Acceso usuarios
City of East Lansing, Estados Unidos
La utilidad de los métodos Bayesianos en la estimación de modelos estadísticos complejos es innegable. Desde un punto de vista Bayesiano, el presente artículo busca demostrar la capacidad de los métodos Bayesianos y proponer un modelo exhaustivo que combina un modelo de medición y un modelo estructural. La utilidad de este método combinado se investiga usando datos reales tomados de una encuesta sobre actitudes sociales. El método combinado permite extraer las características de las respuestas a los ítems como de los errores en la medición para el análisis individual de trayectorias del desarrollo. Tales resultados permiten resolver asuntos que se presentan en investigación en psicología del desarrollo, e.g., tamaños de muestra pequeños, evaluaciones repetidas, etc.
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.
