. In addition to his innovative work on many complex survey efforts, Dr. Folsom has made significant contributions to the development of RTI's computer software for survey data analysis, SUDAAN.

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