Abstract and Keywords
This article shows that the words ‘behavioural’ and ‘behaviour’ turn out to be better measures as judged by tests of criterion and convergent validity. It specifically discusses measurement problems. Further, it pertains to statistical models that link latent variables and their observed indicators as measurement models. The success of measurement — the quality of the inferences provided by a measurement model — is usually assessed with reference to two key concepts: validity and reliability. The distinct uses of measures of latent variables are reported. The article then deals with the costs of ignoring measurement error. Additionally, a quick introduction to factor analysis, item-response models, and a very general class of latent variable models are briefly given. Moreover, it describes the inference for discrete latent variables and the measurement in a dynamic setting.
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