The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements

The usefulness of Bayesian methods in estimating complex statistical models is undeniable. From a Bayesian standpoint, this paper aims to demonstrate the capacity of Bayesian methods and propose a comprehensive model combining both a measurement model (e.g., an item response model, IRM) and a struct...

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Autores principales:	Chueh An Hsieh, Alexander von Eye
Formato:	article
Lenguaje:	EN ES
Publicado:	Universidad de San Buenaventura 2010
Materias:	Bayesian inference item response model latent growth curve analysis simulation generalized linear latent and mixed model Psychology BF1-990
Acceso en línea:	https://doaj.org/article/7cd1cc3593d846deba4ab3b4fdd6a432
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spelling	oai:doaj.org-article:7cd1cc3593d846deba4ab3b4fdd6a4322021-11-25T02:23:56ZThe best of both worlds: a joint modeling approach for the assessment of change across repeated measurements10.21500/20112084.8622011-20842011-7922https://doaj.org/article/7cd1cc3593d846deba4ab3b4fdd6a4322010-06-01T00:00:00Zhttps://revistas.usb.edu.co/index.php/IJPR/article/view/862https://doaj.org/toc/2011-2084https://doaj.org/toc/2011-7922The usefulness of Bayesian methods in estimating complex statistical models is undeniable. From a Bayesian standpoint, this paper aims to demonstrate the capacity of Bayesian methods and propose a comprehensive model combining both a measurement model (e.g., an item response model, IRM) and a structural model (e.g., a latent variable model, LVM). That is, through the incorporation of the probit link and Bayesian estimation, the item response model can be introduced naturally into a latent variable model. The utility of this IRM-LVM comprehensive framework is investigated with a real data example and promising results are obtained, in which the data drawn from part of the British Social Attitudes Panel Survey 1983-1986 reveal the attitude toward abortion of a representative sample of adults aged 18 or older living in Great Britain. The application of IRMs to responses gathered from repeated assessments allows us to take the characteristics of both item responses and measurement error into consideration in the analysis of individual developmental trajectories, and helps resolve some difficult modeling issues commonly encountered in developmental research, such as small sample sizes, multiple discretely scaled items, many repeated assessments, and attrition over time.Chueh An HsiehAlexander von EyeUniversidad de San BuenaventuraarticleBayesian inferenceitem response modellatent growth curve analysissimulationgeneralized linear latent and mixed modelPsychologyBF1-990ENESInternational Journal of Psychological Research, Vol 3, Iss 1 (2010)
institution	DOAJ
collection	DOAJ
language	EN ES
topic	Bayesian inference item response model latent growth curve analysis simulation generalized linear latent and mixed model Psychology BF1-990
spellingShingle	Bayesian inference item response model latent growth curve analysis simulation generalized linear latent and mixed model Psychology BF1-990 Chueh An Hsieh Alexander von Eye The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
description	The usefulness of Bayesian methods in estimating complex statistical models is undeniable. From a Bayesian standpoint, this paper aims to demonstrate the capacity of Bayesian methods and propose a comprehensive model combining both a measurement model (e.g., an item response model, IRM) and a structural model (e.g., a latent variable model, LVM). That is, through the incorporation of the probit link and Bayesian estimation, the item response model can be introduced naturally into a latent variable model. The utility of this IRM-LVM comprehensive framework is investigated with a real data example and promising results are obtained, in which the data drawn from part of the British Social Attitudes Panel Survey 1983-1986 reveal the attitude toward abortion of a representative sample of adults aged 18 or older living in Great Britain. The application of IRMs to responses gathered from repeated assessments allows us to take the characteristics of both item responses and measurement error into consideration in the analysis of individual developmental trajectories, and helps resolve some difficult modeling issues commonly encountered in developmental research, such as small sample sizes, multiple discretely scaled items, many repeated assessments, and attrition over time.
format	article
author	Chueh An Hsieh Alexander von Eye
author_facet	Chueh An Hsieh Alexander von Eye
author_sort	Chueh An Hsieh
title	The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
title_short	The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
title_full	The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
title_fullStr	The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
title_full_unstemmed	The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
title_sort	best of both worlds: a joint modeling approach for the assessment of change across repeated measurements
publisher	Universidad de San Buenaventura
publishDate	2010
url	https://doaj.org/article/7cd1cc3593d846deba4ab3b4fdd6a432
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The best of both worlds: a joint modeling approach for the assessment of change across repeated measurements

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