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:: Volume 22, Issue 1 (12-2017) ::
Andishe 2017, 22(1): 85-96 Back to browse issues page
An application of Measurement error evaluation using latent class analysis
Abstract:   (838 Views)

‎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.

Keywords: ‎Total survey error‎, ‎measurement error‎, ‎probability model‎, ‎latent class analysis‎, ‎gold standard‎, ‎misclassification error‎.
Full-Text [PDF 352 kb]   (284 Downloads)    
Type of Study: Applicable | Subject: Special
Received: 2015/09/25 | Accepted: 2017/03/15 | Published: 2017/03/15
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An application of Measurement error evaluation using latent class analysis. Andishe. 2017; 22 (1) :85-96
URL: http://andisheyeamari.irstat.ir/article-1-392-en.html

Volume 22, Issue 1 (12-2017) Back to browse issues page
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