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Estimating Variances and Covariances
E
In the tree diagram in the upper left pane of the Amos Output window, expand
Estimates, Scalars, and then click Correlations.
Distribution Assumptions for Amos Models
Hypothesis testing procedures, confidence intervals, and claims for efficiency in
maximum likelihood or generalized least-squares estimation depend on certain
assumptions. First, observations must be independent. For example, the 40 young
people in the Attig study have to be picked independently from the population of young
people. Second, the observed variables must meet some distributional requirements. If
the observed variables have a multivariate normal distribution, that will suffice.
Multivariate normality of all observed variables is a standard distribution assumption
in many structural equation modeling and factor analysis applications.
There is another, more general, situation under which maximum likelihood
estimation can be carried out. If some exogenous variables are fixed (that is, they are
either known beforehand or measured without error), their distributions may have any
shape, provided that:
For any value pattern of the fixed variables, the remaining (random) variables have
a (conditional) normal distribution.
The (conditional) variance-covariance matrix of the random variables is the same
for every pattern of the fixed variables.