Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.

Locally dependent latent trait model and the dutch identity revisited

Abstract: In this paper, we propose a class of locally dependent latent trait models for responses to psychological and educational tests. Typically, item response models treat an individual's multiple response to stimuli as conditional independent given the individual's latent trait. In this paper, instead the focus is on models based on a family of conditional distributions, or kernel, that describes joint multiple item responses as a function of student latent trait, not assuming conditional independence. Specifically, we examine a hybrid kernel which comprises a component for one-way item response functions and a component for conditional associations between items given latent traits. The class of models allows the extension of item response theory to cover some new and innovative applications in psychological and educational research. An EM algorithm for marginal maximum likelihood of the hybrid kernel model is proposed. Furthermore, we delineate the relationship of the class of locally dependent models and the log-linear model by revisiting the Dutch identity (Holland, 1990).

Stochastic Approximation Methods for Latent Regression Item Response Models

Abstract: This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response la...

The structure of Web-based information systems satisfaction: Testing of competing models

Abstract: User satisfaction has become one of the most important measures of the success or effectiveness of information systems (IS). In the current study, the dimensionality of Web-based information systems (WIS) satisfaction was first examined. Two composite latent variable models with factor-order structures were then empirically tested and compared to describe the relationships among observable variables concerned with WIS satisfaction. Using data from a sample of 515 university students, a third-order composite latent variable model was retained based on statistical and theoretical criteria. At the third-order level, WIS satisfaction is determined by two second-order constructs: Web information satisfaction and Web system satisfaction. Web information satisfaction is determined by understandability, reliability, and usefulness, while Web system satisfaction is determined by access, usability, and navigation. Overall, the model provides a good fit to the data and is theoretically valid, reflecting logical consistency. Implications of the current investigation for practice and research are provided. © 2008 Wiley Periodicals, Inc.

Model Implied Instrumental Variables (MIIVs): An Alternative Orientation to Structural Equation Modeling.

Abstract: Few dispute that our models are approximations to reality. Yet when it comes to structural equation models (SEMs), we use estimators that assume true models (e.g. maximum likelihood) and that can create biased estimates when the model is inexact. This article presents an overview of the Model Implied Instrumental Variable (MIIV) approach to SEMs from Bollen (1996). The MIIV estimator using Two Stage Least Squares (2SLS), MIIV-2SLS, has greater robustness to structural misspecifications than system wide estimators. In addition, the MIIV-2SLS estimator is asymptotically distribution free. Furthermore, MIIV-2SLS has equation-based overidentification tests that can help pinpoint misspecifications. Beyond these features, the MIIV approach has other desirable qualities. MIIV methods apply to higher order factor analyses, categorical measures, growth curve models, dynamic factor analysis, and nonlinear latent variables. Finally, MIIV-2SLS permits researchers to estimate and test only the latent variable model or any other subset of equations. In addition, other MIIV estimators beyond 2SLS are available. Despite these promising features, research is needed to better understand its performance under a variety of conditions that represent empirical applications. Empirical and simulation examples in the article illustrate the MIIV orientation to SEMs and highlight an R package MIIVsem that implements MIIV-2SLS.