By Paul P. Biemer
Combining theoretical, methodological, and functional features, Latent type research of Survey errors effectively courses readers throughout the exact interpretation of survey effects for caliber evaluate and development. This publication is a entire source at the key statistical instruments and strategies hired through the modeling and estimation of category mistakes, that includes a distinct specialise in either latent category research (LCA) strategies and types for express info from complicated pattern surveys.Drawing from his large adventure within the box of survey technique, the writer examines early types for survey size errors and identifies their similarities and alterations in addition to their strengths and weaknesses. next chapters deal with issues with regards to modeling, estimating, and lowering mistakes in surveys, including:Measurement blunders modeling forcategorical dataThe Hui-Walter version and othermethods for 2 indicatorsThe EM set of rules and its position in latentclass version parameter estimationLatent classification versions for 3 ormore indicatorsTechniques for interpretation of modelparameter estimatesAdvanced themes in LCA, together with sparse facts, boundary values, unidentifiability, and native maximaSpecial issues for interpreting datafrom clustered and unequal likelihood samples with nonresponseThe present country of LCA and MLCA (multilevel latent classification analysis), and an insightful dialogue on components for additional researchThroughout the booklet, greater than a hundred real-world examples describe the offered equipment intimately, and readers are guided by using lEM software program to duplicate the awarded analyses. Appendices provide a primer on express facts research, and a comparable website homes the lEM software.Extensively class-tested to make sure an available presentation, Latent category research of Survey mistakes is a superb publication for classes on size mistakes and survey method on the graduate point. The ebook additionally serves as a invaluable reference for researchers and practitioners operating in enterprise, govt, and the social sciences who improve, enforce, or overview surveys.
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Additional info for Latent Class Analysis of Survey Error (Wiley Series in Survey Methodology)
This information is quite informative for assessing the accuracy of the estimates and whether nonsampling error should be a concern for interpreting the research findings. Likewise, an analysis of measurement error could be useful for explaining why an analysis failed to replicate findings in the literature or why unexpected and inexplicable relationships among the variables were found. To understand the causes of nonsampling error and develop strategies for its prevention, the errors must be measured often.
Chapter 4 describes the standard latent class (LC) model and shows why its assumptions are unnecessarily restrictive for most applications. It introduces a more general modeling framework based on a loglinear model with latent variables and shows how the standard LC model is just a special case of the more general model. Within this general modeling framework, a wide range of error structures and error evaluation designs is discussed and analyzed using familiar loglinear modeling notation, methods and techniques.
A false-positive response). Note that, although the table provides ample evidence of error, it alone is not sufficient to determine which of the two responses for a student is the correct response. For that purpose, data that can be regarded as highly accurate or truthful are required. An important question in survey research is—which question, Q1 or Q2, is better for collecting these data? That is, which of the two questions elicits the truth more often? For that, Kreuter et al. obtained the official transcripts for all 954 students and determined which students had ever received a “D” or “F” grade.