The well‐documented aspiration‐expectation gap refers to lower socioeconomic status (SES) adolescents of color expecting to attain occupations with lower pay and status than the occupations to which they aspire. Sociopolitical inequity, such as structural racism and asymmetrical access to resources, may explain this gap. This article examines the capacity to cope with sociopolitical inequity (i.e., sociopolitical development) and its relationship to vocational expectations among this population. Analyses revealed that sociopolitical development was associated with higher vocational expectations and may contribute to reducing the vocational aspiration‐expectation gap among lower SES adolescents of color. Furthermore, career counseling, guidance, and psychosocial interventions may be augmented by incorporating sociopolitical development.

The application of multidimensional item response theory models to repeated observations has demonstrated great promise in developmental research. It allows researchers to take into consideration both the characteristics of item response and measurement error in longitudinal trajectory analysis, which improves the reliability and validity of the latent growth curve (LGC) model. The purpose of this study is to demonstrate the potential of Bayesian methods and the utility of a comprehensive modeling framework, the one combining a measurement model (e.g., a multidimensional graded response model, MGRM) with a structural model (e.g., an associative latent growth curve analysis, ALGC). All analyses are implemented in WinBUGS 1.4.3 ( Spiegelhalter, Thomas, Best, & Lunn, 2003 ), which allows researchers to use Markov chain Monte Carlo simulation methods to fit complex statistical models and circumvent intractable analytic or numerical integrations. The utility of this MGRM-ALGC modeling framework was investigated with both simulated and empirical data, and promising results were obtained. As the results indicate, being a flexible multivariate multilevel model, this MGRM-ALGC model not only produces item parameter estimates that are readily estimable and interpretable but also estimates the corresponding covariation in the developmental dimensions. In terms of substantive interpretation, as adolescents perceived themselves more socially isolated, the chance that they are engaged with delinquent peers becomes profoundly larger. Generally, boys have a higher initial exposure extent than girls. However, there is no gender difference associated with other latent growth parameters.

The capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, model averaging and model selection. As a simplified demonstration, we illustrate (1) how Bayesians test and compare two non-nested growth curve models using Bayesian estimation with non-informative prior; (2) how Bayesians model and handle missing outcomes in the context of missing values; and (3) how Bayesians incorporate data-based evidence from a previous data set, construct informative priors and treat them as extra information while conducting an up-to-date analogy analysis. IntroductionThe capacity of Bayesian methods in estimating complex statistical models is undeniable. Bayesian data analysis is seen as having a range of advantages, such as an intuitive probabilistic interpretation of the parameters of interest, the efficient incorporation of prior information to empirical data analysis, the ability to take account of model uncertainty among different models and to draw combined inferences when there is no single pre-eminent model, and so on (). For instance, in contrast to classical hypothesis testing, 1 Bayesians are able to produce comparisons of fit between non-nested models (Congdon 2005(Congdon , 2006. Thus, it is appropriate for Bayesians to test non-nested models which differ in other ways than the inclusion and/or exclusion of particular parameters. Several methods have been proposed for model choice based on Bayesian principles; for instance, Spiegelhalter et al. (2002) propose the deviance information criterion (DIC), which includes many features of classical model assessment, such as penalizing complexity and requiring accurate predictions. Because of this favourable characteristic, making nonnested model comparisons turns out to be possible; in the present study, we approach growth models from two distinct perspectives: a two-level hierarchical linear model

No abstract

Lo mejor de ambos mundos: una propuesta de modelamiento combinado para la evaluación del cambio a lo largo de mediciones repetidas. Chueh-An Hsieh and Alexander von EyeMichigan State University, East Lansing, USA ABSTRACTThe 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 Key words: Bayesian inference, item response model, latent growth curve analysis, simulation, generalized linear latent and mixed model RESUME 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.Palabras clave: Inferencia Bayesiana, modelo de respuesta al ítem, análisis de curva de crecimiento latente, simulación, modelo linear generalizado latente y mixto.