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...
- Autores:
-
Hsieh, Chueh An
Von Eye, Alexander
- Tipo de recurso:
- Fecha de publicación:
- 2010
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/6549
- Acceso en línea:
- http://hdl.handle.net/10819/6549
- Palabra clave:
- Bayesian inference
Generalized linear latent and mixed mode
Item response model
Latent growth curve analysis
Simulation
Análisis de curva de crecimiento latente
Inferencia Bayesiana
Modelo de respuesta al ítem
Modelo linear generalizado latente y mixto
Simulación
Estadística
Investigación cuantitativa
- Rights
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
| Summary: | 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 |
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