What is a latent variable?
A latent variable is a variable which is not directly observable and is
assumed to affect the response variables (manifest variables)...
For more on Latent Variables read the explanation...
Consider the following sentence: “Einstein would not have been
able to come up with his e mc2 had he not possessed such an
extraordinary intelligence.” What does this sentence express? It
relates observable behavior (Einstein’s writing e mc2) to an
unobservable attribute (his extraordinary intelligence), and it does
so by assigning to the unobservable attribute a causal role in
bringing about Einstein’s behavior. In psychology, there are many
constructs that play this type of role in theories of human behavior;
examples are constructs like extraversion, spatial ability, selfefficacy,
and attitudes. Such variables are usually referred to as
latent variables. It is common to investigate the structure and
effect of unobservables like intelligence through the analysis of
interindividual differences data by statistically relating covariation
between observed variables to latent variables. This is done, for
example, in the widely used factor model. The idea is that although the fit of a latent variable model to the data may not prove the existence of causally operating latent variables, the model does formulate this as a hypothesis; consequently, the fit of such models can be adduced as evidence supporting this hypothesis. Finally, it is often suggested that the type of causal relation tested in latent
variable modeling is similar to the relation between Einstein’s
intelligence and behavior in the above example; that is, the latent
variable exerts influence at the level of the individual.
Given the intuitive appeal of explaining a wide range of behaviors
by invoking a limited number of latent variables, it is not surprising that latent variables analysis has become a popular technique in post behaviorist psychology. The conceptual framework
of latent variables analysis, however, is older than cognitive
psychology and originates with the work of Spearman (1904), who
developed factor analytic models for continuous variables in the
context of intelligence testing. The basic statistical idea of latent
variables analysis is simple. If a latent variable underlies a number
of observed variables, then conditionalizing on that latent variable
will render the observed variables statistically independent. This is
known as the principle of local independence. The problem of
latent variables analysis is to find a set of latent variables that
satisfies this condition for a given set of observed variables.