The preceding discussion demonstrates the importance of having an explicit measurement model before analyzing measures. It is not valid to make any blanket statement on whether or not indicators should correlate until we know what type of indicators they are. If they are effect-indicators that have “well-behaved” errors and are positive measures of a single latent variable, then the internal-consistency view is appropriate and positive correlations of the indicators should occur. If cause-indicators are used then the NNR view is correct; indicator intercorrelations may be positive, negative, or zero. Finally, in general MIMIC models, cause-indicators have NNR while effect-indicators should be positively related under the assumptions of the model. In general, a cause- or an effect-indicator may have any type of relation.
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