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Classical Assumptions

Classical assumptions for assessing internal consistency among indicators:

The indicators contain random errors these errors are not correlated with each other the errors not correlated with true scores any latent variables affect all items equally the amount of error for each item is equal The classical assumptions are relaxed in what's called "Tau-equivalent" tests (tau stands for T as in true score): The more liberal assumption is that the amount of error variance associated with a given item need not equal the error variance. The last condition above of "equal errors" for each item is relaxed. This means that item means and variances may also vary,

The classical assumptions for scale reliability in the internal consistency sense.

The greater the intercorrelations among the items, the more reliable the scale. The greater the number of items, the more reliable the scale.   For practical research applications, one seeks A scale of few items with high intercorrelations to produce reliable measurement. A metric that can express the degree of reliability.