January 18, 2011
New Book First of Its Kind in Nonsampling Error Methodology
RESEARCH TRIANGLE PARK, N.C.—A new book written by a researcher at RTI International, Latent Class Analysis of Survey Error, addresses the errors in data collected using sample surveys, the nature and magnitude of those errors, and their effects on survey estimates.
This book, written by Paul Biemer, Ph.D., a distinguished fellow at RTI, is the first book of its kind to examine the methods needed in the modeling and estimation of classification errors, particularly latent class analysis techniques. By combining theoretical, methodological and practical aspects of estimating classification error, the book provides a guide for the practitioner as well as a text for the student of survey error evaluation.
The book specifically focuses on nonsampling errors, which are errors that can arise from a variety of sources ranging from interviewers to data processors. Evaluating nonsampling errors is quite difficult and often requires data not normally collected in the typical survey.
Latent Class Analysis of Survey Error discusses methods for evaluating the nonsampling error in survey data focusing primarily on data that are categorical and errors that result in misclassifications. It concentrates on a general set of models and techniques referred to collectively as latent class analysis. Biemer is an expert in the field of survey measurement error and has published extensively in his areas of research interest, which include survey design and analysis, general survey methodology, and nonsampling error modeling and evaluation.
He also serves as the associate director for Survey Research and Development for the Odum Institute at the University of North Carolina at Chapel Hill.
Published by Wiley as part of its series in survey methodology, Latent Class Analysis of Survey Error is available on leading commercial bookseller websites.