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.

Measurement Invariance for Latent Constructs in Multiple Populations: A Critical View and Refocus

Abstract: Popular measurement invariance testing procedures for latent constructs evaluated by multiple indicators in distinct populations are revisited and discussed. A frequently used test of factor loading invariance is shown to possess serious limitations that in general preclude it from accomplishing its goal of ascertaining this invariance. A process of mean intercept invariance evaluation is subsequently examined, and it is indicated that within this framework there is no statistical test available for group identity in them. Rather than pursuing these popular and widely used invariance testing procedures, it is recommended that empirical studies on constructs in multiple populations be concerned in general with alternative measurement invariance examination and ensuring the inclusion of their invariance conditions in models aimed at investigating group differences and similarities in latent means, variances, and interrelationships. The discussion is illustrated using data from a cognitive intervention study.

A variational autoencoder solution for road traffic forecasting systems: Missing data imputation, dimension reduction, model selection and anomaly detection

Abstract: Efforts devoted to mitigate the effects of road traffic congestion have been conducted since 1970s. Nowadays, there is a need for prominent solutions capable of mining information from messy and multidimensional road traffic data sets with few modeling constraints. In that sense, we propose a unique and versatile model to address different major challenges of traffic forecasting in an unsupervised manner. We formulate the road traffic forecasting problem as a latent variable model, assuming that traffic data is not generated randomly but from a latent space with fewer dimensions containing the underlying characteristics of traffic. We solve the problem by proposing a variational autoencoder (VAE) model to learn how traffic data are generated and inferred, while validating it against three different real-world traffic data sets. Under this framework, we propose an online unsupervised imputation method for unobserved traffic data with missing values. Additionally, taking advantage of the low dimension latent space learned, we compress the traffic data before applying a prediction model obtaining improvements in the forecasting accuracy. Finally, given that the model not only learns useful forecasting features but also meaningful characteristics, we explore the latent space as a tool for model and data selection and traffic anomaly detection from the point of view of traffic modelers.

Latent Variable Modeling with R

Abstract: This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed Each chapter also features an introduction, summary, and suggested readings A glossary of the text’s boldfaced key terms and key R commands serve as helpful resources The book is accompanied by a website with exercises, an answer key, and the in-text example data sets Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data -wwwroutledgecom/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book’s practical approach The book provides sufficient conceptual background information to serve as a standalone text Familiarity with basic statistical concepts is assumed but basic knowledge of R is not

Structural-equation models of current drug use: are appropriate models so simple(x)?

Abstract: The simplex and common-factor models of drug use were compared using maximum-likelihood estimation of latent variable structural models in two samples: a sample of 226 high school students, using ratio-scale measures of current drug use, and a sample of 310 industrial workers and 811 college students, using ordinal-scale measures of current drug use. Latent variables of alcohol, marijuana, enhancer hard drugs, and dampener hard drugs were specified in a series of structural models. Contrary to previous findings with cumulative drug-use data, the common-factor model provided a more acceptable representation of the observed current-use data than did the simplex model in both samples. In addition, the similarity of results across both of these samples supports recent contentions by Huba and Bentler (1982) that quantitatively measured variables are not necessarily superior to qualitative, ordinal indicators in latent variable models of drug use.

Bayesian Semiparametric Structural Equation Models with Latent Variables

Abstract: Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semiparametric Bayesian SEMs, which allow mixed categorical and continuous manifest variables while also allowing the latent variables to have unknown distributions. In order to include typical identifiability restrictions on the latent variable distributions, we rely on centered Dirichlet process (CDP) and CDP mixture (CDPM) models. The CDP will induce a latent class model with an unknown number of classes, while the CDPM will induce a latent trait model with unknown densities for the latent traits. A simple and efficient Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using simulated examples, and several applications.