Abstract
A multiple-step procedure is outlined that can be used for examining the latent structure of behavior measurement instruments in complex empirical settings. The method permits one to study their latent structure after assessing the need to account for clustering effects and the necessity of its examination within individual levels of fixed factors, such as gender or group membership of substantive relevance. The approach is readily applicable with binary or binary-scored items using popular and widely available software. The described procedure is illustrated with empirical data from a student behavior screening instrument.
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