00000cmm a22000003i 4500 UP-1685594773862075496 Buklod 20170314171118.0 g||| | || || ta 170314s2011 nju u eng d 9780470891155 (eBook) 9780470891148 (eBook) (iLib)UPBAG-00023564337 DLC DML BAG rda eng ML-ebook Biemer, Paul P. author. Latent class analysis of survey error [electronic resource] Paul P. Biemer. Hoboken, N.J. John Wiley & Sons Inc. 2011. 1 online resource (xix, 387 pages) illustrations text txt rdacontent computer rdamedia online resource cr rdacarrier Wiley series in survey methodology IP-based subscription, access limited to within on-campus computer network. Access via Electronic Resources of the UPD University Library Website. "This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the estimates and make use of the results in reducing the error for future surveys. The book focuses on models that are appropriate for categorical data, although there are references to the differences and special problems that arise in the analysis and modeling of error for continuous data. Though the primary modeling method that is described is latent class analysis (LCA), a wide range of related models and applications are also discussed"--Provided by publisher. "This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the estimates and make use of the results in reducing the error for future surveys"--Provided by publisher. Electronic reproduction. Wiley Wiley & Sons, Inc. 2011. Available via World Wide Web through Wiley Online Library. Error analysis (Mathematics) Sampling (Statistics) Estimation theory. Wiley Online Library. Available for University of the Philippines Baguio via Wiley Online Library. Click here to access http://dx.doi.org/10.1002/9780470891155 (viewed 14 March 2017) FO Monograph UPBAG UPBAG-MAIN ML-ebook Electronic Resource