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Showing 3 results for Total Survey Error
R Alimohammadi,
Volume 14, Issue 2 (3-2010)
Abstract
, , ,
Volume 22, Issue 1 (12-2017)
Abstract
Latent class analysis (LCA) is a method of evaluating non sampling errors, especially measurement error in categorical data. Biemer (2011) introduced four latent class modeling approaches: probability model parameterization, log linear model, modified path model, and graphical model using path diagrams. These models are interchangeable. Latent class probability models express likelihood of cross-classification tables in term of conditional and marginal probabilities for each cell. In this approach model parameters are estimated using EM algorithm. To test latent class model chi-square statistic is used as a measure of goodness-of-fit. In this paper we use LCA and data from a small-scale survey to estimate misclassification error (as a measurement error) of students who had at least a failing grade as well as misclassification error of students with average grades below 14.
Samaneh Beheshtizadeh, Hamidreza Navvabpour,
Volume 25, Issue 1 (1-2021)
Abstract
Evidence-based management and development planning relies on official statistics. There are some obstacles that make it impossible to do a single-mode survey. These obstacles are the sampling frame, time, budget, and accuracy of measurement of each mode. Always we can not use a single-mode survey because of these factors. So we need to use other data collection methods to overcome these obstacles. This method is called the mixed-mode survey, which is a combination of several modes. In this article, we show that mixed-mode surveys can produce more accurate official statistics than single-mode surveys.
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