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date: 20 July 2018

Abstract and Keywords

Item response theory (IRT) is based on the premise that one or more unobservable (latent) variables are manifested in observable behaviors. These discrete observable behaviors are converted into continuous measurements through the application of an IRT model. We present and discuss our IRT models in terms of frames of reference, psychometric purpose, and type of response data. The models presented are applicable for affective, attitudinal, and proficiency data. The benefits and advantages of IRT models are given. We briefly discuss parameter estimation and provide a nonexhaustive list of estimation programs. The processes of model-data fit are presented as are transformation of our continuum’s metric.

Keywords: Item response theory, latent variable, logistic model, normal ogive model, psychometrics, maximum likelihood

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