Latent class analysis of survey error - Info and Reading Options
By Paul P. Biemer

"Latent class analysis of survey error" was published by Wiley in 2011 - Hoboken, N.J, it has 387 pages and the language of the book is English.
“Latent class analysis of survey error” Metadata:
- Title: ➤ Latent class analysis of survey error
- Author: Paul P. Biemer
- Language: English
- Number of Pages: 387
- Publisher: Wiley
- Publish Date: 2011
- Publish Location: Hoboken, N.J
“Latent class analysis of survey error” Subjects and Themes:
- Subjects: ➤ Error analysis (Mathematics) - Estimation theory - Sampling (Statistics) - Error analysis (mathematics) - Surveys - Sampling (statistics) - Mathematics - Sampling Studies - Selection Bias - Statistics as Topic
Edition Specifications:
- Pagination: xix, 387 p. :
Edition Identifiers:
- The Open Library ID: OL25059529M - OL16182650W
- Online Computer Library Center (OCLC) ID: 639164022
- Library of Congress Control Number (LCCN): 2010040979
- ISBN-13: 9780470289075 - 9780470891155 - 9780470891148
- All ISBNs: 9780470289075 - 9780470891155 - 9780470891148
AI-generated Review of “Latent class analysis of survey error”:
"Latent class analysis of survey error" Description:
The Open Library:
"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"-- "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"--
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