Latent and observable variables: Difference between revisions

Revision as of 04:14, 27 May 2021

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Latent-variable methodology is used in many branches of [[medicine]]. A class of problems that naturally lend themselves to latent variables approaches are [[longitudinal studies]] where the time scale (e.g. age of participant or time since study baseline) is not synchronized with the trait being studied. For such studies, an unobserved time scale that is synchronized with the trait being studied can be modeled as a transformation of the observed time scale using latent variables. Examples of this include [[Nonlinear_mixed-effects_model#Example: Disease progression modeling|disease progression modeling]] and [[Nonlinear_mixed-effects_model#Example: Growth analysis|modeling of growth]] (see box).

===Other fields===

Applications of latent variable approaches go beyond psychology, economics and medicien. Latent variable methods have been utilized in many other fields like sociology and religious studies, as they have been used to measure equality, democracy and spiritual capital.

In dealing with big data, latent variable approaches have been considered as a powerful tool as well as one necessary. <ref>{{cite book |last=Liu |first=Alex |title=Structural Equation Modeling and Latent Variable Approaches |publisher=Wiley |year=2015 |doi = 10.1002/9781118900772.etrds0325 }}</ref>

==Inferring latent variables==