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.

Development and validation of a Rasch-derived CES-D short form.

Abstract: The current study presents a Rasch-derived short form of the Center for Epidemiologic Studies-Depression scale (CES-D) for use as a depression screening tool in the general population. In contrast to short forms developed with reliance on classical measurement techniques, those developed using techniques based on item response theory produce a measure that offers true interval scaling, provide enhanced information about responders with extreme scores, and expand understanding of the underlying latent structure. Cross-validation of the Rasch-derived CES-D short form supported its utility and structural validity across samples. Tests of structural validity using latent variable modeling methodology indicated that a hierarchical, single-factor model of depression had the best fit for the original full form and the Rasch-derived short form of the CES-D. This finding challenges depression researchers and theorists to reconsider the interfactor relationships in the study and assessment of depression.

An Alternative Approach to Method Effects by Using Latent-Variable Models: Applications in Organizational Behavior Research

Abstract: Recent research in the area of organizational behavior on social desirability and negative affectivity as potential sources of artifactual covariance is reviewed. Next, an example of a latent-variable structural equation approach is presented as a more appropriate strategy for analyzing method effects that circumvents problems associated with prior statistical techniques. This example illustrates the specification of a structural equation model with and without method effects and describes the sequence of model comparisons that provides direct tests for the presence of method effects and for the impact of these effects on estimates of substantive relationships. Finally, this latent-variable approach is discussed with regard to other potential applications involving method effects. An important stream of research on method variance in organizational behavior studies has attempted to directly measure some of the variables associated with common effects of method

Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters.

Abstract: Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on covariate effects, model size, and class-specific versus class-invariant parameters. When fitting true models, parameter coverage is good for most parameters even for the smallest class separation investigated in this study (0.5 SD between 2 classes). The same holds for convergence rates. Correct class assignment is unsatisfactory for the small class separation without covariates, but improves dramatically with increasing separation, covariate effects, or both. Model performance is not influe...

Latent Class Models for Stage-Sequential Dynamic Latent Variables

Abstract: Stage-sequential dynamic latent variables are of interest in many longitudinal studies. Measurement theory for these latent variables, called Latent Transition Analysis (LTA), can be found in recent generalizations of latent class theory. LTA expands the latent Markov model to allow applications to more complex latent variables and the use of multiple indicators. Because complex latent class models result in sparse contingency tables, that may lead to poor parameter estimation, a simulation study was conducted in order to determine whether model parameters are recovered adequately by LTA, and whether additional indicators result in better measurement or in impossibly sparse tables. The results indicated that parameter recovery was satisfactory overall, although as expected the standard errors were large in some conditions with few subjects. The simulation also indicated that at least within the conditions examined here, the benefits of adding indicators outweigh the costs. Additional indicators improved s...