Introduction Latent Variables for Estimating Airline Assessment

Latent variables may be useful in pain’s assessment

BACKGROUND Unobserved "latent" variables have the potential to minimize "measurement error" inherent to any single clinical assessment or categorical diagnosis. OBJECTIVES To demonstrate the potential utility of latent variable constructs in pain's assessment. DESIGN We created two latent variables representing depressive symptom-related pain (Pd) and its residual, "somatic" pain (Ps), from...

Latent variables in econometrics

Unobservable variables in econometrics are represented in one of three ways: by variables contaminated by measurement errors, by proxy variables, or by various manifest indicators and/or causes. This paper contains a discussion of models involving each of these representations, and highlights certain interesting implications that have been ‘insufficiently emphasized or completely unrecognized i...

Multiple hypothesis testing, adjusting for latent variables

In high throughput settings we inspect a great many candidate variables (e.g. genes) searching for associations with a primary variable (e.g. a phenotype). High throughput hypothesis testing can be made difficult by the presence of systemic effects and other latent variables. It is well known that those variables alter the level of tests and induce correlations between tests. It is less well kn...

Plausible Values for Latent Variables Using Mplus

Estimating and Visualizing Nonlinear Relations Among Latent Variables: A Semiparametric Approach.

The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear relationships among latent variables using mixture...